Biology subjects

Smyth, G. K.

Publications and source records attributed to Smyth, G. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Termination dynamics set RNAPII elongation rate and gate the response to CDK12 inactivation

The transcriptional fidelity of RNA polymerase II (RNAPII) is governed by a tight equilibrium between elongation and termination activities, a balance frequently disrupted in human diseases such as cancer. The transcriptional cyclin-dependent kinase 12 (CDK12) maintains RNAPII elongation rate and processivity throughout the gene body. Inactivation of CDK12 disrupts this homeostatic balance and causes elongation stress, slowing RNAPII and triggering premature termination at intronic polyadenylation sites (IPAs). Despite this, the precise executors of intronic premature termination under CDK12-inactivation-induced elongation stress remain poorly understood. Using genome-wide CRISPR screening combined with chemical-genetic approach, we identified a pro-termination mechanism at intronic checkpoints; upon CDK12 inactivation, SCAF4 recruits the cleavage and polyadenylation (CPA) complex through its catalytic endonuclease CPSF3 to execute premature cleavage at IPAs. Disruption of SCAF4-CPA axis prevents early termination, restores full length transcription and confers resistance to CDK12/cyclin K inhibition. Genetic loss of SCAF4 restores the RNAPII elongation rate under CDK12 inhibition, revealing that termination dynamics actively shape the rate of transcription. Supporting this model, we uncovered an anti-termination mechanism driven by KHDRBS1/SAM68, whose depletion promotes proximal termination and sensitises cells to CDK12 targeting. Our findings mechanistically couple elongation and termination activities at intronic checkpoints as joint contributors to both the processivity and elongation rate of RNAPII. This establishes termination dynamics as an active and tractable axis of the cellular response to elongation stress.

molecular biology↗

bioRxiv2026

3D genome remodelling underlies PU.1-dependent granulocyte maturation

PU.1 is an ETS-domain transcription factor that has critical roles in many aspects of hematopoiesis and immune cell fate and function. In addition, aberrant PU.1 expression has been implicated in the development of acute myeloid leukemia (AML). Loss of PU.1 during adult murine hematopoiesis results in the expansion of immature granulocytes suggesting that PU.1 plays an important role in granulocyte maturation. To understand the molecular underpinnings of this process, we combined gene expression, transcription factor binding and 3D genome analyses with conditional deletion of PU.1 in vivo. We find that in contrast to normal granulocytes, PU.1-deficient cells possessed a transcriptome of immature granulocytes, in line with their cellular phenotype. Furthermore PU.1-deficient granulocytes display altered 3D genome architecture with a significant loss of interactivity in regions bound by PU.1 in control cells. Overall, this study implicates PU.1 as a key regulator of granulocyte maturation and lineage commitment through control of transcriptional programs and 3D chromatin architecture.

immunology↗

bioRxiv2026

Pre-existing levels of pro-survival proteins and induction of BCL-XL dictate cell fate after p53 activation

TP53 (also called TRP53 or p53) is a critical tumour suppressor that prevents cancer development by inducing a transcriptional program which can lead to diverse cellular responses, most prominently, cell proliferation arrest/senescence with survival of cells or cell death by apoptosis. Why distinct cell types undergo different outcomes after p53 activation remains unclear. Using integrated RNA-sequencing, proteomic and functional analyses across a diverse range of murine primary cell types, we demonstrate that cell fate is governed by the balance between pro-survival BCL-2 and pro-apoptotic BH3-only proteins. Cells resistant to apoptosis displays a higher starting ratio of pro-survival BCL-2 to pro-apoptotic BH3-only proteins, along with transcriptional upregulation of the pro-survival gene Bcl2l1, encoding BCL-XL. This control of cell fate is also seen in human wild-type p53 cancer cell lines. These findings reveal the mechanism for understanding p53-driven cell fate decisions, suggest therapeutic strategies to shift p53-induced cell proliferation arrest/senescence toward apoptotic cell death and allowed generation of an RNAseq data-based predictor of outcome for cancer cells after p53 activation.

cancer biology↗

bioRxiv2026

jazzPanda: A hybrid approach to find spatial markergenes in imaging-based spatial transcriptomics data

