Mondrian Map: Piet Mondrian's Abstraction for Differential Pathway Analysis.

Motivation: Visualizing complex biological networks is a significant challenge, as traditional tools often struggle to represent data clearly and intuitively. Drawing inspiration from Piet Mondrian's abstract art style, we introduce Mondrian Map, a novel visualization tool that transforms the complexity of biological networks into a more organized, meaningful, and aesthetically appealing form. Results: In our case study of glioma progression, Mondrian Map reveals distinct pathway patterns across multiple patient profiles at different time points. Afterwards, the significance of these distinctive pathways in glioblastoma multiforme development is validated by recent literature. Mondrian Map's visually intuitive representation of complex biological networks enables researchers to easily identify crucial pathways and potential therapeutic targets. Moreover, The potential applications of Mondrian Map extend beyond biology, making it a versatile tool across domains. Availability and Implementation: The code was implemented in Python version 3.10 and is available through GitHub ( github.com/fuad021/mondrian-map ). The datasets used in this paper were retrieved from Glioma Longitudinal AnalySiS (GLASS) consortium. Contact: jakechen@uab.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

An NLP-based technique to extract meaningful features from drug SMILES.

NLP is a well-established field in ML for developing language models that capture the sequence of words in a sentence. Similarly, drug molecule structures can also be represented as sequences using the SMILES notation. However, unlike natural language texts, special characters in drug SMILES have specific meanings and cannot be ignored. We introduce a novel NLP-based method that extracts interpretable sequences and essential features from drug SMILES notation using N-grams. Our method compares these features to Morgan fingerprint bit-vectors using UMAP-based embedding, and we validate its effectiveness through two personalized drug screening (PSD) case studies. Our NLP-based features are sparse and, when combined with gene expressions and disease phenotype features, produce better ML models for PSD. This approach provides a new way to analyze drug molecule structures represented as SMILES notation, which can help accelerate drug discovery efforts. We have also made our method accessible through a Python library.

Photo-neuro-immuno-endocrinology: How the ultraviolet radiation regulates the body, brain, and immune system.

Ultraviolet radiation (UVR) is primarily recognized for its detrimental effects such as cancerogenesis, skin aging, eye damage, and autoimmune disorders. With exception of ultraviolet B (UVB) requirement in the production of vitamin D3, the positive role of UVR in modulation of homeostasis is underappreciated. Skin exposure to UVR triggers local responses secondary to the induction of chemical, hormonal, immune, and neural signals that are defined by the chromophores and extent of UVR penetration into skin compartments. These responses are not random and are coordinated by the cutaneous neuro-immuno-endocrine system, which counteracts the action of external stressors and accommodates local homeostasis to the changing environment. The UVR induces electrical, chemical, and biological signals to be sent to the brain, endocrine and immune systems, as well as other central organs, which in concert regulate body homeostasis. To achieve its central homeostatic goal, the UVR-induced signals are precisely computed locally with transmission through nerves or humoral signals release into the circulation to activate and/or modulate coordinating central centers or organs. Such modulatory effects will be dependent on UVA and UVB wavelengths. This leads to immunosuppression, the activation of brain and endocrine coordinating centers, and the modification of different organ functions. Therefore, it is imperative to understand the underlying mechanisms of UVR electromagnetic energy penetration deep into the body, with its impact on the brain and internal organs. Photo-neuro-immuno-endocrinology can offer novel therapeutic approaches in addiction and mood disorders; autoimmune, neurodegenerative, and chronic pain-generating disorders; or pathologies involving endocrine, cardiovascular, gastrointestinal, or reproductive systems.

