Biology subjects

Breda, J.

Publications and source records attributed to Breda, J..

7 recordsLinked to original sources

bioRxiv2026

Transcription controls chromatin-nuclear lamina contacts through distinct Lamin A and LBR tethering mechanisms

Lamina-associated domains (LADs) are large genomic regions that interact with the nuclear lamina (NL). Much of the underlying "grammar" governing their positioning at the nuclear periphery remains unclear. LADs are composed of heterochromatin and typically harbor repressed genes, and their association with the NL is generally incompatible with strong transcriptional activity. The extent to which transcription globally shapes chromatin-NL interactions is not fully understood. Here, we combined acute transcription inhibition using Flavopiridol or Triptolide with genome-wide mapping of chromatin-NL contacts. We found that chromatin-NL interactions are rapidly rewired upon transcription inhibition. Changes in chromatin-NL contacts upon transcription shutdown are predictable based on transcriptional activity and the presence of H3K9me3-marked heterochromatin. This rewiring is reversible, as genome-NL interactions quickly return to baseline levels following drug wash-off. Notably, gain and loss of chromatin-NL interactions upon transcription shutdown reflect two distinct tethering mechanisms. Inter-LADs genomic regions (iLADs) enriched in highly active genes and located near stable LADs, which are tethered by Lamin A (LMNA/C), become re-attached to the NL following transcription inhibition. In parallel, H3K9-methylated regions tethered to the nuclear envelope by the Lamin B receptor (LBR) undergo extensive detachment from the NL. Strikingly, LMNA/C and LBR oppositely regulate transcription-sensitive LADs and are required for transcriptional control of chromatin-NL contacts. Together, our findings highlight the plasticity and dynamic nature of chromatin-NL interactions and provide the first evidence that LMNA/C- and LBR-mediated tethering mechanisms exhibit distinct sensitivities to transcription inhibition. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/738400v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@16781a2org.highwire.dtl.DTLVardef@6febd1org.highwire.dtl.DTLVardef@1e3220forg.highwire.dtl.DTLVardef@d137f7_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LITranscription inhibition alters chromatin-NL contacts rapidly and reversibly C_LIO_LIActive transcription prevents inter-LADs located near LMNA/C-tethered LADs from associating with the nuclear lamina. C_LIO_LILBR-tethered heterochromatin is repositioned away from the NL C_LIO_LITranscription-dependent modulation of chromatin-NL contacts is dependent on LMNA/C and partially on LBR C_LI

genomics↗

bioRxiv2024

Coordinated control of genome-nuclear lamina interactions by Topoisomerase 2B and Lamin B receptor

Lamina-associated domains (LADs) are megabase-sized genomic regions anchored to the nuclear lamina (NL). Factors controlling the interactions of the genome with the NL have largely remained elusive. Here, we identified DNA topoisomerase 2 beta (TOP2B) as a regulator of these interactions. TOP2B binds predominantly to inter-LAD (iLAD) chromatin and its depletion results in a partial loss of genomic partitioning between LADs and iLADs, suggesting that its activity might protect specific iLADs from interacting with the NL. TOP2B depletion affects LAD interactions with lamin B receptor (LBR) more than with lamins. LBR depletion phenocopies the effects of TOP2B depletion, despite the different positioning of the two proteins in the genome. This suggests a complementary mechanism for organising the genome at the NL. Indeed, co-depletion of TOP2B and LBR causes partial LAD/iLAD inversion, reflecting changes typical of oncogene-induced senescence. We propose that a coordinated axis controlled by TOP2B in iLADs and LBR in LADs maintains the partitioning of the genome between the NL and the nuclear interior. HighlightsO_LILADs and iLADs differ in supercoiling state C_LIO_LITOP2B controls genome partitioning between nuclear lamina and nuclear interior C_LIO_LITOP2B depletion preferentially affects genome interactions with LBR C_LIO_LISimilar impact of TOP2B depletion and LBR depletion on genome-NL interactions C_LIO_LICo-depletion of TOP2B and LBR recapitulates LAD reshaping typical of oncogene-induced senescence. C_LI

genomics↗

bioRxiv2024

The regulatory grammar of human promoters uncovered by MPRA-trained deep learning

