High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

AIMS/HYPOTHESIS: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. METHODS: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. RESULTS: We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Neurodegeneration and neuroinflammation are linked, but independent of alpha-synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease.

A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α-synuclein. Alpha-synuclein (α-syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α-syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α-syn induced by striatal injection of α-syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α-syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α-syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α-syn inclusion formation is not the major driver in the early phases of PD-like neurodegeneration, but that microglia, activated by diffusible, oligomeric α-syn, may play a key role in this process. Our findings uncover new features of α-syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α-syn spreading.

Development of mode of action networks related to the potential role of PPARγ in respiratory diseases.

The peroxisome proliferator-activated receptor γ (PPARγ) is a key transcription factor, operating at the intercept of metabolic control and immunomodulation. It is ubiquitously expressed in multiple tissues and organs, including lungs. There is a growing body of information supporting the role of PPARγ signalling in respiratory diseases. The aim of the present study was to develop mode of action (MoA) networks reflecting the relationships between PPARγ signalling and the progression/alleviation of a spectrum of lung pathologies. Data mining was performed using the resources of the NIH PubMed and PubChem information systems. By linking available data on pathological/therapeutic effects of PPARγ modulation, knowledge-based MoA networking at different levels of biological organization (molecular, cellular, tissue, organ, and system) was performed. Multiple MoA networks were developed to relate PPARγ modulation to the progress or the alleviation of pulmonary disorders, triggered by diverse pathogenic, genetic, chemical, or mechanical factors. Pharmacological targeting of PPARγ signalling was discussed with regard to ligand- and cell type-specific effects in the context of distinct disease inductor- and disease stage-dependent patterns. The proposed MoA networking analysis allows for a better understanding of the potential role of PPARγ modulation in lung pathologies. It presents a mechanistically justified basis for further computational, experimental, and clinical monitoring studies on the dynamic control of PPARγ signalling in respiratory diseases.

Type 2 Diabetes: Multiple Genes, Multiple Diseases.

PURPOSE OF REVIEW: Type 2 diabetes (T2D), which accounts for the vast majority of diabetes cases, is essentially a diagnosis of exclusion in current clinical practice. Therefore, it is not surprising that T2D is heterogenous in terms of patients' clinical presentation, disease course, and response to treatment. This review summarizes published attempts to improve diabetes subclassification, with a particular focus on the role of genetics. RECENT FINDINGS: A handful of diabetes subclassification schemas have been proposed using clinical data (patient characteristics and laboratory values), with some subgroups associated with distinct management trends or complication risks. However, phenotypically driven classifications suffer from dependencies on time of variable measurement and are not readily linked to disease mechanism. Germline genetic data, in contrast, are essentially unchanged over a person's lifetime and rooted in mechanism. Clustering of T2D genetic loci has identified at least five groupings of loci representing mechanisms of disease that may aid in deconstructing heterogeneity of T2D, but further work is needed to determine clinical utility. Exciting progress in subclassification of diabetes has demonstrated initial steps in deconstructing disease heterogeneity. Incorporation of genetics into classification schemas will require additional research but has the potential to improve our understanding and management of T2D, both as a single disease and as a part of an integrated metabolic disease network.

Targeting IgG in Arthritis: Disease Pathways and Therapeutic Avenues.

Rheumatoid arthritis (RA) is a polygenic and multifactorial syndrome. Many complex immunological and genetic interactions are involved in the final outcome of the clinical disease. Autoantibodies (rheumatoid factors, anti-citrullinated peptide/protein antibodies) are present in RA patients' sera for a long time before the onset of clinical disease. Prior to arthritis onset, in the autoantibody response, epitope spreading, avidity maturation, and changes towards a pro-inflammatory Fc glycosylation phenotype occurs. Genetic association of epitope specific autoantibody responses and the induction of inflammation dependent and independent changes in the cartilage by pathogenic autoantibodies emphasize the crucial contribution of antibody-initiated inflammation in RA development. Targeting IgG by glyco-engineering, bacterial enzymes to specifically cleave IgG/alter N-linked Fc-glycans at Asn 297 or blocking the downstream effector pathways offers new avenues to develop novel therapeutics for arthritis treatment.

