Protein Signaling Networks Research Articles

ScMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data

Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results.

Kinome profiling reveals pathogenic variant specific protein signalling networks in MEN2 children with Medullary Thyroid Cancer

Multiple Endocrine Neoplasia Type 2 (MEN2) is an autosomal dominant disease caused by pathogenic variants in the receptor tyrosine kinase RET, with strong genotype-phenotype correlations. The development and progression of these tumours are not always predictable even within families with the same RET pathogenic variant, demonstrating a need for better understanding of the underlying molecular mechanisms. Precision molecular medicine is not widely used and the standard of care remains prophylactic thyroidectomy. This absence of curative approaches is exacerbated by the lack of novel therapeutic markers/targets. In this study, we investigated the functional kinome of 24 familial MEN2 patients. We identified MEN2 subtype and RET pathogenic variant-specific alterations in signalling pathways including mTOR, PKA, NF-κB and focal adhesions, which were validated in patient thyroid tissue. Overall, our study of MEN2 functional kinomes uncovers novel specific drivers of MEN2 disease and its pathogenic variant subtypes, identifying new potential therapeutic targets for MEN2.

Abstract 2852: Mapping single cell spatial 3D genome landscape of localized and metastatic breast cancer sub-types

Abstract Metastatic breast cancer is the most aggressive form of breast cancer leading to poor disease-free patient survival and accounting for 90% of all breast cancer-related deaths. The specific mechanisms that promote metastasis are not fully understood owing to the complex interplay between cancer cells and their surrounding microenvironment. This microenvironment involves clonal and sub-clonal heterogeneity in genomic, transcriptomic and protein signaling network across different cancer cell sub-populations. Understanding the molecular mechanism behind cancer metastasis demands state of the art single cell and spatial technologies that can provide deeper insight on disease progression. Since, cancer is heavily influenced by genetic changes, the study of the 3D- genome organization in single cells helps to improve understanding of disease progression and prognosis. The genome is organized and packaged in a highly structured manner inside the nucleus of mammalian cells and exhibits dynamic reorganization throughout disease progression. Understanding this 3D organization at the single-cell level with high spatial resolution is crucial for disease research. In this abstract, we present a novel jebFISHTM protocol on the PaintScapeTM platform that can be used to understand differences in global 3D genome organization among several localized and metastatic breast cancer cell lines at single cell, sub-population and population level. Using a highly multiplexed genome wide panel of chromosomal targets, we will show disruption in chromosome territory, radius of gyration, chromosomal instability between localized and metastatic breast cancer cells at single cell and sub-population level. We will also show the signature of specific 3D genome structural alterations and interchromosomal interactions related to breast cancer metastasis compared to normal breast cells. In addition, we investigated differences in global 3D genome architecture of different breast cancer sub-types such as HER2 enriched vs triple negative breast cancer cells. We will show how different breast cancer sub-types carry specific chromosomal instability and differential interchromosomal interaction patterns at single cell, sub-chromosomal and sub-population level. We envision this study will improve our understanding on breast cancer disease progression and provide deeper insight into the underlying genomic heterogeneity of single cancer cells and sub-populations which might help to design better treatment options in the future. We propose that this study serves as proof-of-principle and is generalizable to other types of cancers where changes in 3D genome organization may occur at different cancer stages. Citation Format: Shyamtanu Chattoraj, Huy Nguyen, Jude Dunne. Mapping single cell spatial 3D genome landscape of localized and metastatic breast cancer sub-types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2852.

Abstract 435: JAK/STAT1-interferon-ISGylation networks in breast cancer resistance to inhibitors of FOXM1 and CDK4/6

