Asta Literature Retrieval: Pathophysiology and clinical mechanisms of Bloom syndrome. Core disease mechanisms, molecular and cellular pathways,...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] 18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

• Authors: E. Nemutlu, Song Zhang, N. Juranic, A. Terzic, S. Macura et al.
• Year: 2012
• Venue: Croatian Medical Journal
• URL: https://www.semanticscholar.org/paper/880f053c7f060db4b990e447d0a22c4b69372ddb
• DOI: 10.3325/cmj.2012.53.529
• PMID: 23275318
• PMCID: 3541579
• Citations: 28
• Summary: The potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic is described and discussed briefly.
• Evidence snippets:
• Snippet 1 (score: 0.399) > Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This "molecular reductionist" paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites (at cellular, tissue, and organismal levels). Therefore, an integrated "omics" approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects. > Recent advent of large-scale metabolomics and fluxomic (metabolite dynamics and metabolic flux analysis) completed the "omics revolution" (Figure 1), where genomics, transcriptomics, proteomics, metabolomics, and fluxomics all together complement phenotype determination of living organism. Such integrated "omics" cascades provide a framework for advances in system and network biology, integrative physiology, and system medicine as well as system pharmacology and regenerative medicine. Noteworthy is the "reverse omic" approach or "metabolomicsinformed pharmacogenomics, " where discovery of specific metabolite changes have led to discovery of genetic alterations (2). Therefore, bringing new "omics" technologies to clinical practice will improve disease diagnostics and treatment by targeting drugs and procedures for each unique transcriptomic and metabolomic profiles.

[2] Novel variants in KAT6B spectrum of disorders expand our knowledge of clinical manifestations and molecular mechanisms

• Authors: M. Yabumoto, Jessica Kianmahd, Meghna Singh, Maria F. Palafox, Angela Wei et al.
• Year: 2021
• Venue: Molecular Genetics & Genomic Medicine
• URL: https://www.semanticscholar.org/paper/3a47a1b1208ba7420900b090d3d7d712ed391719
• DOI: 10.1002/mgg3.1809
• PMID: 34519438
• PMCID: 8580094
• Citations: 12
• Influential citations: 2
• Summary: A range of features previously described for KAT6B‐related syndromes are identified, including concern for keratoconus, sensitivity to light or noise, recurring infections, and fractures in greater numbers than previously reported.
• Evidence snippets:
• Snippet 1 (score: 0.367) > Finally, as gene-centric models of disease have started to take hold, understanding the underlying functional mechanisms that are affected can help us elucidate the effect on molecular and cellular phenotypes that are regulated by KAT6B (Klein et al., 2019;Sheikh et al., 2012). We developed a model of KAT6B truncating variants in a human cell line to explore how these variants result in differential regulation of key transcripts. These types of approaches have been performed in a high throughput manner for tumor suppressor genes like BRCA1 (Findlay et al., 2018) and TP53 (Kotler et al., 2018) and can help identify key pathways that are dysregulated by KAT6B-related disorders and could be future targets for translational research. > Here, we analyze 20 clinical cases representing a KAT6B-related clinical spectrum across three domains: their genotype, phenotype, and experience with genetic counseling resources. Furthermore, we developed an in vitro model of KAT6B mutations using CRISPR technology to explore the effect of protein truncation on global transcriptional regulation. Here we demonstrate that the genes that drive core clinical phenotypes are enriched in our in vitro model system. Together, we show that our clinical observations parallel the transcriptional processes in our cell model systems which allow for a further understanding of the mechanisms underlying the KAT6Brelated clinical spectrum.

[3] Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

• Authors: C. Çubuk, F. Can, M. Peña-Chilet, J. Dopazo
• Year: 2020
• Venue: Cells
• URL: https://www.semanticscholar.org/paper/e40f7a3b8f72ba01374ba00fbf308a47a3fa5dd4
• DOI: 10.3390/cells9071579
• PMID: 32610626
• PMCID: 7408716
• Citations: 9
• Summary: Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-s...
• Evidence snippets:
• Snippet 1 (score: 0.363) > Therefore, a proper interpretation of the effect that differences in gene expression have over phenotypes, such as drug response or disease progression, involves understanding the mechanisms of the disease or the mode of action of drugs, which can be interpreted through mechanistic models of cell signaling [12] or cell metabolism [13]. Mechanistic models have helped to understand the disease mechanisms behind different cancers [14,15], including neuroblastoma [16,17], breast cancer [18], rare diseases [19], complex diseases [20], the mechanisms of action of drugs [21,22], and other biologically interesting scenarios such as the molecular mechanisms that explain how stress-induced activation of brown adipose tissue prevents obesity [23] or the molecular mechanisms of death and the post-mortem ischemia of a tissue [24]. Among the few available proposals of mechanistic modeling algorithms that model different aspects of signaling pathway activity, Hipathia has demonstrated having superior sensitivity and specificity [12]. > Here, we propose the use of mechanistic models [13,14] of signaling activity related with cancer hallmarks [25], other cancer-related signaling pathways, and some extra relevant cellular functions to understand the functional consequences of the gender bias in gene expression. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [13,14]. An interesting property of mechanistic models is that they can be used not only to understand molecular mechanisms of disease or of drug action but also to predict the potential consequences of gene perturbations over the circuit activity in a given condition [26]. Actually, in a recent work, our group has successfully predicted therapeutic targets in cancer cell lines with a precision over 60% [15]. Therefore, we will use this mechanistic framework to understand what is the molecular basis of the different effects of cancer drugs by directly simulating their effect in the patients. This approach has recently been used by us to understand the generation of resistances in cancer at the single cell level in glioblastoma [27].