A Disease Pathway Framework for Pain Point Identification and Elaboration of Product Requirements Across Patient Care Plan Using Innovation Think Tank Global Infrastructure.

Healthcare providers as well as medical technologists lay a strong focus on clinical conditions for patient centric care delivery. Currently, the challenges are to (1) obtain a consolidated view of various stakeholders and pain points for the entire disease lifecycle, (2) identify interdependencies between different stages of the disease, and (3) prioritize solutions based on customer needs. A structured approach is required to address clinical needs across disease care plans tailored to different geographies and ethnicities. Innovation Think Tank (ITT) teams across multiple locations formed focus groups to elaborate the pathways of 22 global diseases, selected based on ranking of associated economic burden and threat to life. Ideation sessions were held to identify pain points and find innovative solutions. Additionally, inputs were taken from co-creation sessions at universities worldwide. The optimization and design of infographics and care plan was done based on the key information gathered-facts and figures, stakeholders, pain points and solutions. Finally, validation was obtained from clinical and technology experts globally. A disease pathway framework was created to develop pathways for 22 global diseases. Over 1,500 pain points were collected and about 1,900 ideas were proposed. The approach was applied to optimize its application to 30 product and portfolio definition projects over 2 years at Siemens Healthineers, as well as co-creation programs with universities and hospitals. The disease pathway framework provides a unique foundation for extensive collaboration among multiple stakeholders, through information sharing and delivering high-quality solutions based on the identified problems and customer needs.

Corrigendum: A Disease Pathway Framework for Pain Point Identification and Elaboration of Product Requirements Across Patient Care Plan Using Innovation Think Tank Global Infrastructure.

[This corrects the article DOI: 10.3389/fpubh.2022.862384.].

A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics.

Alzheimer's disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer's disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer's disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.

Understanding Inequalities in the Uptake of Supportive Care to Improve Practices in the Cancer Care Continuum.

(1) Background: While inequalities in the prevalence of cancer, access to care, and survival have been well documented, less research has focused on inequalities in the uptake of supportive oncology care. Given its contribution to improving the quality of life of people affected by cancer, access to such care is a major public health issue. The present study focuses on the access and uptake of those supportive oncology care services. (2) Methods: This study is based on qualitative research methodology, using a thematic analysis tree on NVivo© analysis software. First, an exploratory survey was conducted with users of oncology services, and professionals from these services and supportive oncology care. Then, individual interviews were conducted in June 2022 among people who are currently being treated or have been treated for cancer. (3) Results: The experiences of the 33 respondents revealed that significant variations in the uptake of supportive oncology care are underpinned by identifiable disparities in their healthcare pathways: in their assimilation of information, difficulties in accessing oncology care, personal reluctance and motivations, perceived needs and benefits, and use of other medicines. (4) Conclusion: This study aims to gain some insight into disparities in the uptake of supportive care in the Centre-Val de Loire region (France). Thus, it provides a better understanding of the complex ways in which these inequalities in supportive oncology care uptake are constructed.

Protein-protein interactions: Methods, databases, and applications in virus-host study.

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases.

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with structural annotations of protein-protein interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

Prioritizing disease biomarkers using functional module based network analysis: A multilayer consensus driven scheme.

Many complex diseases occur due to genetic factors. A perturbation in the pathway of gene interactions leads to such disorders. Even though a group of genes is responsible, a few significant genes act as a biomarker for disease, perturbing the healthy network. Identifying such marker genes or a set of genes that play a pivotal role in diseases helps drug prioritization. We propose a scheme for finding potential bio-markers using a multi-layer consensus-driven approach. We reconstruct a functional module guided disease sub-network, followed by a multi-step consensus of network inference methods and shared ontological terms. We perform centrality analysis on the sub-networks under consideration and report hub genes as potentially key players in the target disease. To establish our scheme's effectiveness, we use Alzheimer's Disease (AD) and Breast Cancer as candidate diseases for experimentation. We evaluate the significance of prioritized genes based on reported evidence. We observe that BRCA1, BRCA2, and PTEN are the essential genes for Breast Cancer, whereas MAPK1, APP, and CASP7 are the essential genes playing an important role during AD.