MotivationSpatial transcriptomics resolves the organisation of tissues and their cellular neigh-bourhoods, where cell type identification depends on reliable marker gene detection. Existing marker methods were developed for single-cell RNA sequencing and ignore the spatial coordinates of cells and transcripts, a particular problem for imaging-based platforms where transcript counts per gene per cell are extremely sparse. ResultsWe present jazzPanda, a method for detecting spatially informative marker genes in imaging-based spatial transcriptomics. Transcript and cell coordinates are aggregated into one-dimensional vectors by spatial binning, and gene vectors can be built directly from transcript coordinates without cell segmentation. Markers are identified either by permutation-based rank correlation for single-sample data, or by a lasso-regularised generalised linear model that accommo-dates multiple samples and platform-specific background signal from negative-control probes. To our knowledge, jazzPanda is the only marker detection method to account jointly for spatial distri-bution, replication across samples, and platform background. Benchmarked against the Wilcoxon rank sum test and t-tests on public Xenium and CosMx data, jazzPanda recovers markers with stronger spatial concordance and greater specificity, yielding smaller, more interpretable marker sets. The same vector framework also extends to cluster- and gene-level co-location analysis. Availability and implementationjazzPanda is implemented as an open-source R/Bioconductor package, freely available at https://bioconductor.org/packages/jazzPanda. Analysis code for this article is at https://github.com/phipsonlab/jazzPanda_paper, with an accompanying analysis website at https://phipsonlab.github.io/jazzPanda_workflowr/. Datasets and scripts are deposited at https://zenodo.org/records/18149456. Contactphipson.b@wehi.edu.au

bioinformatics↗

bioRxiv2026

INTS12 Bridges Integrator and NELF to Prevent the Release of Non-processive RNA Polymerase II Complexes

Promoter-proximal RNA Polymerase II (RNAPII) pausing and the processivity are controlled by distinct modules of the Integrator complex, which together fine-tune transcription and protect against the accumulation of defective RNAPII complexes. Compromised activity of individual Integrator modules has been linked to human disease including cancer and developmental disorders, caused by defective transcription of protein-coding or small-nuclear RNAs. Despite extensive characterisation of the Integrator complex both genetically and structurally, the role of smallest member of the complex, INTS12, has remained enigmatic. Here, we uncover that INTS12 loss acts to stabilise the association between NELF and Integrator via its PHD domain and N-terminus, respectively, thus safeguarding against the release of defective RNAPII complexes. Acute degradation of INTS12 results in the selective dissociation of Integrator from the NELF-RNAPII complex which subsequently convert to their canonical paused form from which they can be released by CDK9. In the absence of INTS12 excess release of defective RNAPII via P-TEFb/SEC, loss of the ARMC5 salvage pathway and deletion of the catalytic and core Integrator subunits is toxic to cells. These findings demonstrate that there is interconversion between canonical paused RNAPII and paused-Integrator, and highlight the critical interplay between these processes and P-TEFb mediated pause-release to ensure that only transcription competent complexes are released into elongation. O_LIINTS12 degradation confers CDK9 inhibitor resistance and triggers cellular stress through a phosphatase module-independent mechanism. C_LIO_LIINTS12 stabilizes the Integrator-NELF complex through its N-terminus and PHD domain. C_LIO_LIAcute INTS12 degradation promotes aberrant release of promoter-proximal RNA polymerase II complexes. C_LIO_LIRNA polymerase II complexes released upon INTS12 loss exhibit defective elongation and reduced processivity. C_LIO_LIINTS12 loss removes Integrator from RNAPII resulting in aberrant paused-state from which it can be released by CDK9. C_LIO_LIExcess CDK9 activity and ARMC5 loss are synthetically lethal with INTS12 deficiency. C_LI

molecular biology↗

bioRxiv2025

Quantification and differential analysis of mass spectrometry proteomics data with probabilistic recovery of information from missing values

Mass spectrometry (MS) is the technology standard for expression proteomics, but statistical analysis of the resulting data is complicated by the occurrence of missing values. Missing values remain ubiquitous in MS-based proteomics data and are especially frequent in the emerging fields of single-cell and spatial proteomics. The limpa package implements new methods for quantification and differential expression analysis of MS proteomics data, including probabilistic information recovery from missing values. limpa summarises peptide-level data to estimate an expression value for every protein in every sample. Expression values that are supported by fewer detected peptides or involve more missing values are treated as less precise and are downweighted in the differential expression analysis, maximising statistical power while avoiding false discoveries. limpa produces a linear model object suitable for downstream analysis with the limma package, allowing complex experimental designs and other downstream tasks such as the gene ontology or pathway analysis.

bioinformatics↗

bioRxiv2025

Dividing out quantification uncertainty enables assessment of differential transcript usage with diffSplice