[4] Future research trends in understanding the mechanisms underlying allergic diseases for improved patient care

• Authors: H. Breiteneder, Z. Diamant, T. Eiwegger, W. Fokkens, C. Traidl‐Hoffmann et al.
• Year: 2019
• Venue: Allergy
• URL: https://www.semanticscholar.org/paper/e19b0755c4f4903f68377333676edebf9bd73c89
• DOI: 10.1111/all.13851
• PMID: 31056763
• PMCID: 6973012
• Citations: 90
• Influential citations: 3
• Summary: Recent developments in research and patient care and future trends in the discipline are reviewed and topics on food allergy, biologics, small molecules, and novel therapeutic concepts in allergen‐specific immunotherapy for airway disease are highlighted.
• Evidence snippets:
• Snippet 1 (score: 0.360) > The past decades have witnessed extensive progress in unraveling cellular and molecular mechanisms of immune regulation in asthma, allergic diseases, organ transplantation, autoimmune diseases, tumor biology, and chronic infections. 1,2 Consequently, a better understanding of the functions, the reciprocal regulation, and the counterbalance of subsets of immune and inflammatory cells but also structural cells-for example, epithelial and vascular cells, airway smooth muscle cells, neuroendocrine system-that interact via various intercellular messengers will indicate avenues for immune interventions and novel treatment modalities of allergic diseases and immunological disorders. It is generally expected that drug development in the next decades will show a significant shift from chemicals to biologicals. > After more than 20 years without any breakthrough drug becoming available for patients, several disciplines including allergology are now experiencing extraordinary times with the recent licensing of several major biological drugs and novel allergen-specific immunotherapy (AIT) vaccines. Several biological modifiers of the immune response targeting intracellular messengers or their receptors have been developed to date. [3][4][5][6][7][8] In addition, a number of promising small molecule drugs and vaccines are in the development pipeline. [9][10][11] This new era is now calling for the development of biomarkers and phenoand endotyping of diseases for customized patient care, which is termed stratified medicine, precision medicine, or personalized medicine. 4 Distinguishing phenotypes of a complex disease covers the observable clinically relevant properties of the disease but does not show a direct relationship to disease etiology and pathophysiology. In a complex condition, such as asthma, different pathogenetic mechanisms can induce similar clinical manifestations; however, they may require different treatment approaches. 12,13 These pathophysiological mechanisms underlying disease subgroups are addressed by the term "endotype." [12][13][14] Classification of complex diseases based on the concept of endotypes provides advantages for epidemiological, genetic, and drug-related studies. Accurate endotyping by using reliable biomarkers reflects the natural history of the disease and aims to predict the response to (targeted) treatments. 15 Recent studies have focused on better understanding

[5] The evolving burden of asthma and contemporary advances in management: Implications for clinical practice in Southern Africa

• Authors: A. Kiboneka
• Year: 2020
• Venue: Unknown venue
• URL: https://www.semanticscholar.org/paper/0ba536bc7dbea898dcaabe247c92c7897c7e059c
• DOI: 10.30574/wjarr.2020.8.3.0315
• Citations: 1
• Summary: The development of novel asthma phenotyping & endo typing plus better classification of patients using machine learning and big data have markedly improved asthma treatment outcomes in both children and Adults, and several research groups have developed cluster analyses of phenotypes in severe asthma.
• Evidence snippets:
• Snippet 1 (score: 0.360) > Research Program (SARP) I and II cohorts to study mechanisms differentiating severe from non-severe asthma. SARP investigators characterized severe asthma as a heterogeneous syndrome with diverse molecular, biochemical, and cellular inflammatory features and structure-function abnormalities. > Adults and children with severe asthma were further categorized by unbiased statistical methods into clusters based on distinguishing clinical features. These studies have not been done in Sub-Sahara Africa. Research performed over the past one to two decades has sought to better understand the heterogeneous clinical nature of asthma. Whereas older attempts at phenotyping asthma emphasized the duality of allergic vs. non-allergic asthma, more recent non-biased analyses have attempted to cluster patients by a multitude of possible features, including age of onset, atopy, severity of airways obstruction, and requirement for medication. Examples of these phenotypes include early-onset mild allergic asthma, later-onset asthma associated with obesity, and severe non-atopic asthma with frequent exacerbations. The elucidation of asthma phenotypes has been further refined by including information regarding pathophysiologic mechanisms present in different groups. These groups, called endo-types, include examples such as aspirin-exacerbated respiratory disease and allergic bronchopulmonary mycosis. > A phenotype covers the clinically relevant properties of the disease, but does not show the direct relationship to disease etiology and pathophysiology. Different patho-genetic mechanisms might cause similar asthma symptoms and might be operant in a certain phenotype. These putative mechanisms are addressed by the term 'endotype'. > Classification of asthma based on endo-types provides advantages for epidemiological, genetic, and drug-related studies. A successful definition of endo-types should link key pathogenic mechanisms with the asthma phenotype. Thus, the identification of corresponding molecular biomarkers for individual pathogenic-mechanism underlying phenotypes or subgroups within a phenotype is important. > The term asthma encompasses a disease spectrum with mild to very severe disease phenotypes whose traditional common characteristic is reversible airflow limitation. Unlike milder disease, severe asthma is poorly controlled by the current standard of care.