Abstract Resistance to targeted therapies often develops in advanced estrogen receptor (ER)-positive breast cancer, but the mechanisms underlying this resistance are still not fully known. We show that ER-positive FOXM1 inhibitor resistant cells and CDK4/6 inhibitor (Palbociclib and Abemaciclib) resistant cells all exhibit an increased JAK/STAT1-interferon responsive gene and protein signaling network with elevated interferon stimulated gene15 (ISG15). ISG15 protein is present as high intracellular free ISG15 and also as increased ISGylated protein conjugates that can be markedly reduced by treatment of resistant cells with the JAK1/2 inhibitor ruxolitinib. Likewise, resistant cells contain increased levels of ubiquitin-like conjugating enzymes HERC5 and HERC6 and ubiquitin ligases (e.g., UBE2L6) known to facilitate the transfer of ISG15 onto cell proteins. Notably, elevated ISG15 and elevated HERC5 and HERC6 are all associated with poorer relapse-free and overall patient survival. Breast cancer cells resistant to the CDK4/6 inhibitors Palbo or Abema, which are often used in first line treatment of patients with HR-positive breast cancer, and cells resistant to FOXM1 inhibitor show some similarities in the ISGylated protein patterns. However, there are also some differences observed in these patterns between Palbo and Abema and FOXM1 inhibitor (NB73) resistant cells. Upregulation of this STAT1-interferon-ISGylation network in ER-positive breast cancers displaying resistance to three different drugs suggests it may be centrally involved in supporting the drug-resistant state. Importantly, Palbo and Abema resistant cells and organoids can still be effectively growth inhibited by FOXM1 inhibitor NB73, and likewise, FOXM1 inhibitor resistant cells and organoids are sensitive to suppression of viability by Palbo or Abema. This suggests that sequential treatment approaches might be effective in overcoming resistance and enabling the suppression of these drug-resistant cancers.(Supported by Breast Cancer Research Foundation grant BCRF-083 and NIH R01CA220284 to BSK and BCRF grant BCRF-145 to RS) Citation Format: Benita S. Katzenellenbogen, Yvonne Ziegler, Sandeep Kumar, Blake N. Plotner, Carlos M. Saeh, Grace O. Pento, Sung Hoon Kim, Rachel Schiff, John A. Katzenellenbogen. JAK/STAT1-interferon-ISGylation networks in breast cancer resistance to inhibitors of FOXM1 and CDK4/6 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 435.

Abstract 5241: Mapping spatial 3D genome landscape of breast cancer cells in different tumor immune microenvironments using onco-immune co-culture as a model

One of the leading causes of tumor cell heterogeneity is the complex nature of the tumor immune microenvironment involving crosstalk between different signaling pathways of cancer cells and immune cells. For cancer cells to grow into a primary tumor and then metastasize into secondary tumors, they must avoid or overcome the attack of different types of immune cells, including macrophages, natural killer (NK) cells, and T lymphocytes. For example, cancer cells can polarize macrophages from “tumor killing” to “tumor promoting” type using specific signaling molecules. Such tumor immune crosstalk is dependent on the clonal and sub-clonal heterogeneity in genomic, transcriptomic and protein signaling networks across different cancer cell sub-populations, immune cells and its surrounding microenvironment. Since structural aberrations affect gene dysregulation, the study of the 3D- genome organization in single cells can help in better understanding of disease progression and prognosis. The genome is organized and packaged in a highly structured manner inside the nucleus of mammalian cells and exhibits dynamic reorganization throughout disease progression. Understanding this 3D organization at the single-cell level with high spatial resolution is crucial for immune-oncology disease research such as investigating tumor-immune cell interactions. In this abstract, we present a novel jebFISHTM protocol on the PaintScapeTM platform that can be used to investigate immune cell modulated differences in global 3D genome organization among different localized and metastatic breast cancer cell lines at single cell, sub-population and population level. Using a genome wide panel of chromosomal targets on an onco-immune co-culture system involving different breast cancer cell lines and CD4+ T-cells or macrophages, we will show disruption in chromosome territory, radius of gyration, chromosomal instability between localized and metastatic breast cancer cells at single cell and sub-population level. We observed signatures of specific 3D genome structural alterations and interchromosomal interactions in cancer cells and/or immune cells, related to the effect of different types of immune response. We will show how the 3D architecture of key genomic loci and its associated regulatory elements belonging to major cancer signaling pathways exhibit specific signatures including differential folding, chromosomal instability, differential interchromosomal interaction and structural variation patterns at single cell, sub-chromosomal and sub-population level. We envision this study will improve our understanding on breast cancer tumor immune microenvironment and provide deeper insight into the underlying genomic heterogeneity of single cancer cells and sub-populations which might help to design better treatment options in the future. Citation Format: Jude Dunne, Huy Nguyen, Shyamtanu Chattoraj. Mapping spatial 3D genome landscape of breast cancer cells in different tumor immune microenvironments using onco-immune co-culture as a model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5241.