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of individual cells. However, the effectiveness of the currently available tools for processing and interpreting these immense datasets is limited. We incorporated three Artificial Intelligence (AI) techniques into a toolkit for evaluating scRNAseq data: AI Autoencoding separates data from different cell types and subpopulations of cell types (cluster analysis); AI Sparse Modeling identifies genes and signaling mechanisms that are differentially activated between subpopulations (pathway/gene set enrichment analysis), and AI Semisupervised Learning tracks the transformation of cells from one subpopulation into another (trajectory analysis). Autoencoding was often used in data denoising; yet, in our pipeline, Autoencoding was exclusively used for cell embedding and clustering. The performance of our AI scRNAseq toolkit and other highly cited non-AI tools was evaluated with three scRNAseq datasets obtained from the Gene Expression Omnibus database. Autoencoder was the only tool to identify differences between the cardiomyocyte subpopulations found in mice that underwent MI or sham-MI surgery on postnatal day (P) 1. Statistically significant differences between cardiomyocytes from P1-MI mice and mice that underwent MI on P8 were identified for six cell-cycle phases and five signaling pathways when the data were analyzed via Sparse Modeling, compared to just one cell-cycle phase and one pathway when the data were analyzed with non-AI techniques. Only Semisupervised Learning detected trajectories between the predominant cardiomyocyte clusters in hearts collected on P28 from pigs that underwent apical resection (AR) on P1, and on P30 from pigs that underwent AR on P1 and MI on P28. In another dataset, the pig scRNAseq data were collected after the injection of CCND2-overexpression Human-induced Pluripotent Stem Cell-derived cardiomyocytes (CCND2hiPSC) into injured P28 pig heart; only the AI-based technique could demonstrate that the host cardiomyocytes increase proliferating by through the HIPPO/YAP and MAPK signaling pathways. For the cluster, pathway/gene set enrichment, and trajectory analysis of scRNAseq datasets generated from studies of myocardial regeneration in mice and pigs, our AI-based toolkit identified results that non-AI techniques did not discover. These different results were validated and were important in explaining myocardial regeneration.

How cancer hijacks the body's homeostasis through the neuroendocrine system.

During oncogenesis, cancer not only escapes the body's regulatory mechanisms, but also gains the ability to affect local and systemic homeostasis. Specifically, tumors produce cytokines, immune mediators, classical neurotransmitters, hypothalamic and pituitary hormones, biogenic amines, melatonin, and glucocorticoids, as demonstrated in human and animal models of cancer. The tumor, through the release of these neurohormonal and immune mediators, can control the main neuroendocrine centers such as the hypothalamus, pituitary, adrenals, and thyroid to modulate body homeostasis through central regulatory axes. We hypothesize that the tumor-derived catecholamines, serotonin, melatonin, neuropeptides, and other neurotransmitters can affect body and brain functions. Bidirectional communication between local autonomic and sensory nerves and the tumor, with putative effects on the brain, is also envisioned. Overall, we propose that cancers can take control of the central neuroendocrine and immune systems to reset the body homeostasis in a mode favoring its expansion at the expense of the host.

Pharmacogenomics of intravenous immunoglobulin response in Kawasaki disease.

Introduction: Kawasaki disease (KD) is a diffuse vasculitis in children. Response to high dose intravenous gamma globulin (IVIG), the primary treatment, varies according to genetic background. We sought to identify genetic loci, which associate with treatment response using whole genome sequencing (WGS). Method: We performed WGS in 472 KD patients with 305 IVIG responders and 167 non-responders defined by AHA clinical criteria. We conducted logistic regression models to test additive genetic effect in the entire cohort and in four subgroups defined by ancestry information markers (Whites, African Americans, Asians, and Hispanics). We performed functional mapping and annotation using FUMA to examine genetic variants that are potentially involved IVIG non-response. Further, we conducted SNP-set [Sequence] Kernel Association Test (SKAT) for all rare and common variants. Results: Of the 43,288,336 SNPs (23,660,970 in intergenic regions, 16,764,594 in introns and 556,814 in the exons) identified, the top ten hits associated with IVIG non-response were in FANK1, MAP2K3:KCNJ12, CA10, FRG1DP, CWH43 regions. When analyzed separately in ancestry-based racial subgroups, SNPs in several novel genes were associated. A total of 23 possible causal genes were pinpointed by positional and chromatin mapping. SKAT analysis demonstrated association in the entire MANIA2, EDN1, SFMBT2, and PPP2R5E genes and segments of CSMD2, LINC01317, HIVEPI, HSP90AB1, and TTLL11 genes. Conclusions: This WGS study identified multiple predominantly novel understudied genes associated with IVIG response. These data can serve to inform regarding pathogenesis of KD, as well as lay ground work for developing treatment response predictors.

Neuroendocrine signaling in the skin with a special focus on the epidermal neuropeptides.