[6] The Diabetes Syndrome – A Collection of Conditions with Common, Interrelated Pathophysiologic Mechanisms

• Authors: A. W. Rachfal, S. Grant, S. Schwartz
• Year: 2021
• Venue: International Journal of General Medicine
• URL: https://www.semanticscholar.org/paper/4c088a6a8b613c15e817f7491d24022497b7f5c4
• DOI: 10.2147/IJGM.S305156
• PMID: 33776471
• PMCID: 7987256
• Citations: 6
• Summary: The “Diabetes Syndrome”, an overarching group of interrelated conditions linked by these overlapping mechanisms, can be viewed as a conceptual framework that can facilitate understanding of the inter-relationships of superficially disparate conditions.
• Evidence snippets:
• Snippet 1 (score: 0.359) > Although many pathways lead to hyperglycemia in diabetes -the so-called "Egregious Eleven" (Listed in Table 1) -β-cell dysfunction is the core defect. 1,2 Four basic pathophysiologic mechanisms damage the β-cell, namely, genes and epigenetic changes, inflammation, an abnormal environment [especially fuel excess], and insulin resistance (IR). 1,2 2][3] The interplay between these pathophysiologic mechanisms influences the specific risk of development and progression of complications in an individual patient. [6][7][8][9][10][11][12][13][14][15] In clinical practice we often encounter these common diseases, frequently within one individual patient and they are treated as independent conditions. However, we believe their epidemiologic associations is, in part, due to the same underlying pathophysiologies driving β-cell damage and diabetic complications. That is, the same pathophysiologic mechanisms that damage the β-cell and promote diabetesspecific complications also have key roles in the pathogenesis of these diseases. ][9][10][11][12][13][14][15] However, we propose these connections go beyond mere epidemiologic links due to overlapping pathophysiology. In fact, these conditions occur together in enough frequency and have common overlapping pathophysiologic drivers that we have created a conceptual framework called "The Diabetes Syndrome". The name is inspired by the Greek meaning of syndromē (sun-[together] + dramein [to run]) as the conditions, indeed, run together (Figure 1). This article will describe the shared pathophysiologic and etiologic factors across these prevalent and related diseases within the Diabetes Syndrome conceptual framework discussed within the context of the 4 basic pathophysiologic mechanisms -genes and epigenetic changes, abnormal environment, inflammation, and IR -with a focus on commonalities between these diseases and DM. In brief, genetics can mediate susceptibility to damage from abnormal external and internal environmental factors, including inflammation and IR. All these mechanisms can promote epigenetic changes.

[7] Transcriptional profiling of Hutchinson-Gilford progeria patients identifies primary target pathways of progerin

• Authors: Sandra Vidak, Sohyoung Kim, Tom Misteli
• Year: 2026
• Venue: Nucleus
• URL: https://www.semanticscholar.org/paper/4bd99b0875508364d8672b6da5a50d024d485a53
• DOI: 10.1080/19491034.2025.2611484
• PMID: 41489464
• PMCID: 12773485
• Summary: To probe the clinical relevance of previously implicated cellular pathways and to address the extent of gene expression heterogeneity between patients, transcriptomic analysis of a comprehensive set of HGPS patients finds misexpression of several cellular pathways, including multiple signaling pathways, the UPR and mesodermal cell fate specification.
• Evidence snippets:
• Snippet 1 (score: 0.357) > Oxidative stress represents another key pathogenic mechanism in HGPS, as impaired NRF2 activity or increased reactive oxygen species (ROS) levels are sufficient to recapitulate HGPSassociated phenotypes [17,32,60]. Collectively, these findings underscore the multifactorial nature of HGPS pathogenesis, implicating interconnected signaling cascades involved in inflammation, oxidative stress, proteostasis, and vascular remodeling. Reassuringly, our findings indicate that many of the major pathways that have been described to contribute to HGPS phenotypes in mouse and cellular disease models are also misregulated in progeria patients, and targeting these pathways may provide therapeutic avenues to mitigate disease severity and improve outcomes in HGPS. > Although individuals with HGPS typically exhibit a characteristic set of clinical features, such as craniofacial abnormalities, growth retardation, and cardiovascular complications, there is notable variability in the age of onset, severity, and progression of symptoms between patients [7,9]. At the cellular level, HGPS is associated with several hallmark abnormalities, including nuclear envelope defects, decreased expression of several nuclear proteins and epigenetic marks, mitochondrial dysfunction, and increased cellular senescence [1,11,30,31,61]. These cellular phenotypes also exhibit considerable variation between patients, possibly contributing to differences in clinical outcomes. Our results indicate that even though some degree of transcriptional heterogeneity between the individual patients exists, the majority of patients exhibit misregulation of a set of shared pathways, suggesting that these pathways are universal driver mechanisms in HGPS. Further work is needed to understand the molecular and genetic factors that underlie inter-individual variability in disease expression and progression. > A limitation of pathway analysis of HGPS patient samples is to distinguish the pathways which are directly targeted by the disease-causing progerin protein and the emergence of adaptive secondary response pathways during progression of the disease in patients during their lifetime. The same caveat applies to the use of cell-based models used in the study of HGPS disease mechanisms.