Sketch of a novel approach to a neural model

There is room on the inside. In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical integration model of neural processing. The horizontal plane consists of a network of neurons connected by adaptive transmission links. This fits with standard computational neuroscience approaches. Each individual neuron also has a vertical dimension with internal parameters steering the external membrane-expressed parameters. These determine neural transmission. The vertical system consists of (a) external parameters at the membrane layer, divided into compartments (spines, boutons) (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In such models, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are systematically separated. This is an important conceptual advance over synaptic weight models. We discuss the membrane-based (external) filtering and selection of outside signals for processing. Not every transmission event leaves a trace. We also illustrate the neuron-internal computing strategies from intracellular protein signaling to the nucleus as the core system. We want to show that the individual neuron has an important role in the computation of signals. Many assumptions derived from the synaptic weight adjustment hypothesis of memory may not hold in a real brain. We present the neuron as a self-programming device, rather than passively determined by ongoing input. We believe a new approach to neural modeling will benefit the third wave of AI. Ultimately we strive to build a flexible memory system that processes facts and events automatically.

ARIES domains: functional signaling units of type I interferon responses.

The innate immune system relies on a network of signaling proteins classified by shared domains, which serve as functional units that orchestrate inflammatory and host defensive activities. Within type I interferon (IFN) responses, the stimulator of interferon genes protein (STING), mitochondrial antiviral-signaling protein (MAVS), Toll-IL-1 receptor-resistance protein domain-containing adapter-inducing interferon-β (TRIF), Toll-like receptor adapter interacting with SLC15A4 on the lysosome (TASL), insulin receptor tyrosine kinase substrate protein of 53 kDa (IRSp53), and GEM interacting protein (GMIP) utilize a conserved pLxIS motif to recruit IRF family transcription factors. Notably, the pLxIS motif functions within a larger signaling unit, which is referred to here as an Activator of Interferon Expression via a pLxIS motif (ARIES) domain. ARIES domains consist of the pLxIS motif and adjacent kinase activation motifs that together drive IFN responses. This review explores how ARIES domains promote immune responses via shared and distinct signaling mechanisms, protein localization, and regulation of metabolic shifts, underscoring their evolutionary conservation and critical role in host defense.

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks.

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

Randomization based evaluation of distinct topological and cancer expression characteristics of mutually acting gene pairs.

Small scale molecular network patterns and motifs are crucial for systems level understanding of cellular information transduction. Using randomizations, we statistically explored, previously overlooked basic patterns of mutually acting pairs, i.e. mutually positive (PP) or negative (NN) and positive-negative (PN) pairs, in two comprehensive and distinct large-scale molecular networks from literature; the human protein signaling network (PSN) and the human gene regulatory network (GRN). Only the positive and negative signs of all interacting pairs were randomized, while the gene pairs and the number of positive and negative signs in the original network were kept constant. While the numbers of NN and PN pairs were significantly higher, the number of PP pairs was significantly lower than randomly expected values. Genes participating in mutual pairs were more connected than other genes. NN genes were more connected than PP and PN in GRN for all types of degree values, including in, out, positive or negative connections, but less connected for in-degree and more connected for out-degree values in PSN. They also had significantly high number of intersections with each other and PN pairs than randomly expected values, indicating potential cooperative mechanisms. The three mutual interaction designs we examined had distinct RNA and protein expression correlation characteristics. NN protein pairs were uniquely over-represented across normal tissue samples, whose negative correlations were lost across cancer tissue samples. PP and PN pairs showed non-random positive RNA or protein expression correlation across normal or cancer tissue samples. Moreover, we developed an online tool, i.e. MGPNet, for further user specific analysis of mutual gene pairs. We identified SNCA with significantly enriched negatively correlated NN pairs. Unique non-random characteristics of mutual gene pairs identified in two different comprehensive molecular networks could provide valuable information for a better comparative understanding of molecular design principles between normal and cancer states. Insight Box/Paragraph Statement: This study provides a systems-level perspective on cellular information transduction by analyzing mutually acting pairs of genes. By examining mutually positive (PP), mutually negative (NN), and positive-negative (PN) pairs in the human protein signaling network (PSN) and the human gene regulatory network (GRN), we uncover significant variations in their connectivity and expression correlation. Our findings highlight the unique features of NN pairs across normal and cancer tissues and offer insights into molecular design principles. The development of the MGPNet tool further enhances user-specific analyses, enabling a deeper understanding of gene pair mechanisms and their potential cooperative roles in cellular processes.

Functional kinome profiling reveals brain protein kinase signaling pathways and gene networks altered by acute voluntary exercise in rats.