The skin, which is comprised of the epidermis, dermis, and subcutaneous tissue, is the largest organ in the human body and it plays a crucial role in the regulation of the body's homeostasis. These functions are regulated by local neuroendocrine and immune systems with a plethora of signaling molecules produced by resident and immune cells. In addition, neurotransmitters, endocrine factors, neuropeptides, and cytokines released from nerve endings play a central role in the skin's responses to stress. These molecules act on the corresponding receptors in an intra-, juxta-, para-, or autocrine fashion. The epidermis as the outer most component of skin forms a barrier directly protecting against environmental stressors. This protection is assured by an intrinsic keratinocyte differentiation program, pigmentary system, and local nervous, immune, endocrine, and microbiome elements. These constituents communicate cross-functionally among themselves and with corresponding systems in the dermis and hypodermis to secure the basic epidermal functions to maintain local (skin) and global (systemic) homeostasis. The neurohormonal mediators and cytokines used in these communications regulate physiological skin functions separately or in concert. Disturbances in the functions in these systems lead to cutaneous pathology that includes inflammatory (i.e., psoriasis, allergic, or atopic dermatitis, etc.) and keratinocytic hyperproliferative disorders (i.e., seborrheic and solar keratoses), dysfunction of adnexal structure (i.e., hair follicles, eccrine, and sebaceous glands), hypersensitivity reactions, pigmentary disorders (vitiligo, melasma, and hypo- or hyperpigmentary responses), premature aging, and malignancies (melanoma and nonmelanoma skin cancers). These cellular, molecular, and neural components preserve skin integrity and protect against skin pathologies and can act as "messengers of the skin" to the central organs, all to preserve organismal survival.

WINNER: A network biology tool for biomolecular characterization and prioritization.

Background and contribution: In network biology, molecular functions can be characterized by network-based inference, or "guilt-by-associations." PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process. Results: We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding "non-seed" molecules to the original biomolecular interaction network consisting of "seed" molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree-preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND. Conclusion: WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.

Importance: An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records. Objectives: To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA). Design, Setting, and Participants: This diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which used existing radiographic images and expert-curated Sharp-van der Heijde (SvH) scores from 2 clinical studies (674 radiographic sets from 562 patients) for training (367 sets), leaderboard (119 sets), and final evaluation (188 sets). Challenge participants were tasked with developing methods to automatically quantify overall damage (subchallenge 1), joint space narrowing (subchallenge 2), and erosions (subchallenge 3). The challenge was finished on June 30, 2020. Main Outcomes and Measures: Scores derived from submitted algorithms were compared with the expert-curated SvH scores, and a baseline model was created for benchmark comparison. Performances were ranked using weighted root mean square error (RMSE). The performance and reproductivity of each algorithm was assessed using Bayes factor from bootstrapped data, and further evaluated with a postchallenge independent validation data set. Results: The RA2-DREAM Challenge received a total of 173 submissions from 26 participants or teams in 7 countries for the leaderboard round, and 13 submissions were included in the final evaluation. The weighted RMSEs metric showed that the winning algorithms produced scores that were very close to the expert-curated SvH scores. Top teams included Team Shirin for subchallenge 1 (weighted RMSE, 0.44), HYL-YFG (Hongyang Li and Yuanfang Guan) subchallenge 2 (weighted RMSE, 0.38), and Gold Therapy for subchallenge 3 (weighted RMSE, 0.43). Bootstrapping/Bayes factor approach and the postchallenge independent validation confirmed the reproducibility and the estimation concordance indices between final evaluation and postchallenge independent validation data set were 0.71 for subchallenge 1, 0.78 for subchallenge 2, and 0.82 for subchallenge 3. Conclusions and Relevance: The RA2-DREAM Challenge resulted in the development of algorithms that provide feasible, quick, and accurate methods to quantify joint damage in RA. Ultimately, these methods could help research studies on RA joint damage and may be integrated into electronic health records to help clinicians serve patients better by providing timely, reliable, and quantitative information for making treatment decisions to prevent further damage.

An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases.