[8] Clinical metabolomics in type 2 diabetes mellitus: from pathogenesis to biomarkers

• Authors: Chuanxin Liu, Hetao Chen, Yujin Ma, Lei Zhang, Lulu Chen et al.
• Year: 2025
• Venue: Frontiers in Endocrinology
• URL: https://www.semanticscholar.org/paper/36f8d26a208b7b96763df2e9aa3211e440031c0e
• DOI: 10.3389/fendo.2025.1501305
• PMID: 40070584
• PMCID: 11893406
• Citations: 11
• Summary: The results facilitate understanding the pathophysiology and mechanism of type 2 diabetes mellitus and supports research in accurate diagnosis, risk prediction, curative effect, distinct stages, and prognosis judgment of T2DM.
• Evidence snippets:
• Snippet 1 (score: 0.356) > The metabolome is sensitive to a variety of genetic and environmental stimuli and susceptible to genetic, environmental, and gut microbiome pressures, so subtle differences between individuals can lead to large perturbations in metabolite concentrations and fluxes (15, 24). At present, cystatin C has become an ideal endogenous marker for evaluating glomerular filtration function because it is not affected by sex, age or muscle mass (25). In addition, more and more evidence shows that serum CysC is involved in the pathological process of vascular remodeling and neovascularization, which is closely related to the occurrence and development of diabetic microangiopathy (26). > Eighty-four papers were included in this review and obtained through database searches, namely, PubMed, Cochrane Library, China national knowledge internet(CNKI), General Purpose, and VIP Database. The keywords for the searches were "metabolomics" and "type 2 diabetes mellitus" and its complications. The papers were incorporated by reading and summarizing the literature according to the classification standards (27). The profound analysis of clinical differential metabolites identified in type 2 diabetes and its complications were conducted concerning composition, frequency of category, sample type, and pathways to explore the pathological mechanism of type 2 diabetes and its complications to provide a systematic basis for clinical diagnosis, risk stratification, comprehending disease progression, prognosis assessment, and drug efficacy. Our goal is to apply metabolomics to clinical diagnostic biomarkers, metabolic mechanisms, and prognostic observations, and early diagnosis can be made through metabolites to avoid progression to more serious complications.

[9] A Metabolic Pattern in Healthy Subjects Given a Single Dose of Metformin: A Metabolomics Approach

• Authors: Lina A. Dahabiyeh, M. Mujammami, T. Arafat, H. Benabdelkamel, A. Alfadda et al.
• Year: 2021
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/072dece85121c66abd1a380c68a8ff465654eeea
• DOI: 10.3389/fphar.2021.705932
• PMID: 34335266
• PMCID: 8319764
• Citations: 32
• Influential citations: 1
• Summary: The distinctive metabolic pattern linked to metformin administration can be used as a metabolic signature to predict the potential effect and mechanism of actions of new chemical entities during drug development.
• Evidence snippets:
• Snippet 1 (score: 0.354) > Several disordered and complications are controlled and improved by metformin, including; metabolic and reproductive abnormalities of polycystic ovary syndrome (PCOS), cardiovascular complications associated with diabetes, cancer prognosis, and neurodegenerative diseases (Viollet et al., 2012;Rotermund et al., 2018;Foretz et al., 2019). Additionally, clinical studies have shown that metformin has beneficial effects on systemic inflammatory markers (Cameron et al., 2016) and weight loss in insulin-sensitive and insulin-resistant overweight and obese patients (Seifarth et al., 2013). > The pleiotropic properties of metformin and its numerous therapeutic areas suggest that various underlying mechanisms and metabolic pathways could be involved. Despite being introduced into the market for over 60 years, the mechanism of action of metformin remains partially explored and understood (Viollet et al., 2012;Foretz et al., 2014;Foretz et al., 2019). This urges the need for new and considerable efforts to understand better the cellular and molecular mechanisms of action of metformin. > Metabolomics is the comprehensive analysis of a set of small molecules (i.e., amino acids, lipids, and carbohydrates), referred to as metabolites within cells, biofluids, tissues, or organisms. It is a powerful analytical tool that is widely used to provide rich mechanistic information on drugs, and aid in identifying potential biomarkers that can be used to monitor the efficacy of drug therapies (Balashova et al., 2018;Jacob et al., 2019;Dahabiyeh et al., 2021). Pharmacometabolomics is an effective approach to capture the metabolic signatures linked to drug exposure and, therefore, improves the understanding of their underlying mechanisms of actions and allows individual differences recognition and drug toxicity prediction (Adam et al., 2016;Malkawi et al., 2018;Dahabiyeh et al., 2020).

[10] A Lifelike guided journey through the pathophysiology of pulmonary hypertension—from measured metabolites to the mechanism of action of drugs

• Authors: Nathan Weinstein, Jørn Carlsen, S. Schulz, T. Stapleton, Hanne H. Henriksen et al.
• Year: 2023
• Venue: Frontiers in Cardiovascular Medicine
• URL: https://www.semanticscholar.org/paper/0b2dc837dea11add7e2f67f06acf6280ac96a019
• DOI: 10.1101/2023.11.21.23298782
• PMID: 38845688
• PMCID: 11153715
• Citations: 3
• Summary: The present study shows the power of mining knowledge graphs using Lifelike's diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions.
• Evidence snippets:
• Snippet 1 (score: 0.353) > Pulmonary hypertension (PH) is a pathological condition that affects approximately 1% of the population. The prognosis for many patients is poor, even after treatment. Our knowledge about the pathophysiological mechanisms that cause or are involved in the progression of PH is incomplete. Additionally, the mechanism of action of many drugs used to treat pulmonary hypertension, including sotatercept, requires elucidation. Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs. In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to Activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, interleukin 6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis. The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike's diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies

[11] Chemotherapy and Mechanisms of Resistance in Breast Cancer

• Authors: A. Oliveira, R. E. Santos, F. F. O. Rodrigues
• Year: 2012
• Venue: Unknown venue
• URL: https://www.semanticscholar.org/paper/502a86d8bcd7208be6f539fcceba631f82f25a7d
• DOI: 10.5772/24629
• Summary: The addition of adjuvant polychemotherapy in advanced breast cancer showed gain by controlling survival of micrometastases in patients with lymph nodes affected by cancer or not.
• Evidence snippets:
• Snippet 1 (score: 0.353) > The main reasons responsible for treatment failure in cancer patients are the mechanisms of drug resistance and emergence of disseminated disease (Terek et al, 2003). We identified two types of resistance most relevant to BC: primary resistance, which corresponds to the clinical situation where the patient showed no response to therapy, and secondary or acquired resistance in which, initially, there is an observed response and a subsequent failure of the treatment regimen (Kroger et al, 1999). Several mechanisms may cause the phenotype of multidrug resistance to chemotherapy drugs and are well characterized in in vitro experiments, including alterations in systemic pharmacology (pharmacokinetics and metabolism), extracellular mechanisms (tumor environment, multicellular drug resistance), and cellular mechanisms (cellular pharmacology, activation and inactivation of drugs, modification of specific targets and regulatory pathways of apoptosis) (Leonessa et al, 2003, Riddick et al, 2005. Identification of factors that affect cell metabolism, which are related to drug resistance, will enable the identification of which patients are at particular risk of treatment failure. Among the biochemical and molecular mechanisms of drug resistance, we stress: changes in the activity of topoisomerase II, alterations in the DNA repair mechanism, overexpression of P-glycoprotein; high intracellular concentrations of enzymes purification of cellular metabolism -among them enzymes the family of glutathione S-transferases (GSTs) and changes in the mechanisms of signaling via c-Jun N-terminal kinase 1 (JNK1) -and "apoptosis signal-regulating kinase (ASK1) required for activation of the" mitogenactivated protein (MAP kinases) in apoptosis and cellular restoration. These pathways are also mediated by proteins encoded by genes of GSTs (O'Brien, Tew, 1996;Burg, Mulder, 2002, L'Ecuyer et al, 2004). Different response rates to particular chemotherapy regimens, as observed in patient groups with the same biological characteristics and stage, suggest the existence of different mechanisms of drug resistance, probably induced by genetic alterations (Hayes, Pulford, 1995;O'Brien , Tew, 1996;Pakunlu et al, 2003). Among the mechanisms of purification of cellular metabolism involved in the

[12] Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data

• Authors: Kinza Rian, Marta R. Hidalgo, C. Çubuk, M. M. Falco, C. Loucera et al.
• Year: 2021
• Venue: Computational and Structural Biotechnology Journal
• URL: https://www.semanticscholar.org/paper/af786ba590cb9db62ff31318c785685ade68bfd9
• DOI: 10.1016/j.csbj.2021.05.022
• PMID: 34136096
• PMCID: 8170118
• Citations: 10
• Summary: This work presents the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effects of mutations in complex scenarios.
• Evidence snippets:
• Snippet 1 (score: 0.352) > Mechanistic models of signaling pathways provide a natural bridge from variations in genotype (at the scale of gene activity or integrity) to variations in phenotype (at the scale of cells, tissues or organisms) [1]. They are built over graphs that represent the biological knowledge of the complex functional relationships among proteins within the cell, as described in repositories such as KEGG [2], Reactome [3], WikiPathways [4], or other more specialized, such as Disease Maps [5]. Specifically, they provide a conceptual framework for the interpretation of gene expression or genomic variation data and their consequences over downstream processes and phenotypic responses, such as cell proliferation and death, which are particularly relevant for studying disease progression or drug response [6]. Mechanistic models have successfully been used to understand the disease mechanisms behind different cancers [7,8] (including neuroblastoma [9,10] and glioblastoma [11]) rare diseases [12,13], complex diseases such as diabetes [14] or obesity [15], the mechanisms of action of drugs [16] or gender-specific effects of drugs in cancer [17]. In addition to diseases, other scenarios have been studied, such as the molecular mechanisms of death and the post-mortem ischemia of a tissue [18] or the effects of nanoplastics on embryos and human induced pluripotent stem cells [19]. > One of the most important aspects of mechanistic models is that they convey the notion of causality and can, therefore, be used to predict the downstream consequences of perturbations of specific conditions [20]. Thus, the possibility of simulating the effect of a drug allowed a systematic in silico drug repurposing experiment in Fanconi Anemia [21] in which some of the drugs predicted were further validated [22]. Also recently, all the targeted drugs currently in clinical trials for testing treatment and prevention of COVID-19 [23] were predicted by means of a mechanistic model [24] of the COVID-19 disease map [25]. application of mechanistic modeling show how the simulation of drug inhibitions at single-cell level uncovers the molecular basis of the generation of resistance to