Differential transcript usage (DTU) refers to changes in the relative abundance of transcript isoforms of the same gene between experimental conditions, even when the total expression of the gene doesnt change. DTU analysis requires the quantification of individual isoforms from RNA-seq data, which has a high level of uncertainty due to transcript overlap and read-to-transcript ambiguity (RTA). Popular DTU analysis methods do not directly account for the RTA overdispersion within their statistical frameworks, leading to reduced statistical power or poor error rate control, particularly in scenarios with small sample sizes. This article presents limma and edgeR analysis pipelines that account for RTA during DTU assessment. Leveraging recent advancements in the limma and edgeR Bioconductor packages, we propose DTU analysis pipelines optimized for small and large datasets with a unified interface via the diffSplice function. The pipelines make use of divided counts to remove RTA-induced dispersion from transcript isoform counts and account for the sparsity in transcript-level counts. Simulations and analysis of real data from mouse mammary epithelial cells demonstrate that the diffSplice pipelines provide greater power, improved efficiency, and improved FDR control compared to existing specialized DTU methods.

bioinformatics↗

bioRxiv2024

Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR 4.0

Differential transcript expression analysis of RNA-seq data is an increasingly popular tool to assess changes in expression of individual transcripts between biological conditions. Software designed for transcript-level differential expression analyses account for the uncertainty of transcript quantification, the read-to-transcript ambiguity (RTA), in statistical analyses via resampling methods. Bootstrap sampling is a popular resampling method that is implemented in the RNA-seq quantification tools kallisto and Salmon. However, bootstrapping is computationally intensive and provides replicate counts with low resolution when the number of sequence reads originating from a gene is low. For lowly expressed genes, bootstrap sampling results in noisy replicate counts for the associated transcripts, which in turn leads to non reproducible and unrealistically high RTA-dispersion for those transcripts. Gibbs sampling is a more efficient and high resolution algorithm implemented in Salmon. Here we leverage the developments of edgeR v4 to present an improved differential transcript expression analysis pipeline with Salmons Gibbs sampling algorithm. The new bias-corrected quasi-likelihood method with adjusted deviances for small counts from edgeR, combined with the efficient Gibbs sampling algorithm from Salmon, provides faster and more accurate DTE analyses of RNA-seq data. Comprehensive simulations and test data show that the presented analysis pipeline is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate.

bioinformatics↗

bioRxiv2024

MORC2 phosphorylation fine tunes its DNA compaction activity

Variants in the poorly characterised oncoprotein, MORC2, a chromatin remodelling ATPase, lead to defects in epigenetic regulation and DNA damage response. The C-terminal domain (CTD) of MORC2, frequently phosphorylated in DNA damage, promotes cancer progression, but its role in chromatin remodelling remains unclear. Here, we report a molecular characterisation of full-length, phosphorylated MORC2, demonstrating its preference for binding open chromatin and functioning as a DNA sliding clamp. We identified a phosphate interacting motif within the CTD that dictates ATP hydrolysis rate and cooperative DNA binding. The DNA binding impacts several structural domains within the ATPase region. We provide the first visual proof that MORC2 induces chromatin remodelling through ATP hydrolysis-dependent DNA compaction, regulated by its phosphorylation state. These findings highlight phosphorylation of MORC2 CTD as a key modulator of chromatin remodelling, presenting it as a potential therapeutic target.

molecular biology↗

bioRxiv2024

edgeR 4.0: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyse complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C, and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously.

bioinformatics↗

bioRxiv2023

Unraveling the timeline of gene expression: a pseudo-temporal trajectory analysis of single-cell RNA sequencing data

There has been a rapid development in single cell RNA sequencing (scRNA-seq) technologies in recent years. Droplet-based single cell platforms such as the 10x Genomics Chromium system enable gene expression profiling of tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Trajectory analysis can also be used to infer the developmental or differentiation trajectories of cells by ordering them along a putative lineage tree based on their gene expression profiles. This analysis positions cells and cell clusters along a pseudotime trajectory that represents a biological process such as cell differentiation, development, or disease progression. Here we demonstrate a time-course analysis to identify genes that are significantly associated with pseudotime. The article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudobulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR.

bioinformatics↗

bioRxiv2023

The T2T-CHM13 reference genome has more accurate sequences for immunoglobulin genes than GRCh38

Antibody production by B-cells is essential for protective immunity. The clonal selection theory posits that each mature B cell has a unique immunoglobulin receptor generated through random gene recombination and, when stimulated to differentiate into an antibody-secreting cell, has the capacity to produce only a single antibody specificity. It follows from this "one-cell-one-antibody" dogma, that single-cell RNA-seq profiling of antibody-secreting cells should find that each cell expresses only a single form of each of the immunoglobulin heavy and light chains. However, when using GRCh38 as the genome reference, we found that many antibody-secreting cells appeared to express multiple immunoglobulin isotypes. When the newly published T2T-CHM13 genome was used instead as the genome reference, every antibody-secreting cell was found to express a unique isotype, and read mapping quality was also improved. We show that the superior performance of T2T-CHM13 was due to its European origin matching the genetic background of the query samples. On the other hand, T2T-CHM13 failed to appropriately fit the "one-cell-one-antibody" dogma when applied to data derived from East Asia. Our results show that read assignment to human immunoglobulin isotype genes is very sensitive to the ancestral origin of the genome reference.