Regular exercise confers numerous physical and mental health benefits, yet individual variability in exercise participation and outcomes is still poorly understood. Uncovering the neurobiological mechanisms governing exercise behavior is essential for promoting physical activity and developing targeted interventions for related disorders. While genetic studies have provided insights, they often cannot account for protein-level alterations, such as changes in kinase activity. Here, we employ protein kinase activity profiling to delineate brain protein kinase activity and signaling networks modulated by acute voluntary exercise in rats. Focusing on the dorsal striatum, which governs voluntary exercise, as well as the hippocampus, which is susceptible to modulation by physical activity, we aim to understand the molecular basis of exercise behavior. Utilizing high throughput kinome array profiling and advanced pathway analyses, we identified protein kinase signaling pathways implicated in regulating voluntary exercise. Pathway analysis using Gene Ontology (GO) revealed significant alterations in 155 GO terms in the dorsal striatum and 206 GO terms in the hippocampus. Changes in kinase activity were observed in the striatum and hippocampus between the exercise (voluntary wheel running, VWR) and sedentary control rats. In both regions, global serine-threonine kinase (STK) activity was decreased, while global phospho-tyrosine kinase (PTK) activity was increased in VWR rats compared to control rats. We also identified specific kinases altered in VWR rats, including the IKappaB Kinase (IKK) and protein kinase delta (PKD) families. C-terminal src Kinase (CSK), epidermal growth factor (EGFR), and vascular endothelial growth factor receptor (VEGFR) tyrosine kinase were also enriched. These findings suggest regional heterogeneity of kinase activity following voluntary exercise, emphasizing potential molecular mechanisms underlying exercise behavior. This exploratory study lays the groundwork for future investigations into the causality of variations in exercise outcomes among individuals and different sexes, as well as the development of targeted interventions to promote physical activity and combat associated chronic diseases.

Logic-Based Modeling of Inflammatory Macrophage Crosstalk with Glomerular Endothelial Cells in Diabetic Kidney Disease.

Diabetic kidney disease is a complication in one out of three patients with diabetes. Aberrant glucose metabolism in diabetes leads to structural and functional damage in glomerular tissue and a systemic inflammatory immune response. Complex cellular signaling is at the core of metabolic and functional derangement. Unfortunately, the mechanism underlying the role of inflammation in glomerular endothelial cell dysfunction during diabetic kidney disease is not fully understood. Mathematical models in systems biology allow the integration of experimental evidence and cellular signaling networks to understand mechanisms involved in disease progression. This study developed a logic-based ordinary differential equations model to study inflammatory crosstalk between macrophages and glomerular endothelial cells during diabetic kidney disease progression using a protein signaling network stimulated with glucose and lipopolysaccharide. This modeling approach reduced the biological parameters needed to study signaling networks. The model was fitted to and validated against available biochemical data from \textit{in vitro} experiments. The model identified mechanisms for dysregulated signaling in macrophages and glomerular endothelial cells during diabetic kidney disease. In addition, the influence of signaling interactions on glomerular endothelial cell morphology through selective knockdown and downregulation was investigated. Simulation results showed that partial knockdown of VEGF receptor 1, PLC-γ, adherens junction proteins, and calcium partially recovered the intercellular gap width between glomerular endothelial cells. These findings contribute to understanding signaling and molecular perturbations that affect the glomerular endothelial cells in the early stage of diabetic kidney disease.

MTORC2: A neglected player in aging regulation.

Mammalian target of rapamycin (mTOR) is a serine/threonine kinase that plays a pivotal role in various biological processes, through integrating external and internal signals, facilitating gene transcription and protein translation, as well as by regulating mitochondria and autophagy functions. mTOR kinase operates within two distinct protein complexes known as mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), which engage separate downstream signaling pathways impacting diverse cellular processes. Although mTORC1 has been extensively studied as a pro-proliferative factor and a pro-aging hub if activated aberrantly, mTORC2 received less attention, particularly regarding its implication in aging regulation. However, recent studies brought increasing evidence or clues for us, which implies the associations of mTORC2 with aging, as the genetic elimination of unique subunits of mTORC2, such as RICTOR, has been shown to alleviate aging progression in comparison to mTORC1 inhibition. In this review, we first summarized the basic characteristics of mTORC2, including its protein architecture and signaling network. We then focused on reviewing the molecular signaling regulation of mTORC2 in cellular senescence and organismal aging, and proposed the multifaceted regulatory characteristics under senescent and nonsenescent contexts. Next, we outlined the research progress of mTOR inhibitors in the field of antiaging and discussed future prospects and challenges. It is our pleasure if this review article could provide meaningful information for our readers and call forth more investigations working on this topic.