[13] Role of Transcriptomics in Precision Oncology

• Authors: Ruby Srivastava
• Year: 2024
• Venue: Reports of Radiotherapy and Oncology
• URL: https://www.semanticscholar.org/paper/0bd862558bbb7286336111d9dfd232b5f905d3d9
• DOI: 10.5812/rro-142195
• Citations: 4
• Summary: : Transcriptome profiling is one of the most widely used approaches in the field of multiomics research. It plays a crucial role in the prognostic, diagnostic, and predictive treatment of cancer patients. Novel next-generation sequencing (NGS) technologies permit the identification of cancer biomarkers, gene signatures, and their abnormal expression, affecting oncogenic and molecular targets and novel biomarkers for cancer therapies. Multiomics studies have changed the overall understanding o...
• Evidence snippets:
• Snippet 1 (score: 0.351) > : Transcriptome profiling is one of the most widely used approaches in the field of multiomics research. It plays a crucial role in the prognostic, diagnostic, and predictive treatment of cancer patients. Novel next-generation sequencing (NGS) technologies permit the identification of cancer biomarkers, gene signatures, and their abnormal expression, affecting oncogenic and molecular targets and novel biomarkers for cancer therapies. Multiomics studies have changed the overall understanding of cancer and opened a precise perspective for tumor diagnostics and therapy. The use of these approaches has strengthened our understanding of disease pathophysiology and classifications at the molecular level, including specific interference with drug mechanisms of action. Still, it has limited added value in the clinical setting. The omics data on precision medicine include the application of data from genes, transcripts, and proteins for diagnosis, monitoring of diseases, risk factor determination, counseling, and development of novel therapeutics. Bioinformatics applications have expanded statistics-based analysis toward deriving molecular pathways and process models for characterizing phenotypes and drug action mechanisms. In this review, we will discuss transcriptomics and interference analysis that allows the identification of predictive biomarkers at the molecular level to test drug response and analyze the molecular process interface of disease progression-relevant pathophysiology and mechanism of action to propose predictive biomarkers.

[14] Modeling psychiatric disorders: from genomic findings to cellular phenotypes

• Authors: Anna Falk, Vivi M. Heine, A. Harwood, Patrick F. Sullivan, M. Peitz et al.
• Year: 2016
• Venue: Molecular Psychiatry
• URL: https://www.semanticscholar.org/paper/235b41240d78140de7ab06a3ad8a7d0b1bdff1a5
• DOI: 10.1038/mp.2016.89
• PMID: 27240529
• PMCID: 4995546
• Citations: 77
• Influential citations: 2
• Summary: The challenges for modeling of psychiatric disorders, potential solutions and how iPSC technology can be used to develop an analytical framework for the evaluation and therapeutic manipulation of fundamental disease processes are critically reviewed.
• Evidence snippets:
• Snippet 1 (score: 0.351) > The key challenge for iPSC-based disease modeling is to identify one or more relevant cellular phenotypes that accurately represent the disease pathophysiology. Increasing numbers of reports have demonstrated that for many diseases specific pathophysiology can be captured in human iPSC-based disease models. These range from cardiovascular disease, 44,45 cancer, 46,47 ocular disease, 48,49 diabetes mellitus 50,51 and neurological disorders of the brain. 52,53 Can the same approach be applied to complex psychiatric disorders? > The problem is that almost all psychiatric disorders are characterized by clinical signs and symptoms, but lack independent verification from objective biomarkers. Thus, how might these clinical phenotypes manifest themselves in terms of cell behavior? The identity of robust cellular 'readouts', which typify any psychiatric disorder, is a crucial unsolved problem and an area of intense study 54 (Table 2). When satisfactorily answered, this will herald a new degree of biological objectivity and quantification for the study of psychiatric disorders. > The aim is to find a single or small number of cell phenotypes or parameters that strongly associate with psychiatric disorders, and establish a cellular profile characteristic of cells derived from the general patient population. Although a consensus set of cellular phenotypes for psychiatric disorder is yet to be established, we can define some of their desired characteristics. First, cellular phenotypes have to relate to the biological pathways identified by genetics. Second, although there are many risk genes in disparate biological pathways, at some level, phenotypes should converge onto a much smaller grouping. Third, phenotypes need to be quantifiable. Finally, to be useful for drug development cellular phenotypes should be reversed by pharmacological treatment, although not necessarily by drugs in current use. > Although human iPSC-based approaches underrepresent the complexity of the human central nervous system, cellular phenotypes are likely to lie more proximal to molecular disease mechanisms than phenotypes seen at the level of a tissue or organism, 55 and thus may bypass compensatory homeostatic (2) Gene expression profiles of SCZ human iPSC neurons identified altered expression of many components of the cyclic AMP and WNT signaling pathways. > (3

[15] Changes in Serum Proteomic Profiles at Different Stages of Pregnancy Toxemia in Goats

• Authors: M. Uzti̇mür, C. N. Ünal, Gurler Akpinar
• Year: 2025
• Venue: Journal of Veterinary Internal Medicine
• URL: https://www.semanticscholar.org/paper/4b9c488b5dbd65d7b26fd2ad9aed70e8c4b59942
• DOI: 10.1111/jvim.70139
• PMID: 40492724
• PMCID: 12150350
• Summary: Understanding the serum proteome profiles of goats with pregnancy toxemia might help identify the proteomes and pathways responsible for the development of this disease and improve diagnosis and treatment.
• Evidence snippets:
• Snippet 1 (score: 0.351) > The pathophysiology and progression of this disease are not fully understood. > Traditional biomedical research has focused on the analysis of single genes, proteins, metabolites, or metabolic pathways in diseases. This molecular reductionist approach is based on the assumption that identifying genetic variations and molecular components will lead to new treatments for diseases [13][14][15][16]. However, many diseases are complex and multifactorial, and in order to determine the phenotype of such diseases, it is necessary to understand the changes that occur in more than one gene, pathway, protein, or metabolite at the cellular, tissue, and organismal levels [17][18][19]. Therefore, in recent years, proteomics, as one field of multi-omics technologies, has helped in evaluating the complex pathogenetic mechanisms of different diseases from a broad perspective and has made substantial contributions [20,21]. In veterinary medicine, proteomic analysis of metabolic diseases such as ketosis [16], hypocalcemia [22], and fatty liver [23] in dairy cows has contributed valuable insights for the definition of new pathophysiological pathways and new diagnosis and treatment protocols for these diseases. The proteomic approach can contribute importantly to a broad and detailed understanding of the changes that occur at the organismal level associated with the increase in BHBA concentration in goats with pregnancy toxemia. Our aim was to evaluate the serum protein profiles of goats with SPT or CPT using proteomic techniques to determine the proteomic profiles of these animals and to identify the relevant pathophysiological mechanisms.

