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Annual Review of Pharmacology and... Jan 2024Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the... (Review)
Review
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
Topics: Humans; Artificial Intelligence; Genotype; Biomarkers; Phenotype; Drug-Related Side Effects and Adverse Reactions
PubMed: 37585662
DOI: 10.1146/annurev-pharmtox-032023-121106 -
Trends in Endocrinology and Metabolism:... Mar 2024The function and phenotype of macrophages are intimately linked with pathogen detection. On sensing pathogen-derived signals and molecules, macrophages undergo a... (Review)
Review
The function and phenotype of macrophages are intimately linked with pathogen detection. On sensing pathogen-derived signals and molecules, macrophages undergo a carefully orchestrated process of polarization to acquire pathogen-clearing properties. This phenotypic change must be adequately supported by metabolic reprogramming that is now known to support the acquisition of effector function, but also generates secondary metabolites with direct microbicidal activity. At the same time, bacteria themselves have adapted to both manipulate and take advantage of macrophage-specific metabolic adaptations. Here, we summarize the current knowledge on macrophage metabolism during infection, with a particular focus on understanding the 'arms race' between host immune cells and bacteria during immune responses.
Topics: Humans; Macrophages; Bacterial Infections; Phenotype
PubMed: 38040578
DOI: 10.1016/j.tem.2023.11.002 -
Biophysical Journal Nov 2023Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven...
Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.
Topics: Environment; Adaptation, Physiological; Selection, Genetic; Biological Evolution; Phenotype
PubMed: 37876159
DOI: 10.1016/j.bpj.2023.10.019 -
Genes Sep 2023Cardiomyopathies (CMPs) represent a significant healthcare burden and are a major cause of heart failure leading to premature death. Several CMPs are now recognized to... (Review)
Review
Cardiomyopathies (CMPs) represent a significant healthcare burden and are a major cause of heart failure leading to premature death. Several CMPs are now recognized to have a strong genetic basis, including arrhythmogenic cardiomyopathy (ACM), which predisposes patients to arrhythmic episodes. Variants in one of the five genes ( and ) encoding proteins of the desmosome are known to cause a subset of ACM, which we classify as desmosome-related ACM (dACM). Phenotypically, this disease may lead to sudden cardiac death in young athletes and, during late stages, is often accompanied by myocardial fibrofatty infiltrates. While the pathogenicity of the desmosome genes has been well established through animal studies and limited supplies of primary human cells, these systems have drawbacks that limit their utility and relevance to understanding human disease. Human induced pluripotent stem cells (hiPSCs) have emerged as a powerful tool for modeling ACM in vitro that can overcome these challenges, as they represent a reproducible and scalable source of cardiomyocytes (CMs) that recapitulate patient phenotypes. In this review, we provide an overview of dACM, summarize findings in other model systems linking desmosome proteins with this disease, and provide an up-to-date summary of the work that has been conducted in hiPSC-cardiomyocyte (hiPSC-CM) models of dACM. In the context of the hiPSC-CM model system, we highlight novel findings that have contributed to our understanding of disease and enumerate the limitations, prospects, and directions for research to consider towards future progress.
Topics: Humans; Induced Pluripotent Stem Cells; Myocytes, Cardiac; Cardiomyopathies; Phenotype
PubMed: 37895213
DOI: 10.3390/genes14101864 -
Frontiers in Immunology 2023Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally... (Review)
Review
Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients' prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype.
Topics: Humans; Tumor-Associated Macrophages; Macrophages; Neoplasms; Biomarkers; Phenotype
PubMed: 37503340
DOI: 10.3389/fimmu.2023.1193235 -
The ISME Journal Sep 2023Experimental evolution in a laboratory helps researchers to understand the genetic and phenotypic background of adaptation under a particular condition. Simultaneously,...
Experimental evolution in a laboratory helps researchers to understand the genetic and phenotypic background of adaptation under a particular condition. Simultaneously, the simplified environment that represents certain aspects of a complex natural niche permits the dissection of relevant parameters behind the selection, including temperature, oxygen availability, nutrients, and biotic factors. The presence of other microorganisms or a host has a major influence on microbial evolution that often differs from the adaptation paths observed in response to abiotic conditions. In the current issue of the ISME Journal, Cosetta and colleagues reveal how cross-kingdom interaction representing the cheese microbiome succession promotes distinct evolution of the food- and animal-associated bacterium, . The authors also identified a global regulator-dependent adaption that leads to evolved derivatives exhibiting reduced pigment production and colony morphologies in addition to altered differentiation phenotypes that potentially contribute to increased fitness.
Topics: Staphylococcus; Biological Evolution; Phenotype
PubMed: 37524911
DOI: 10.1038/s41396-023-01479-w -
International Journal of Chronic... 2023Physical activity (PA) and sedentary behavior (SB) have attracted attention in chronic obstructive pulmonary disease (COPD), and there have been efforts to evaluate PA...
INTRODUCTION
Physical activity (PA) and sedentary behavior (SB) have attracted attention in chronic obstructive pulmonary disease (COPD), and there have been efforts to evaluate PA and SB separately. The factors associated with the characteristics of the four activity phenotypes defined by the durations of PA and SB are largely unknown. The aim of this study was to clarify the factors that could differentiate each activity phenotype.
MATERIALS AND METHODS
Study subjects were outpatients with stable COPD who were ≥40 years of age. We investigated the influence of 26 different factors on the activity phenotypes of COPD and extracted the factors that showed significant differences among the four activity phenotypes.