Neomorphic Gαo mutations gain interaction with Ric8 proteins in GNAO1 encephalopathies.

GNAO1 mutated in pediatric encephalopathies encodes the major neuronal G protein Gαo. Of the more than 80 pathogenic mutations, most are single amino acid substitutions spreading across the Gαo sequence. We performed extensive characterization of Gαo mutants, showing abnormal GTP uptake and hydrolysis and deficiencies in binding Gβγ and RGS19. Plasma membrane localization of Gαo was decreased for a subset of mutations that leads to epilepsy; dominant interactions with GPCRs also emerged for the more severe mutants. Pathogenic mutants massively gained interaction with Ric8A and, surprisingly, Ric8B proteins, relocalizing them from cytoplasm to Golgi. Of these 2 mandatory Gα-subunit chaperones, Ric8A is normally responsible for the Gαi/Gαo, Gαq, and Gα12/Gα13 subfamilies, and Ric8B solely responsible for Gαs/Gαolf. Ric8 mediates the disease dominance when engaging in neomorphic interactions with pathogenic Gαo through imbalance of the neuronal G protein signaling networks. As the strength of Gαo-Ric8B interactions correlates with disease severity, our study further identifies an efficient biomarker and predictor for clinical manifestations in GNAO1 encephalopathies. Our work uncovers the neomorphic molecular mechanism of mutations underlying pediatric encephalopathies and offers insights into other maladies caused by G protein malfunctioning and further genetic diseases.

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

Abstract 3490: Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks

Abstract Sex differences in lung adenocarcinoma (LUAD) are evident in incidence rates, prognostic outcomes, and therapy responses, yet the underlying molecular mechanisms driving these disparities remain underexplored. In this study, we conducted a comprehensive proteogenomic analysis encompassing 38 females and 73 males with LUAD from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Employing Transcription Inference using Gene Expression and Regulatory data (TIGER), we inferred sex-differentially activated transcription factors (TFs) from The Cancer Genome Atlas (TCGA) LUAD gene expression data and identified sex-differentially activated kinases using CPTAC protein phosphorylation data. We further constructed a comprehensive kinase-TF signaling network by integrating these sex-differentially activated kinases with TFs, identifying all paths shorter than 3 in the protein interaction networks to highlight druggable pathways. Our analyses revealed that many proteins exhibit not only sex-biased abundance but also sex-biased phosphorylation and acetylation. Furthermore, these sex-biased proteins were associated with critical biological pathways including cell proliferation, immune response, and metabolism. Using kinase-TF signaling networks, we found substantial sex bias in the activities of clinically actionable TFs and kinases, including the glucocorticoid receptor (NR3C1), AR, AURKA, CDK6, and MAPK14. Leveraging the PRISM cancer cell line screening database, we identified several small-molecule drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors, potentially exhibiting sex-specific efficacy as LUAD therapeutics. Our findings showed that the activity of some clinically relevant TFs and kinases differ by sex in LUAD, underscoring the need to consider sex as a biological variable and the utility of multi-omics integrative protein signaling networks in advancing our understanding of cancer biology and the development of sex-aware therapeutics. Citation Format: Chen Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush, Camila M. Lopes-Ramos. Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3490.

DephosNet: A Novel Transfer Learning Approach for Dephosphorylation Site Prediction

Protein dephosphorylation is the process of removing phosphate groups from protein molecules, which plays a vital role in regulating various cellular processes and intricate protein signaling networks. The identification and prediction of dephosphorylation sites are crucial for this process. Previously, there was a lack of effective deep learning models for predicting these sites, often resulting in suboptimal outcomes. In this study, we introduce a deep learning framework known as “DephosNet”, which leverages transfer learning to enhance dephosphorylation site prediction. DephosNet employs dual-window sequential inputs that are embedded and subsequently processed through a series of network architectures, including ResBlock, Multi-Head Attention, and BiGRU layers. It generates predictions for both dephosphorylation and phosphorylation site probabilities. DephosNet is pre-trained on a phosphorylation dataset and then fine-tuned on the parameters with a dephosphorylation dataset. Notably, transfer learning significantly enhances DephosNet’s performance on the same dataset. Experimental results demonstrate that, when compared with other state-of-the-art models, DephosNet outperforms them on both the independent test sets for phosphorylation and dephosphorylation.