[16] New Insights into Mitochondria in Health and Diseases

• Authors: Ya Li, Huhu Zhang, Chunjuan Yu, Xiaolei Dong, Fanghao Yang et al.
• Year: 2024
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/23002a4ffabfd043f52c664f4d5acab85b8dcac0
• DOI: 10.3390/ijms25189975
• PMID: 39337461
• PMCID: 11432609
• Citations: 37
• Summary: This overview outlines the various mechanisms by which mitochondria are involved in numerous illnesses and cellular physiological activities and provides new discoveries regarding the involvement of mitochondria in both disorders and the maintenance of good health.
• Evidence snippets:
• Snippet 1 (score: 0.350) > Mitochondria are essential organelles within cells, playing critical roles not only in energy metabolism but also in various cellular activities, such as cell differentiation, signal transduction, and apoptosis. Mitochondrial dysfunction is implicated in a range of diseases, including but not limited to diabetes and its complications, neurodegenerative disorders, myocardial ischemia-reperfusion injury, and heart failure. Therefore, investigating the structure and function of mitochondria as well as the mechanisms underlying mitochondrial dysfunction in disease contexts holds significant scientific and clinical importance. > Basic scientific research: Diseases manifest systemically and exhibit complexity; thus, it is imperative to understand mitochondrial structure at the molecular level along with known pathways while characterizing novel pathways that influence mitochondrial behavior and functionality. For instance, mapping genetic interactions among genes encoding mitochondrial proteins can elucidate interrelations between different aspects of mitochondrial function. The first focused map of mitochondria has been constructed in yeast models, revealing dense and significant connections among localization pathways distributed across various mitochondrial compartments [126]. > Disease diagnosis: A comprehensive understanding of the mechanisms governing mitochondrial dysfunction can facilitate the development of innovative diagnostic tools. By monitoring specific indicators related to mitochondrial function, earlier diagnosis of diseases associated with mitochondrial impairment becomes feasible. Employing nextgeneration sequencing technologies for analyzing the mitochondrial proteome aids in identifying novel proteins and pathways linked to mitochondria while enabling streamlined diagnostics alongside genetic counseling opportunities for patients with mitochondrial diseases [127]. > Drug development: Advancements in our comprehension of how mitochondria contribute to disease processes may promote targeted therapeutic strategies. For example, metformin-a widely used antidiabetic agent-has recently been repurposed as an anticancer drug; its combination with standard epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) significantly improves progression-free survival rates and overall survival outcomes for patients with advanced lung adenocarcinoma [125]. > Personalized medicine: Given that manifestations of mitochondrial dysfunction may vary among individuals, research into mitochondria provides a theoretical foundation for personalized medicine by allowing tailored treatment plans based on individual states of mitochondrial functionality [127].

[17] Precision Therapeutics in Lennox–Gastaut Syndrome: Targeting Molecular Pathophysiology in a Developmental and Epileptic Encephalopathy

• Authors: Debopam Samanta
• Year: 2025
• Venue: Children
• URL: https://www.semanticscholar.org/paper/455479c1bfbea7b90b73c109228f67c813d13888
• DOI: 10.3390/children12040481
• PMID: 40310132
• PMCID: 12025602
• Citations: 19
• Influential citations: 1
• Summary: A narrative review explores precision therapeutic strategies for LGS based on molecular pathophysiology, including channelopathies, receptor and ligand dysfunction, receptor and ligand dysfunction, cell signaling abnormalities, cell signaling abnormalities, synaptopathies, and the repurposing of existing medications with mechanism-specific effects.
• Evidence snippets:
• Snippet 1 (score: 0.350) > Lennox–Gastaut syndrome (LGS) is a severe childhood-onset developmental and epileptic encephalopathy characterized by multiple drug-resistant seizure types, cognitive impairment, and distinctive electroencephalographic patterns. Current treatments primarily focus on symptom management through antiseizure medications (ASMs), dietary therapy, epilepsy surgery, and neuromodulation, but often fail to address the underlying pathophysiology or improve cognitive outcomes. As genetic causes are identified in 30–40% of LGS cases, precision therapeutics targeting specific molecular mechanisms are emerging as promising disease-modifying approaches. This narrative review explores precision therapeutic strategies for LGS based on molecular pathophysiology, including channelopathies (SCN2A, SCN8A, KCNQ2, KCNA2, KCNT1, CACNA1A), receptor and ligand dysfunction (GABA/glutamate systems), cell signaling abnormalities (mTOR pathway), synaptopathies (STXBP1, IQSEC2, DNM1), epigenetic dysregulation (CHD2), and CDKL5 deficiency disorder. Treatment modalities discussed include traditional ASMs, dietary therapy, targeted pharmacotherapy, antisense oligonucleotides, gene therapy, and the repurposing of existing medications with mechanism-specific effects. Early intervention with precision therapeutics may not only improve seizure control but could also potentially prevent progression to LGS in susceptible populations. Future directions include developing computable phenotypes for accurate diagnosis, refining molecular subgrouping, enhancing drug development, advancing gene-based therapies, personalizing neuromodulation, implementing adaptive clinical trial designs, and ensuring equitable access to precision therapeutic approaches. While significant challenges remain, integrating biological insights with innovative clinical strategies offers new hope for transforming LGS treatment from symptomatic management to targeted disease modification.