One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. At the heart of gene regulation are promoters, yet their regulatory logic is still incompletely understood. Here, we report PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences. PARM requires [~]1,000 times less computational power than state-of-the-art technology, and reliably predicts autonomous promoter activity throughout the genome from DNA sequence alone, in multiple cell types. PARM can even design purely synthetic strong promoters. We leveraged PARM to systematically identify binding sites of transcription factors (TFs) that are likely to contribute to the activity of each natural human promoter. We uncovered and experimentally confirmed striking positional preferences of TFs that differ between activating and repressive regulatory functions, as well as a complex grammar of motif-motif interactions. For example, many, but not all, TFs act as repressors when their binding motif is located near or just downstream of the transcription start site. Our approach lays the foundation towards a deep understanding of the regulation of human promoters by TFs. HighlightsO_LICausality-trained deep learning model PARM captures regulatory grammar of human promoters C_LIO_LIPARM is highly economical, both experimentally and computationally C_LIO_LITranscription factors have different preferred positions for their regulatory activity C_LIO_LIMany (but not all) transcription factors act as repressors when binding downstream of transcription start sites C_LI

genetics↗

bioRxiv2022

The protooncogene Ski regulates the neuron-glia switch during development of the mammalian cerebral cortex

The brain is the most complex organ in mammals and understanding the origin of this complexity is a major challenge for developmental biologists. Crucial to the size and morphology of the cortex is the timing and transition of neural stem cell (NSC) fate. An interesting candidate for modulating and fine tuning these processes is the transcriptional regulator Ski, a protooncogene expressed in cortical cells. Ski is involved in diverse cellular processes and epigenetic programs, and mice deficient in Ski exhibit complex central nervous system defects that resemble some of the features observed in patients with 1p36 deletion syndrome and Shprintzen-Goldberg syndrome. Here, we took advantage of in vivo transgenic labeling and next-generation sequencing to analyze the gene expression profiles of NSCs, basal progenitor (BP) cells, and newborn neurons (NBNs) from wildtype and Ski-deficient embryos throughout cortical development. We created a unique database that allowed us to identify and compare signaling pathways and transcriptional networks within each progenitor population in the presence and absence of Ski. We find that NSCs are the most affected cell population and uncover that mutant NSCs fail to switch to a gliogenic fate in time. We show that Ski functions in concert with the Bone Morphogenetic Protein (BMP) signaling pathway to alter the cell differentiation fate of NSCs from neurons to glia, which is key to generating adequate numbers of specific cell types during corticogenesis. Thus, by combining genetic tools and bioinformatic analysis, our work not only deepens the knowledge of how Ski functions in the brain, but also provides an immense resource for studying neurodevelopmental disorders.

systems biology↗

bioRxiv2022

A novel approach to single cell analysis to reveal intrinsic differences in immune marker expression in unstimulated macrophages from BALB/c and C57BL/6 mouse strains

Macrophages are cells of the innate immune system that provide the first line of defense against pathogens. Their functional and morphological heterogeneity is well known, though the origin of this heterogeneity is still debated. Furthermore, while mouse strains differ in the type of immune responses that they mount to individual pathogens, the range of gene expression variation among their macrophages in the absence of a specific stimulus is not known. By applying single cell RNA sequencing we here reveal the gene expression variation in pre-stimulation macrophage populations from specific pathogen-free BALB/c and C57BL/6 mice, two mouse strains that give prototypical Th2 and Th1-biased immune responses, directed towards extracellular or intracellular pathogens, respectively. We show that intrinsic differences between the macrophages of these two strains are detectable before any specific stimulation and we place the gene expression profile of these cells within the range of variation that is measured upon in vitro stimulation with pro-inflammatory lipopolysaccharide (LPS) and interferon {gamma} (IFN), or anti-inflammatory IL-4. We find that C57BL/6 mice show stronger evidence of macrophage polarization than BALB/c mice, which could explain their resistance to pathogens such as Leishmania. Our computational methods for analyzing single cell RNA sequencing data, controlling for common sources of stochastic variation, can be more generally adopted to uncover biological variation between cell populations.

systems biology↗

bioRxiv2022

Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs) and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time, and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP and NBN clusters, and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

neuroscience↗

bioRxiv2019

Bayesian inference of the gene expression states of single cells from scRNA-seq data

In spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters. Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

bioinformatics↗