Biology subjects

Chance, M. R.

Publications and source records attributed to Chance, M. R..

7 recordsLinked to original sources

bioRxiv2026

scProfiterole: Clustering of Single-Cell Proteomic DataUsing Graph Contrastive Learning via Spectral Filters

Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type identification for single cell proteomic (scProteomics) data are relatively scarce. Adoption of algorithms that have been developed for scRNAseq into scProteomics is challenged by the larger number of drop-outs, missing data, and noise in single cell proteomic data. Graph contrastive learning (GCL) on cell-to-cell similarity graphs derived from single cell protein expression profiles show promise in cell type identification. However, missing edges and noise in the cell-to-cell similarity graph requires careful design of convolution matrices to overcome the imperfections in these graphs. Here, we introduce scPO_SCPLOWROFITEROLEC_SCPLOW (Single Cell Proteomics Clustering via Spectral Filters), a computational framework to facilitate effective use of spectral graph filters in GCL-based clustering of single cell proteomic data. Since clustering assumes a homophilic network topology, we consider three types of homophilic filters: (i) random walks, (ii) heat kernels, (iii) beta kernels. Direct implementation of these filters is computationally prohibitive, thus the filters are either truncated or approximated in practice. To overcome this limitation, scPO_SCPLOWROFITEROLEC_SCPLOW uses Arnoldi orthonormalization to implement polynomial interpolations of any given spectral graph filter. Our results on comprehensive single cell proteomic data show that (i) graph contrastive learning with learnable polynomial coefficients that are carefully initialized improves the effectiveness and robustness of cell type identification, (ii) heat kernels and beta kernels improve clustering performance over adjacency matrices or random walks, and (iii) polynomial interpolation of spectral filters outperforms approximation or truncation. The source code for scPO_SCPLOWROFITEROLEC_SCPLOW and Supplementary Materials are available at https://github.com/mustafaCoskunAgu/scProfiterole.

bioinformatics↗

bioRxiv2025

Structural and functional basis of PU.1-BAF interaction enables targeting of lineage-specific transcription

Chromatin remodeling complexes like BAF finely regulate transcriptional programs by working in concert with transcription factors. However, evidence is lacking as to whether TFs interact directly with BAF and if so, what the mechanistic and structural principles governing these critical interactions are. Here, we establish direct engagement between a crucial and therapeutically relevant full-length human TF, PU.1 (SPI1), and BAF. Within this 1MDa+ complex, we precisely map the binding site of PU.1 to a YEATS-like domain on BAF60A and elucidate the structure of the PU.1-BAF60A complex. This work reveals that upon binding to BAF, a disordered region within the TF adopts a helical conformation, and that disruption of this functionally critical interface via knockdown abrogates the ability of PU.1 to rescue cell viability. To explore the druggability of TF-BAF protein-protein interactions (PPIs), we conducted a high-throughput screen that identified small molecules capable of disrupting the PU.1-BAF60A PPI by binding to BAF60A. Co-crystal structures reveal distinct compound binding modes that converge on a critical PU.1-BAF60A interaction hotspot. These findings define, for the first time, the structural interface between a human TF and a chromatin remodeling complex and establish a platform that enables the targeting of these interactions, a novel mechanism in cancer therapeutics.

biochemistry↗

bioRxiv2023

Making Proteomics Accessible: RokaiXplorer for interactive analysis of phospho-proteomic data

In this work, we present RokaiXplorer, an intuitive web tool designed to address the scarcity of user-friendly solutions for proteomics and phospho-proteomics data analysis and visualization. RokaiXplorer streamlines data processing, analysis, and visualization through an interactive online interface, making it accessible to researchers without specialized training in proteomics or data science. With its comprehensive suite of modules, RokaiXplorer facilitates the analysis of phosphosites, proteins, kinases, and gene ontology terms. The tool offers functionalities such as data normalization, statistical testing, enrichment analysis, subgroup analysis, report generator, and multiple visualizations, including volcano plots, bar plots, heatmaps, tables, and interactive networks. Additionally, RokaiXplorer allows researchers to effortlessly deploy their own data browsers, fostering the sharing of research data and findings interactively. By providing simplicity, efficiency, and multi-level data analysis within a single application, it is poised to become a valuable resource for the scientific community working with phospho-proteomic data. Access RokaiXplorer at: http://explorer.rokai.io

systems biology↗

bioRxiv2022

Hypo-responsiveness of human alveolar macrophages to IFN-γ is not due to attenuated STAT1 signaling

Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment of Mycobacterium tuberculosis (Mtb) in lung macrophages is highly dependent on IFN-{gamma} secreted by Th-1 lymphocytes, however, the proteomic IFN-{gamma} response profile in AM remains mostly unknown. In this study, we measured IFN-{gamma} induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-{gamma} stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-{gamma} in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-{gamma} which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

immunology↗

bioRxiv2022

Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites

MotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations. ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks. ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations. AvailabilityThe code and data are available at compbio.case.edu/NetKSA.

bioinformatics↗

bioRxiv2021

GABARAP membrane conjugation sequesters the FLCN-FNIP tumor suppressor complex to activate TFEB and lysosomal biogenesis

Adaptive changes in lysosomal capacity are driven by the transcription factors TFEB and TFE3 in response to increased autophagic flux and endolysosomal stress, yet the molecular details of their activation are unclear. LC3 and GABARAP members of the ATG8 protein family are required for selective autophagy and sensing perturbation within the endolysosomal system. Here we show that during single membrane ATG8 conjugation (SMAC), Parkin-dependent mitophagy, and Salmonella-induced xenophagy, the membrane conjugation of GABARAP, but not LC3, is required for activation of TFEB/TFE3 to control lysosomal homeostasis and capacity. GABARAP directly binds to a novel LC3-interacting motif (LIR) in the FLCN/FNIP tumor suppressor complex with picomolar affinity and regulates its relocalization to these GABARAP-conjugated membrane compartments. This disrupts the regulation of RagC/D by the FLCN/FNIP GAP complex, resulting in impaired mTOR-dependent phosphorylation of TFEB without changing mTOR activity towards other substrates. Thus, the GABARAP-FLCN/FNIP-TFEB axis serves as a universal molecular sensor that coordinates lysosomal homeostasis with perturbations and cargo flux within the autophagy-lysosomal network.

cell biology↗

bioRxiv2020

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules in Breast Cancer

MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. ResultsWe develop a network-based algorithm, CoPPNO_SCPLOWETC_SCPLOW, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural, and functional association of phosphosites, kinase-substrate associations, and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry based phosphorylation datasets for breast cancer, and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive breast cancer subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers. Availability and implementationCoPPNO_SCPLOWETC_SCPLOW is available at http://compbio.case.edu/coppnet/

bioinformatics↗