[18] Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology

• Authors: Lucía Cremades-Jimeno, M. D. de Pedro, M. López-Ramos, J. Sastre, P. Mínguez et al.
• Year: 2021
• Venue: Frontiers in Immunology
• URL: https://www.semanticscholar.org/paper/d8ca6e130adec2dfa39545eb1763827d9450e4f5
• DOI: 10.3389/fimmu.2021.640791
• PMID: 33936056
• PMCID: 8081895
• Citations: 12
• Influential citations: 1
• Summary: This study has enabled it to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.
• Evidence snippets:
• Snippet 1 (score: 0.349) > Firstly, the molecular characterization of the three pathophysiological processes of interest (respiratory allergy, allergic asthma, and nonallergic asthma) was performed using the Therapeutic Performance Mapping System (TPMS) technology (Anaxomics Biotech, Barcelona, Catalonia, Spain) (31). Briefly, systems biology generates models that are able to reproduce the behavior of a disease in a patient, thus identifying the key genes, proteins, or metabolites in the development of the disease. A dictionary has been created to translate clinical and medical terms into molecular biology data, effectively linking the molecular and the clinical words. This dictionary, called the Biological Effectors Database (BED), relates biological processes (adverse events of drugs, drug indications, diseases, etc.) with the proteins most closely associated with them. Thus, the dictionary acts as a translator of clinical phenotypes into terms comprehensible for protein networks, and conversely allows for the translation of molecular measures toward clinical outcomes. The BED is structured hierarchically, where the biggest level is the entire disease, which is divided into different pathophysiological molecular motifs, which in turn contain the proteins involved in the development of the disease. The motifs are classified into two levels depending on their respective implication, i.e. causal motifs, which are directly related to the onset or pathophysiology of the condition, and symptomatic (manifestative) motifs, which are a consequence of the disease. > In the present study, respiratory allergy, allergic asthma, and non-allergic asthma have been characterized at the molecular level. Therefore, the analysis of high throughput data by means of TPMS allows for identification of those proteins closely associated with the disease of interest and can provide a mechanistic rationale for their involvement. The effector proteins of the manifestative and causal molecular motifs of these three diseases have been identified through bibliographic review and curate data. Figure 1 summarizes the workflow used for this study.

[19] Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

• Authors: P. Mesdom, R. Colle, É. Lebigot, S. Trabado, Eric Deflesselle et al.
• Year: 2020
• Venue: Current Neuropharmacology
• URL: https://www.semanticscholar.org/paper/79368e365458486de96794333613c12a6063bf54
• DOI: 10.2174/1570159X17666191021141057
• PMID: 31631822
• PMCID: 7327943
• Citations: 12
• Summary: This review highlights the great and still underused potential of HDF, which stands out as a very promising tool in the understanding of MDD and AD mechanisms of action.
• Evidence snippets:
• Snippet 1 (score: 0.349) > Background: Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood. Objective The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response. Methods The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action. Results HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action. Conclusion The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action

[20] Deciphering cellular states of innate tumor drug responses

• Authors: Esther Graudens, V. Boulanger, Cindy Mollard, R. Mariage-Samson, Xavier Barlet et al.
• Year: 2006
• Venue: Genome Biology
• URL: https://www.semanticscholar.org/paper/c79e62f4751e287a9527444fdeae83162022d48a
• DOI: 10.1186/gb-2006-7-3-r19
• PMID: 16542501
• PMCID: 1557757
• Citations: 135
• Influential citations: 7
• Summary: Molecular interaction networks are described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses, and represent a starting point from which by-pass chemotherapy schemes may be developed for critical therapeutic intervention in CRC patients.
• Evidence snippets:
• Snippet 1 (score: 0.349) > to TOP1 inhibitors [20,22]. > Our current understanding of mechanisms associated with drug resistance has been furthered by investigating drugresistant cellular models created by exposing a parental population (yeast, bacteria, mammalian cell lines) to increasing concentrations of a cytotoxic agent [23][24][25][26]. It has been difficult, however, to translate these insights into clinically meaningful improvements in cancer treatment, suggesting that in vitro unicellular models may not be applicable to the in vivo situation or represent the disease in its entirety. For instance, in CRC, TOP1 mutations that decrease the formation of DNA cleavage complexes were identified [27], but their implication in clinical resistance was not confirmed. > Since the introduction of molecular genetics methods in clinical oncology, examination of individual mRNA/protein expression levels of drug target molecules provided complementary indications on the mechanisms involved. Thus far, however, only a limited number of clinical studies of drug resistance have focused on individual candidate genes and these used clinical samples exclusively derived from patients that were already treated with drugs. In CRC, such gene-bygene molecular biology studies have highlighted only a partial list of candidate genes [28][29][30][31][32][33]; some of these genes were shown to be involved in mechanisms altering drug metabolite potency, others are known to participate in increase of drug efflux or decrease of drug toxicity, or to participate in inhibition of apoptosis (for an overview, see [32][33][34][35][36][37]). It is unclear at present whether these mechanisms play a causative role in clinical drug resistance, and no comprehensive analysis of entire drug resistance pathways has been conducted. > Pharmacogenetics and pharmacogenomics approaches have been initiated to study the relationship between individual variations and drug response rates [38,39]. Genetic polymorphisms of specific genes were found to be associated with clinical outcomes in patients treated through chemotherapy, and amplification of genes encoding drug targets or transporters was shown to alter the sensitivity of cancer cells to a particular chemotherapy [40,41]. Finally, loss of heterozygosity at specific regions of chromosomes was identified in specific carcinoma, although its consequence in treatment outcome remains

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.