Transcriptional Profiling and Biological Pathway(s) Analysis of Type 2 Diabetes Mellitus in a Pakistani Population.

The epidemic of type 2 diabetes mellitus (T2DM) is an important global health concern. Our earlier epidemiological investigation in Pakistan prompted us to conduct a molecular investigation to decipher the differential genetic pathways of this health condition in relation to non-diabetic controls. Our microarray studies of global gene expression were conducted on the Affymetrix platform using Human Genome U133 Plus 2.0 Array along with Ingenuity Pathway Analysis (IPA) to associate the affected genes with their canonical pathways. High-throughput qRT-PCR TaqMan Low Density Array (TLDA) was performed to validate the selected differentially expressed genes of our interest, viz., ARNT, LEPR, MYC, RRAD, CYP2D6, TP53, APOC1, APOC2, CYP1B1, SLC2A13, and SLC33A1 using a small population validation sample (n = 15 cases and their corresponding matched controls). Overall, our small pilot study revealed a discrete gene expression profile in cases compared to controls. The disease pathways included: Insulin Receptor Signaling, Type II Diabetes Mellitus Signaling, Apoptosis Signaling, Aryl Hydrocarbon Receptor Signaling, p53 Signaling, Mitochondrial Dysfunction, Chronic Myeloid Leukemia Signaling, Parkinson's Signaling, Molecular Mechanism of Cancer, and Cell Cycle G1/S Checkpoint Regulation, GABA Receptor Signaling, Neuroinflammation Signaling Pathway, Dopamine Receptor Signaling, Sirtuin Signaling Pathway, Oxidative Phosphorylation, LXR/RXR Activation, and Mitochondrial Dysfunction, strongly consistent with the evidence from epidemiological studies. These gene fingerprints could lead to the development of biomarkers for the identification of subgroups at high risk for future disease well ahead of time, before the actual disease becomes visible.

HyperTraPS: Inferring Probabilistic Patterns of Trait Acquisition in Evolutionary and Disease Progression Pathways.

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalizable statistical platform to infer the dynamic pathways by which many, potentially interacting, traits are acquired or lost over time. We use HyperTraPS (hypercubic transition path sampling) to efficiently learn progression pathways from cross-sectional, longitudinal, or phylogenetically linked data, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. This Bayesian approach allows inclusion of prior knowledge, quantifies uncertainty in pathway structure, and allows predictions, such as which symptom a patient will acquire next. We provide visualization tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.

Integration of curated and high-throughput screening data to elucidate environmental influences on disease pathways.

Addressing the complex relationship between public health and environmental exposure requires multiple types and sources of data. An important source of chemical data derives from high-throughput screening (HTS) efforts, such as the Tox21/ToxCast program, which aim to identify chemical hazard using primarily in vitro assays to probe toxicity. While most of these assays target specific genes, assessing the disease-relevance of these assays remains challenging. Integration with additional data sets may help to resolve these questions by providing broader context for individual assay results. The Comparative Toxicogenomics Database (CTD), a publicly available database that builds networks of chemical, gene, and disease information from manually curated literature sources, offers a promising solution for contextual integration with HTS data. Here, we tested the value of integrating data across Tox21/ToxCast and CTD by linking elements common to both databases (i.e., assays, genes, and chemicals). Using polymarcine and Parkinson's disease as a case study, we found that their union significantly increased chemical-gene associations and disease-pathway coverage. Integration also enabled new disease associations to be made with HTS assays, expanding coverage of chemical-gene data associated with diseases. We demonstrate how integration enables development of predictive adverse outcome pathways using 4-nonylphenol, branched as an example. Thus, we demonstrate enhancements to each data source through database integration, including scenarios where HTS data can efficiently probe chemical space that may be understudied in the literature, as well as how CTD can add biological context to those results.

