Biology subjects

Krismer, K.

Publications and source records attributed to Krismer, K..

8 recordsLinked to original sources

bioRxiv2025

Proximity-labeling proteomics reveals remodeled interactomes and altered localization of pathogenic SHP2 variants

Missense mutations in PTPN11, which encodes the protein tyrosine phosphatase SHP2, are common in several developmental disorders and cancers. While many mutations disrupt auto-inhibition and hyperactivate SHP2, several do not enhance catalytic activity. Both activating and non-activating mutations could potentially drive pathogenic signaling by altering SHP2 interactions or localization. We employed proximity-labeling proteomics to map the interaction networks of wild-type SHP2, ten clinically-relevant mutants, and SHP2 bound to an inhibitor that stabilizes its auto-inhibited state. Our analyses revealed mutation- and inhibitor-dependent alterations in the SHP2 interactome, with several mutations also changing localization. Some mutants had increased mitochondrial localization and impacted mitochondrial function. This study provides a resource for exploring SHP2 signaling and offers new insights into the molecular basis of SHP2-driven diseases. Furthermore, this work highlights the capacity for proximity-labeling proteomics to detect missense-mutation-dependent changes in protein interactions and localization.

molecular biology↗

bioRxiv2022

A global atlas of substrate specificities for the human serine/threonine kinome

Protein phosphorylation is one of the most widespread post-translational modifications in biology. With the advent of mass spectrometry-based phosphoproteomics, more than 200,000 sites of serine and threonine phosphorylation have been reported, of which several thousand have been associated with human diseases and biological processes. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein Ser/Thr kinases encoded in the human genome is responsible. Here, we utilize synthetic peptide libraries to profile the substrate sequence specificity of nearly every functional human Ser/Thr kinase. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. Our kinome-wide dataset was used to computationally annotate and identify the most likely protein kinases for every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites where the protein kinases involved have been previously identified, our predictions were in excellent agreement. When this approach was applied to examine the signaling response of tissues and cell lines to hormones, growth factors, targeted inhibitors, and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the full extent of substrate specificity of the human Ser/Thr kinome, illuminate cellular signaling responses, and provide a rich resource to link unannotated phosphorylation events to biological pathways.

systems biology↗

bioRxiv2021

seqgra: Principled Selection of Neural Network Architectures for Genomics Prediction Tasks

Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process. The method can be used to (1) generate data under the assumption of a hypothesized model of genome regulation, (2) identify neural network architectures capable of recovering the rules of said model, and (3) analyze a models predictive performance as a function of training set size, noise level, and the complexity of the rules behind the simulated data.

bioinformatics↗

bioRxiv2021

spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

bioinformatics↗

bioRxiv2020

Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay

A key mechanism in cellular regulation is the ability of the transcriptional machinery to physically access DNA. Pioneer transcription factors interact with DNA to open chromatin, which subsequently enables changes to gene expression during development, disease, or as a response to environmental stimuli. However, the regulation of DNA accessibility via the recruitment of transcription factors is difficult to understand in the context of the native genome because every genomic site is distinct in multiple ways. Here we introduce the Multiplexed Integrated Accessibility Assay (MIAA), a multiplexed parallel reporter assay which measures changes to genome accessibility as a result of the integration of synthetic oligonucleotide phrase libraries into a controlled, natively inaccessible genomic context. We apply MIAA to measure the effects of sequence motifs on cell type-specific DNA accessibility between mouse embryonic stem cells and embryonic stem cell-derived definitive endoderm cells, screening a total of 7,905 distinct phrases. MIAA is able to recapitulate differential accessibility patterns of 100-nt sequences derived from natively differential genomic regions, identifying the presence of E-box motifs common to epithelial-mesenchymal transition driver transcription factors in stem cell-specific accessible regions that become repressed during differentiation to endoderm. We further present causal evidence that a single binding motif for a key regulatory transcription factor is sufficient to open chromatin, and classify sets of stem cell-specific, endoderm-specific, and shared pioneer factor motifs. We also demonstrate that over-expression of two definitive endoderm transcription factors, Brachyury and FoxA2, results in changes to accessibility in phrases containing their respective DNA-binding motifs. Finally, we use MIAA results to explore the order of motif interactions and identify preferential motif ordering arrangements that appear to have an effect on accessibility.

genomics↗

bioRxiv2019

IDR2D identifies reproducible genomic interactions

Chromatin interaction data from protocols such as ChIA-PET, HiChIP, and HiC provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce a generalization of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

bioinformatics↗

bioRxiv2018

Transite: A computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression

RNA-binding proteins (RBPs) play critical roles in regulating gene expression by modulating splicing, RNA stability, and protein translation. In response to various stimuli, alterations in RBP function contribute to global changes in gene expression, but identifying which specific RBPs are responsible for the observed changes in gene expression patterns remains an unmet need. Here, we present Transite a multi-pronged computational approach that systematically infers RBPs influencing gene expression changes through alterations in RNA stability and degradation. As a proof of principle, we applied Transite to public RNA expression data from human patients with non-small cell lung cancer whose tumors were sampled at diagnosis, or after recurrence following treatment with platinum-based chemotherapy. Transite implicated known RBP regulators of the DNA damage response and identified hnRNPC as a new modulator of chemotherapeutic resistance, which we subsequently validated experimentally. Transite serves as a generalizable framework for the identification of RBPs responsible for gene expression changes that drive cell-state transitions and adds additional value to the vast wealth of publicly-available gene expression data.

bioinformatics↗

bioRxiv2018

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

bioinformatics↗