Key molecular regulators of acquired radiation resistance in recurrent glioblastoma (GBM) are largely unknown, with a dearth of accurate preclinical models. To address this, we generated 8 GBM patient-derived xenograft (PDX) models of acquired radiation therapy-selected (RTS) resistance compared with same-patient, treatment-naive (radiation-sensitive, unselected; RTU) PDXs. These likely unique models mimic the longitudinal evolution of patient recurrent tumors following serial radiation therapy. Indeed, while whole-exome sequencing showed retention of major genomic alterations in the RTS lines, we did detect a chromosome 12q14 amplification that was associated with clinical GBM recurrence in 2 RTS models. A potentially novel bioinformatics pipeline was applied to analyze phenotypic, transcriptomic, and kinomic alterations, which identified long noncoding RNAs (lncRNAs) and targetable, PDX-specific kinases. We observed differential transcriptional enrichment of DNA damage repair pathways in our RTS models, which correlated with several lncRNAs. Global kinomic profiling separated RTU and RTS models, but pairwise analyses indicated that there are multiple molecular routes to acquired radiation resistance. RTS model-specific kinases were identified and targeted with clinically relevant small molecule inhibitors. This cohort of in vivo RTS patient-derived models will enable future preclinical therapeutic testing to help overcome the treatment resistance seen in patients with GBM.

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics.

Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

A nonredundant role for T cell-derived interleukin 22 in antibacterial defense of colonic crypts.

Interleukin (IL)-22 is central to immune defense at barrier sites. We examined the contributions of innate lymphoid cell (ILC) and T cell-derived IL-22 during Citrobacter rodentium (C.r) infection using mice that both report Il22 expression and allow lineage-specific deletion. ILC-derived IL-22 activated STAT3 in C.r-colonized surface intestinal epithelial cells (IECs) but only temporally restrained bacterial growth. T cell-derived IL-22 induced a more robust and extensive activation of STAT3 in IECs, including IECs lining colonic crypts, and T cell-specific deficiency of IL-22 led to pathogen invasion of the crypts and increased mortality. This reflected a requirement for T cell-derived IL-22 for the expression of a host-protective transcriptomic program that included AMPs, neutrophil-recruiting chemokines, and mucin-related molecules, and it restricted IFNγ-induced proinflammatory genes. Our findings demonstrate spatiotemporal differences in the production and action of IL-22 by ILCs and T cells during infection and reveal an indispensable role for IL-22-producing T cells in the protection of the intestinal crypts.

Corrigendum: Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

[This corrects the article DOI: 10.3389/fdata.2021.725276.].

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)-one drawn upon the cell-points expressing the gene (the "foreground curve") and the other drawn upon all cell-points in the cluster (the "background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster-thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster-thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.

Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as "clinotypes," such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)-patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.

Biological Network Mining.

In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation: (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.

Metabolic alterations mediated by STAT3 promotes drug persistence in CML.

Leukemic stem cells (LSCs) can acquire non-mutational resistance following drug treatment leading to therapeutic failure and relapse. However, oncogene-independent mechanisms of drug persistence in LSCs are incompletely understood, which is the primary focus of this study. We integrated proteomics, transcriptomics, and metabolomics to determine the contribution of STAT3 in promoting metabolic changes in tyrosine kinase inhibitor (TKI) persistent chronic myeloid leukemia (CML) cells. Proteomic and transcriptional differences in TKI persistent CML cells revealed BCR-ABL-independent STAT3 activation in these cells. While knockout of STAT3 inhibited the CML cells from developing drug-persistence, inhibition of STAT3 using a small molecule inhibitor sensitized the persistent CML cells to TKI treatment. Interestingly, given the role of phosphorylated STAT3 as a transcription factor, it localized uniquely to genes regulating metabolic pathways in the TKI-persistent CML stem and progenitor cells. Subsequently, we observed that STAT3 dysregulated mitochondrial metabolism forcing the TKI-persistent CML cells to depend on glycolysis, unlike TKI-sensitive CML cells, which are more reliant on oxidative phosphorylation. Finally, targeting pyruvate kinase M2, a rate-limiting glycolytic enzyme, specifically eradicated the TKI-persistent CML cells. By exploring the role of STAT3 in altering metabolism, we provide critical insight into identifying potential therapeutic targets for eliminating TKI-persistent LSCs.