Biology subjects

Mackiewicz, M.

Publications and source records attributed to Mackiewicz, M..

7 recordsLinked to original sources

bioRxiv2024

Comparative atlas of genome-wide chromatin-associated protein co-occupancy

Accurate transcriptional regulation and chromatin dynamics requires the coordination and activity of chromatin-associated proteins (CAPs) at distinct loci. While the combinatorial activity of a select set of CAPs has been previously examined, these studies are limited by the underrepresentation of proteins and cell types explored, making it difficult to identify the global associations as well as the conservation of these associations across different cell types. Here, we performed 270 CAP chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) experiments in both K562 and HepG2 cancer cell lines and explored the relationship between cell identity and CAP co-association using three distinct approaches. We employed a machine learning algorithm to organize the genome-wide binding profiles into 56 and 70 interpretable co-association modules for HepG2 and K562 cell lines, respectively. We found CAP co-association modules are mostly cell type-specific, however those present in both cell lines are largely comprised of TFs from a single TF family and anchor to unique loci via lineage-specific factors. While enhancer-associated co-binding modules were largely composed of cell type-specific CAPs, we found regulatory activity at promoter-enhancer module contacts to be enriched for chromatin remodeling proteins. Additionally, we used colocalization information derived from co-association models in conjunction with neural network models of transcription factor (TF) activity to identify high-confidence candidate TF cooperative pairs. Finally, through comparing CAP enrichment in high occupancy target (HOT) regions in K562 and HepG2 cell lines, we found cell type-specific HOT sites, but not common HOT sites, are selectively enriched at high copy number loci. Overall, this study uncovers principles of sequence-level and large-scale CAP genomic organization and demonstrates how this contributes to cell type-specific regulatory mechanisms and cellular functions.

genomics↗

bioRxiv2023

MAPT expression is regulated by long-range interactions with cis-regulatory elements

BackgroundTauopathies are a group of neurodegenerative diseases driven by abnormal aggregates of tau, a microtubule associated protein encoded by the MAPT gene. MAPT expression is absent in neural progenitor cells (NPCs) and increases during differentiation. This temporally dynamic expression pattern suggests that MAPT expression is controlled by transcription factors and cis-regulatory elements specific to differentiated cell types. Given the relevance of MAPT expression to neurodegeneration pathogenesis, identification of such elements is relevant to understanding genetic risk factors. MethodsWe performed HiC, chromatin conformation capture (Capture-C), single-nucleus multiomics (RNA-seq+ATAC-seq), bulk ATAC-seq, and ChIP-seq for H3K27Ac and CTCF in NPCs and neurons differentiated from human iPSC cultures. We nominated candidate cis-regulatory elements (cCREs) for MAPT in human NPCs, differentiated neurons, and pure cultures of inhibitory and excitatory neurons. We then assayed these cCREs using luciferase assays and CRISPR interference (CRISPRi) experiments to measure their effects on MAPT expression. Finally, we integrated cCRE annotations into an analysis of genetic variation in AD cases and controls. ResultsUsing orthogonal genomics approaches, we nominated 94 cCREs for MAPT, including the identification of cCREs specifically active in differentiated neurons. Eleven regions enhanced reporter gene transcription in luciferase assays. Using CRISPRi, 5 of the 94 regions tested were identified as necessary for MAPT expression as measured by RT-qPCR and RNA-seq. Rare and predicted damaging genetic variation in both nominated and confirmed CREs was depleted in AD cases relative to controls (OR = 0.40, p = 0.004), consistent with the hypothesis that variants that disrupt MAPT enhancer activity, and thereby reduce MAPT expression, may be protective against neurodegenerative disease. ConclusionsWe identified both proximal and distal regulatory elements for MAPT and confirmed the regulatory function for several regions, including three regions centromeric to MAPT beyond the well-described H1/H2 haplotype inversion breakpoint. This study provides compelling evidence for pursuing detailed knowledge of CREs for genes of interest to permit better understanding of disease risk.

genomics↗

bioRxiv2021

Multi-tissue integrative analysis of personal epigenomes

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗

bioRxiv2018

Dynamics of microRNA expression during mouse prenatal development

MicroRNAs (miRNAs) play a critical role as post-transcriptional regulators of gene expression. The ENCODE project profiled the expression of miRNAs in a comprehensive set of tissues during a time-course of mouse embryonic development and captured the expression dynamics of 785 miRNAs. We found distinct tissue and developmental stage specific miRNA expression clusters, with an overall pattern of increasing tissue specific expression as development proceeds. Comparative analysis of conserved miRNAs in mouse and human revealed stronger clustering of expression patterns by tissue types rather than by species. An analysis of messenger RNA gene expression clusters compared with miRNA expression clusters identifies the potential role of specific miRNA expression clusters in suppressing the expression of mRNAs specific to other developmental programs in the tissue where these microRNAs are expressed during embryonic development. Our results provide the most comprehensive timecourse of miRNA expression as an integrated part of the ENCODE reference dataset for mouse embryonic development.

genomics↗

bioRxiv2018

Occupancy patterns of 208 DNA-associated proteins in a single human cell type

Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes, but only a small fraction of the >1,600 transcription factors (TFs) encoded in the human genome has been assayed. Here we present data and analyses of ChIP-seq experiments for 208 DNA-associated proteins (DAPs) in the HepG2 hepatocellular carcinoma line, spanning nearly a quarter of its expressed TFs, transcriptional co-factors, and chromatin regulator proteins. The DAP binding profiles classify into major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalog of DNA sequence motifs; 77 factors showed similar motifs to those previously described using in vivo and/or in vitro methods, and 17 yielded novel motifs. We also describe motifs corresponding to other TFs that co-enrich with the primary ChIP target. FOX family motifs are, for example, significantly enriched in ChIP-seq peaks of 37 other DAPs. We show that promoters and enhancers can be discriminated based on motif content and occupancy patterns. This large catalog reveals High Occupancy Target (HOT) regions at which many DAPs associate, although each contains motifs for only a minority of the numerous associated DAPs. These analyses provide a deeper and more complete overview of the gene regulatory networks that define this cell type.

genomics↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗

bioRxiv2017

A toolbox of immunoprecipitation-grade monoclonal antibodies against human transcription factors.

A key component to overcoming the reproducibility crisis in biomedical research is the development of readily available, rigorously validated and renewable protein affinity reagents. As part of the NIH Protein Capture Reagents Program (PCRP), we have generated a collection of 1406 highly validated, immunoprecipitation (IP) and/or immunoblotting (IB) grade, mouse monoclonal antibodies (mAbs) to 736 human transcription factors. We used HuProt human protein microarrays to identify mAbs that recognize their cognate targets with exceptional specificity. Using an integrated production and validation pipeline, we validated these mAbs in multiple experimental applications, and have distributed them to the Developmental Studies Hybridoma Bank (DSHB) and several commercial suppliers. This study allowed us to perform a meta-analysis that identified critical variables that contribute to the generation of high quality mAbs. We find that using full-length antigens for immunization, in combination with HuProt analysis, provides the highest overall success rates. The efficiencies built into this pipeline ensure substantial cost savings compared to current standard practices.

biochemistry↗