Biology subjects

Sargeant, T.

Publications and source records attributed to Sargeant, T..

6 recordsLinked to original sources

bioRxiv2025

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities - including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chro- matin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenomes ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

bioinformatics↗

bioRxiv2024

A multi-track landscape of haematopoiesis informed by cellular barcoding and agent-based modelling

In classic ball-and-stick models of haematopoiesis the implicit assumption is that all cells within each defined stem or progenitor cell population are equivalent in their fate. Instead, more recent models suggest a haematopoietic stem and progenitor cell (HSPC) continuum of lineage bias and commitment, which is largely inferred through snapshot analysis of single cell gene expression or clonal fate. However, the dynamic assessment of lineage commitment of specific HSPC populations and their clonal output over time in vivo is still lacking but is essential to fully inform accurate models of haematopoiesis. Here, using cellular barcoding we compare the single cell output of long-term haematopoietic stem cells (LT-HSCs), short-term HSCs (ST-HSCs), multipotent progenitors (LMPPs), common myeloid progenitors (CMPs), common lymphoid progenitors (CLPs), and macrophage/dendritic cell progenitors (MDPs). Each population was assessed for their output to multiple haematopoietic cell types spanning a subset of time points from 9 to 112 days of haematopoiesis after transplantation. These analyses revealed a wide range of clonal fate patterns that were inconsistent with their eponymous labels, i.e. stem and multipotent progenitors were rarely multi- or equipotent, and common progenitors were often highly restricted in their fate. To better describe how these clonal patterns integrate into a revised landscape, a novel agent-based mathematical modelling approach that explicitly accounts for haematopoiesis at a clonal level was developed to allow the simulation of growth, timing and branching of clonal trajectories that underlie the process. Rather than a continuum, the proposed model is suggestive of multiple tracks down which clonal trajectories progress, and where fate can branch to a track of lower potency at multiple points down the entire cascade of haematopoiesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/587126v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b09137org.highwire.dtl.DTLVardef@de064org.highwire.dtl.DTLVardef@10705e3org.highwire.dtl.DTLVardef@139b6f_HPS_FORMAT_FIGEXP M_FIG C_FIG In a multi-track model, while some HSPCs are multipotent and branch into daughters with committed fate (left trajectory), other HSPCs may already committed or biased to a certain lineage such that their daughters inherit and maintain it in subsequent divisions during haematopoiesis. However, this commitment/fate bias is not evident through current phenotypic definitions of HSPC subsets (background colour) but occurs through putative expression of transcription factors, epigenetic programming or other factor that is currently unresolved (as indicated by the coloured fate potential triangles inside cells).

immunology↗

bioRxiv2022

Transcriptional targets of senataxin and E2 promoter binding factors are associated with neuro-degenerative pathways during increased autophagic flux

Autophagy is an intracellular recycling process that degrades harmful molecules, maintains optimal composition of cellular organelles and enables survival during starvation. Previous studies have demonstrated how transcription factors (TFs) can increase autophagy with therapeutic potential (impaired autophagy in the ageing brain, in particular, may be an important risk factor for dementia). To investigate the transcriptional regulation of autophagy from a systems perspective, we induced autophagy by amino acid starvation and mTOR inhibition in HeLa, HEK 293 and SH-SY5Y cells and used RNA-seq to measure gene expression at three time points. We observed 453 differentially expressed (DE) genes due to starvation and 284 genes due to mTOR inhibition (PFDR < 0.05 in every cell line). Pathway analyses confirmed enrichment of genes implicated in Alzheimers (PFDR < 0.001 in SH-SY5Y and HeLa) and Parkinsons (PFDR [≤] 0.024 in SH-SY5Y and HeLa) diseases and amyotrophic lateral sclerosis (ALS, PFDR < 0.05 in 4 of 6 experiments). We then integrated Signaling Pathway Impact Analysis and TF target enrichment testing to predict which TF target genes were contributing to pathway perturbation. Differential expression of the Senataxin (SETX) target gene set was predicted to activate multiple neurodegenerative pathways (PFDR [≤] 0.04). Notably, SETX is a causal gene for a rare form of ALS. In the SH-SY5Y cells of neuronal origin, the E2F transcription family was predicted to activate Alzheimers disease pathway (PFDR [≤] 0.0065). SETX and E2F may be important mediators of transcriptional regulation of autophagy and may provide new therapeutic opportunities for neuro-degenerative conditions.

genomics↗

bioRxiv2018

SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

Conventional single cell RNA-seq methods are destructive, such that a given cell cannot also then be tested for fate and function, without a time machine. Here, we develop a clonal method SIS-seq, whereby single cells are allowed to divide, and progeny cells are assayed separately in SISter conditions; some for fate, others by RNA-seq. By cross-correlating progenitor gene expression with mature cell fate within a clone, and doing this for many clones, we can identify the earliest gene expression signatures of dendritic cell subset development. SIS-seq could be used to study other populations harboring clonal heterogeneity, including stem, reprogrammed and cancer cells to reveal the transcriptional origins of fate decisions.

systems biology↗

bioRxiv2018

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples. SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq.

bioinformatics↗

bioRxiv2017

Dynamic changes in clonal architecture during disease progression in follicular lymphoma

Follicular lymphoma (FL) is typically a slow growing cancer that can be effectively treated. Some patients undergo transformation to diffuse large B cell lymphoma (DLBCL), which is frequently resistant to chemotherapy and is generally fatal. Targeted sequencing of DNA and RNA was applied to identify mutations and transcriptional changes that accompanied transformation in a cohort of 16 patients, including 14 with paired samples. In most cases we found mutations that were specific to the FL clone dominant at diagnosis, supporting the view that DLBCL does not develop directly from FL, but from an ancestral progenitor. We identified frequent mutations in TP53, cell cycle regulators (cyclins and cyclin-dependent kinases) and the PI3K pathway, as well as recurrent somatic copy number variants (SCNVs) on chromosome 3, 7 and 17p associated with transformation. An integrated analysis of RNA and DNA identified allele specific expression changes in oncogenes, including MYC, that could be attributed to structural rearrangements. By focusing on serial samples taken from two patients, we identified evidence of convergent tumour evolution, where clonal expansion was repeatedly associated with mutations targeting the same genes or pathways. Analysis of serial samples is a powerful way to identify core dependencies that support lymphoma growth.

cancer biology↗