Control of intracellular pH and bicarbonate by CO2 diffusion into human sperm

The reaction of CO2 with H2O to form bicarbonate (HCO3−) and H+ controls sperm motility and fertilization via HCO3−-stimulated cAMP synthesis. A complex network of signaling proteins participates in this reaction. Here, we identify key players that regulate intracellular pH (pHi) and HCO3− in human sperm by quantitative mass spectrometry (MS) and kinetic patch-clamp fluorometry. The resting pHi is set by amiloride-sensitive Na+/H+ exchange. The sperm-specific putative Na+/H+ exchanger SLC9C1, unlike its sea urchin homologue, is not gated by voltage or cAMP. Transporters and channels implied in HCO3− transport are not detected, and may be present at copy numbers < 10 molecules/sperm cell. Instead, HCO3− is produced by diffusion of CO2 into cells and readjustment of the CO2/HCO3−/H+ equilibrium. The proton channel Hv1 may serve as a unidirectional valve that blunts the acidification ensuing from HCO3− synthesis. This work provides a new framework for the study of male infertility.

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.

Oncogenic signaling is coupled to colorectal cancer cell differentiation state.

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this process, sequential mutations of APC, KRAS, TP53, and SMAD4 enable oncogenic signaling and establish the hallmarks of cancer. Here, we use mass cytometry of isogenic human colon organoids and patient-derived cancer organoids to capture oncogenic signaling, cell phenotypes, and differentiation states in a high-dimensional single-cell map. We define a differentiation axis in all tumor progression states from normal to cancer. Our data show that colorectal cancer driver mutations shape the distribution of cells along the differentiation axis. In this regard, subsequent mutations can have stem cell promoting or restricting effects. Individual nodes of the cancer cell signaling network remain coupled to the differentiation state, regardless of the presence of driver mutations. We use single-cell RNA sequencing to link the (phospho-)protein signaling network to transcriptomic states with biological and clinical relevance. Our work highlights how oncogenes gradually shape signaling and transcriptomes during tumor progression.

Abstract 875: Breast cancer bioinformatics: Untangling the roles of nucleolin and BRCA1 in dysregulated DNA repair

Abstract Proteins that enable cells to sense and repair DNA damage are essential for maintaining the stability of genomes across cell divisions; dysregulation of these processes is an established hallmark of tumorigenesis. Nucleolin (NCL), a major RNA binding protein (RBP) and a caretaker tumor suppressor protein BRCA1 (the breast &amp; ovarian cancer susceptibility gene) colocalize in breast cancer. Both NCL and BRCA1 have defined roles in homologous recombination (HR) and non-homologous end joining (NHEJ) DNA repair pathways which are often dysregulated in breast cancer. BRCA1, along with a complex molecular network of signaling proteins gets recruited to damaged replication forks to initiate the HR pathway where NCL function is also implicated. However, how NCL collaborates with BRCA1 to orchestrate the complex mechanism of DNA repair under stress conditions remains unknown. To address this gap in knowledge, we have applied in silico approaches to uncover the key players which drive the molecular interactions of NCL and BRCA1 at the damaged sites. We utilized the NIH PPI, IntAct, STRING, BioGRID, GeneMania, PrePPI, and Mentha databases to derive an overlapping interactome. In this study we present a comprehensive interactome analysis and 47 proteins identified that interact with both NCL and BRCA1. As expected, majority of these common interactors participate in DNA damage damage response along with several that are implicated in regulating the gene expression via chromatin remodeling/RNA binding or controlling cell proliferation. We classified this interactome into 5 major categories based on their GO functional annotations: chromatin remodeling, DNA damage, DNA repair, RNA-binding proteins, and cell cycle. Previously, we have successfully used computational approaches to model the RNA-binding domains (RBD) of NCL and delineate the binding interfaces between NCL-RBD and a subset of miRNA that are specifically dysregulated in breast cancer. Using the same strategy, we have modeled the full length NCL to provide a complete structural representation of the protein. Our model fills the gap in missing NCL structural data, especially the unexplored N- and C - termini that may play important roles in NCL protein-protein interactions. Our results provide predicted interaction scenarios between NCL and the subset of the overlapping interactome of NCL and BRCA1, focused on DNA repair mechanisms. These in silico models are critical to understand the protein complexity at the interface of damaged DNA to identify candidates that can be targeted in breast carcinoma. Citation Format: Nitu Farhin, Andy Lam, Anjana D. Saxena, Shaneen Singh. Breast cancer bioinformatics: Untangling the roles of nucleolin and BRCA1 in dysregulated DNA repair [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 875.