Attributing diseases to multiple pathways: a causal-pie modeling approach.

Characterizing the relations between exposures and diseases is the central tenet of epidemiology. Researchers may want to evaluate exposure-disease causation by assessing whether the disease under concern is induced by the various exposures - the so-called "attribution". In this paper, the authors propose a method to attribute diseases to multiple pathways based on the causal-pie model. The method can also be used to evaluate the potential impact of an intervention strategy and to allocate responsibility in tort-law liability issues.

GLADIATOR: a global approach for elucidating disease modules.

BACKGROUND: Understanding the genetic basis of disease is an important challenge in biology and medicine. The observation that disease-related proteins often interact with one another has motivated numerous network-based approaches for deciphering disease mechanisms. In particular, protein-protein interaction networks were successfully used to illuminate disease modules, i.e., interacting proteins working in concert to drive a disease. The identification of these modules can further our understanding of disease mechanisms. METHODS: We devised a global method for the prediction of multiple disease modules simultaneously named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction). GLADIATOR relies on a gold-standard disease phenotypic similarity to obtain a pan-disease view of the underlying modules. To traverse the search space of potential disease modules, we applied a simulated annealing algorithm aimed at maximizing the correlation between module similarity and the gold-standard phenotypic similarity. Importantly, this optimization is employed over hundreds of diseases simultaneously. RESULTS: GLADIATOR's predicted modules highly agree with current knowledge about disease-related proteins. Furthermore, the modules exhibit high coherence with respect to functional annotations and are highly enriched with known curated pathways, outperforming previous methods. Examination of the predicted proteins shared by similar diseases demonstrates the diverse role of these proteins in mediating related processes across similar diseases. Last, we provide a detailed analysis of the suggested molecular mechanism predicted by GLADIATOR for hyperinsulinism, suggesting novel proteins involved in its pathology. CONCLUSIONS: GLADIATOR predicts disease modules by integrating knowledge of disease-related proteins and phenotypes across multiple diseases. The predicted modules are functionally coherent and are more in line with current biological knowledge compared to modules obtained using previous disease-centric methods. The source code for GLADIATOR can be downloaded from http://www.cs.tau.ac.il/~roded/GLADIATOR.zip .

Immunomodulation of inflammatory leukocyte markers during intravenous immunoglobulin treatment associated with clinical efficacy in chronic inflammatory demyelinating polyradiculoneuropathy.

OBJECTIVE: The objective of the study was to profile leukocyte markers modulated during intravenous immunoglobulin (IVIg) treatment, and to identify markers and immune pathways associated with clinical efficacy of IVIg for chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) with potential for monitoring treatment efficacy. METHODS: Response to IVIg treatment in newly diagnosed IVIg-naïve and established IVIg-experienced patients was assessed by changes in expression of inflammatory leukocyte markers by flow cytometry. The adjusted INCAT disability and Medical Research Council sum scores defined clinical response. RESULTS: Intravenous immunoglobulin modulated immunopathogenic pathways associated with inflammatory disease in CIDP. Leukocyte markers of clinical efficacy included reduced CD185+ follicular helper T cells, increased regulatory markers (CD23 and CD72) on B cells, and reduction in the circulating inflammatory CD16+ myeloid dendritic cell (mDC) population and concomitant increase in CD62L and CD195 defining a less inflammatory lymphoid homing mDC phenotype. A decline in inflammatory CD16+ dendritic cells was associated with clinical improvement or stability, and correlated with magnitude of improvement in neurological assessment scores, but did not predict relapse. IVIg also induced a nonspecific improvement in regulatory and reduced inflammatory markers not associated with clinical response. CONCLUSIONS: Clinically effective IVIg modulated inflammatory and regulatory pathways associated with ongoing control or resolution of CIDP disease. Some of these markers have potential for monitoring outcome.