RESULTS
Two hundred sixteen patients were included in the analysis. Exercise capacity and dyspnea were determinants that distinguished the low PA groups from the high PA groups. The pulmonary function and desaturation during exercise were factors that distinguished the high PA with low SB group from the low PA with high SB group. BMI, grip strength, upper arm circumference and HbA1c were higher in the low PA and low SB group than in the low PA and high SB group.
CONCLUSION
These factors could be the determinants discriminating activity phenotypes of patients with COPD.
Topics: Humans; Pulmonary Disease, Chronic Obstructive; Dyspnea; Exercise; Hand Strength; Phenotype
PubMed: 37671143
DOI: 10.2147/COPD.S421755 -
Journal of Endocrinological... Jan 2024To evaluate the genotypic and phenotypic relationship in a large cohort of OI patients and to compare the differences between eastern and western OI cohorts.
PURPOSE
To evaluate the genotypic and phenotypic relationship in a large cohort of OI patients and to compare the differences between eastern and western OI cohorts.
METHODS
A total of 671 OI patients were included. Pathogenic mutations were identified, phenotypic information was collected, and relationships between genotypes and phenotypes were analyzed. Literature about western OI cohorts was searched, and differences were compared between eastern and western OI cohorts.
RESULTS
A total of 560 OI patients were identified as carrying OI pathogenic mutations, and the positive detection rate of disease-causing gene mutations was 83.5%. Mutations in 15 OI candidate genes were identified, with COL1A1 (n = 308, 55%) and COL1A2 (n = 164, 29%) being the most common mutations, and SERPINF1 and WNT1 being the most common biallelic variants. Of the 414 probands, 48.8, 16.9, 29.2 and 5.1% had OI types I, III, IV and V, respectively. Peripheral fracture was the most common phenotype (96.6%), and femurs (34.7%) were most commonly affected. Vertebral compression fracture was observed in 43.5% of OI patients. Biallelic or COL1A2 mutation led to more bone deformities and poorer mobility than COL1A1 mutation (all P < 0.05). Glycine substitution of COL1A1 or COL1A2 or biallelic variants led to more severe phenotypes than haploinsufficiency of collagen type I α chains, which induced the mildest phenotypes. Although the gene mutation spectrum varied among countries, the fracture incidence was similar between eastern and western OI cohorts.
CONCLUSION
The findings are valuable for accurate diagnosis and treatment of OI, mechanism exploration and prognosis judgment. Genetic profiles of OI may vary among races, but the mechanism needs to be explored.
Topics: Humans; Osteogenesis Imperfecta; Collagen Type I, alpha 1 Chain; Fractures, Compression; Spinal Fractures; Collagen Type I; Genotype; Phenotype; Mutation; Bone Diseases, Metabolic
PubMed: 37270749
DOI: 10.1007/s40618-023-02123-2 -
Genes Dec 2023Glutaric aciduria type 1 (GA-1) is a rare but treatable autosomal-recessive neurometabolic disorder of lysin metabolism caused by biallelic pathogenic variants in...
Glutaric aciduria type 1 (GA-1) is a rare but treatable autosomal-recessive neurometabolic disorder of lysin metabolism caused by biallelic pathogenic variants in glutaryl-CoA dehydrogenase gene () that lead to deficiency of GCDH protein. Without treatment, this enzyme defect causes a neurological phenotype characterized by movement disorder and cognitive impairment. Based on a comprehensive literature search, we established a large dataset of variants using the Leiden Open Variation Database (LOVD) to summarize the known genotypes and the clinical and biochemical phenotypes associated with GA-1. With these data, we developed a -specific variation classification framework based on American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines. We used this framework to reclassify published variants and to describe their geographic distribution, both of which have practical implications for the molecular genetic diagnosis of GA-1. The freely available -specific LOVD dataset provides a basis for diagnostic laboratories and researchers to further optimize their knowledge and molecular diagnosis of this rare disease.
Topics: Humans; Brain Diseases, Metabolic; Glutaryl-CoA Dehydrogenase; Phenotype; Genotype
PubMed: 38137040
DOI: 10.3390/genes14122218 -
Rheumatology (Oxford, England) Dec 2023Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high...
OBJECTIVES
Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high heterogeneity. In this study, we aimed to identify different phenotypes in knee osteoarthritis (KOA) patients with joint effusion based on their metabolic and inflammatory profiles.
METHODS
A non-supervised strategy based on statistical and machine learning methods was applied to 45 parameters measured on 168 female KOA patients with persistent joint effusion, consecutively recruited at our hospital after a monographic OA outpatient visit. Data comprised anthropometric and metabolic factors and a panel of systemic and local inflammatory markers. The resulting clusters were compared regarding their clinical, radiographic and ultrasound severity at baseline and their radiographic progression at two years.
RESULTS
Our analyses identified four KOA inflammatory phenotypes (KOIP): a group characterized by metabolic syndrome, probably driven by body fat and obesity, and by high local and systemic inflammation (KOIP-1); a metabolically healthy phenotype with mild overall inflammation (KOIP-2); a non-metabolic phenotype with high inflammation levels (KOIP-3); and a metabolic phenotype with low inflammation and cardiovascular risk factors not associated with obesity (KOIP-4). Of interest, these groups exhibited differences regarding pain, functional disability and radiographic progression, pointing to a clinical relevance of the uncovered phenotypes.
CONCLUSION
Our results support the existence of different KOA phenotypes with clinical relevance and differing pathways regarding their pathophysiology and disease evolution, which entails implications in patients' stratification, treatment tailoring and the search of novel and personalized therapies.
Topics: Humans; Female; Osteoarthritis, Knee; Clinical Relevance; Phenotype; Obesity; Inflammation; Knee Joint
PubMed: 36944271
DOI: 10.1093/rheumatology/kead135