genomics↗

bioRxiv2023

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

bioinformatics↗

bioRxiv2022

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures

The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs ("sequins"). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that, StringTie2 and bambu outperformed other tools from the 6 isoform detection tools tested, DESeq2, edgeR and limma-voom were best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.

bioinformatics↗

bioRxiv2022

Missing values are informative in label-free shotgun proteomics data: estimating the detection probability curve

Mass spectrometry proteomics is a powerful tool in biomedical research but its usefulness is limited by the frequent occurrence of missing values in peptides that cannot be reliably quantified for particular samples. Many analysis strategies have been proposed for missing values where the discussion often focuses on distinguishing whether values are missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). We argue here that missing values should always be viewed as MNAR in label-free proteomics because physical missing value mechanisms cannot be identified for individual points and because the probability of detection is related to underlying intensity. We show that the probability of detection can be accurately modeled by a logit linear curve. The curve asymptotes close to 100%, limiting the potential role of missing values unrelated to intensity. The curve is also incompatible with simple censoring mechanisms. We propose a statistical method for estimating the detection probability curve as a function of the underlying intensity, whether observed or not. The model quantifies the bias of missing intensities as compared to those that are observed. The model demonstrates that missing values are informative and suggests possible approaches to imputation and differential expression.

bioinformatics↗

bioRxiv2021

R code and downstream analysis objects for the scRNA-seq atlas of human breast spanning normal, preneoplastic and tumorigenic states

Breast cancer is a common and highly heterogeneous disease. Understanding the cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into the cancer development in human breast. Recently, a large-scale single-cell RNA expression atlas was constructed of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Details of all the bioinformatic analyses that produced the results described in the study are provided.

bioinformatics↗

bioRxiv2020

Chromosomes distribute randomly to, but not within, human neutrophil nuclear lobes

The proximity pattern and radial distribution of chromosome territories within spherical nuclei are well understood to be random and non-random, respectively. Whether this distribution pattern is conserved in the partitioned or lobed nuclei of polymorphonuclear cells is unclear. Here we use chromosome paint technology and a novel high-throughput imaging analysis pipeline to examine the chromosome territories of all 46 chromosomes in hundreds of single human neutrophils - an abundant and famously polymorphonuclear immune cell. By comparing the distribution of chromosomes to randomly shuffled controls, and validating with orthogonal chromosome conformation capture technology, we show for the first time that all human chromosomes randomly distribute to neutrophil nuclear lobes, while maintaining a non-random radial distribution within these lobes. Furthermore, by leveraging the power of this vast dataset, we are able to reveal characteristics of chromosome territories not detected previously. For example, we demonstrate that chromosome length correlates with three-dimensional volume not only in neutrophils but other human immune cells. This work demonstrates that chromosomes are largely passive passengers during the neutrophil lobing process, but are able to maintain their macro-level organisation within lobes. Furthermore, the random distribution of chromosomes to the naturally partitioned nuclear lobes suggests that specific transchromosomal interactions are unimportant in mature neutrophils.

immunology↗

bioRxiv2020

Suv39h-catalysed H3K9me3 is critical for euchromatic genome organisation and the maintenance of gene transcription

H3K9me3-dependent heterochromatin is critical for the silencing of repeat-rich pericentromeric regions and also has key roles in repressing lineage-inappropriate protein-coding genes in differentiation and development. Here, we investigate the molecular consequences of heterochromatin loss in cells deficient in both Suv39h1 and Suv39h2 (Suv39DKO), the major mammalian histone methyltransferase enzymes that catalyse heterochromatic H3K9me3 deposition. Unexpectedly, we reveal a predominant repression of protein-coding genes in Suv39DKO cells, with these differentially expressed genes principally in euchromatic (DNaseI-accessible, H3K27ac-marked) rather than heterochromatic (H3K9me3-marked) regions. Examination of the 3D nucleome reveals that transcriptomic dysregulation occurs in euchromatic regions close to the nuclear periphery in 3-dimensional space. Moreover, this transcriptomic dysregulation is highly correlated with altered 3-dimensional genome organization in Suv39DKO cells. Together, our results suggest that the nuclear lamina-tethering of Suv39-dependent H3K9me3 domains provides an essential scaffold to support euchromatic genome organisation and the maintenance of gene transcription for healthy cellular function.

genomics↗