Structure-function studies of the bHLH phosphorylation domain of TWIST1 in prostate cancer cells

The TWIST1 gene has diverse roles in development and pathologic diseases such as cancer. TWIST1 is a dimeric basic helix-loop-helix (bHLH) transcription factor existing as TWIST1-TWIST1 or TWIST1-E12/47. TWIST1 partner choice and DNA binding can be influenced during development by phosphorylation of Thr125 and Ser127 of the Thr-Gln-Ser (TQS) motif within the bHLH of TWIST1. The significance of these TWIST1 phosphorylation sites for metastasis is unknown. We created stable isogenic prostate cancer cell lines overexpressing TWIST1 wild-type, phospho-mutants, and tethered versions. We assessed these isogenic lines using assays that mimic stages of cancer metastasis. In vitro assays suggested the phospho-mimetic Twist1-DQD mutation could confer cellular properties associated with pro-metastatic behavior. The hypo-phosphorylation mimic Twist1-AQA mutation displayed reduced pro-metastatic activity compared to wild-type TWIST1 in vitro, suggesting that phosphorylation of the TWIST1 TQS motif was necessary for pro-metastatic functions. In vivo analysis demonstrates that the Twist1-AQA mutation exhibits reduced capacity to contribute to metastasis, whereas the expression of the Twist1-DQD mutation exhibits proficient metastatic potential. Tethered TWIST1-E12 heterodimers phenocopied the Twist1-DQD mutation for many in vitro assays, suggesting that TWIST1 phosphorylation may result in heterodimerization in prostate cancer cells. Lastly, the dual phosphatidylinositide 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) inhibitor BEZ235 strongly attenuated TWIST1-induced migration that was dependent on the TQS motif. TWIST1 TQS phosphorylation state determines the intensity of TWIST1-induced pro-metastatic ability in prostate cancer cells, which may be partly explained mechanistically by TWIST1 dimeric partner choice

The p53 Inhibitor MDM2 Facilitates Sonic Hedgehog-Mediated Tumorigenesis and Influences Cerebellar Foliation

Disruption of cerebellar granular neuronal precursor (GNP) maturation can result in defects in motor coordination and learning, or in medulloblastoma, the most common childhood brain tumor. The Sonic Hedgehog (Shh) pathway is important for GNP proliferation; however, the factors regulating the extent and timing of GNP proliferation, as well as GNP differentiation and migration are poorly understood. The p53 tumor suppressor has been shown to negatively regulate the activity of the Shh effector, Gli1, in neural stem cells; however, the contribution of p53 to the regulation of Shh signaling in GNPs during cerebellar development has not been determined. Here, we exploited a hypomorphic allele of Mdm2 (Mdm2puro), which encodes a critical negative regulator of p53, to alter the level of wild-type MDM2 and p53 in vivo. We report that mice with reduced levels of MDM2 and increased levels of p53 have small cerebella with shortened folia, reminiscent of deficient Shh signaling. Indeed, Shh signaling in Mdm2-deficient GNPs is attenuated, concomitant with decreased expression of the Shh transducers, Gli1 and Gli2. We also find that Shh stimulation of GNPs promotes MDM2 accumulation and enhances phosphorylation at serine 166, a modification known to increase MDM2-p53 binding. Significantly, loss of MDM2 in Ptch1+/− mice, a model for Shh-mediated human medulloblastoma, impedes cerebellar tumorigenesis. Together, these results place MDM2 at a major nexus between the p53 and Shh signaling pathways in GNPs, with key roles in cerebellar development, GNP survival, cerebellar foliation, and MB tumorigenesis

FoxP3+ T regulatory cells in cancer : prognostic biomarkers and therapeutic targets

T Regulatory cells (Tregs) can have both protective and pathological roles. They maintain immune homeostasis and inhibit immune responses in various diseases, including cancer. Proportions of Tregs in the peripheral blood of some cancer patients increase by five-to ten-folds, compared to those in healthy individuals. Tregs contribute to cancer development and progression by suppressing T effector cell functions, thereby compromising tumor killing and promoting tumor growth. Highly immunosuppressive Tregs express upregulated levels of the transcription factor, Forkhead box protein P3 (FoxP3). Elevated levels of FoxP3+ Tregs within the tumor microenvironment (TME) showed a positive correlation with poor prognosis in various cancer patients. Despite the success of immunotherapy, including the use of immune checkpoint inhibitors, a significant proportion of patients show low response rates as a result of primary or acquired resistance against therapy. Some of the mechanisms which underlie the development of therapy resistance are associated with Treg suppressive function. In this review, we describe Treg contribution to cancer development/progression, and the mechanisms of Treg-mediated immunosuppression. We discuss the prognostic significance of FoxP3+ Tregs in different cancers and their potential use as prognostic biomarkers. We also describe potential therapeutic strategies to target Tregs in combination with other types of immunotherapies aiming to overcome tumor resistance and improve clinical outcomes in cancer patients. Overall, understanding the prognostic significance of FoxP3+ Tregs in various cancers and their contribution to therapeutic resistance could help in the development of more effective targeted therapeutic strategies to enhance the clinical outcomes in cancer patients

The role of Mdm2 in cerebellum development and medulloblastoma tumorigenesis

During postnatal cerebellar development, Granule Neuron Precursors (GNPs) undergo a proliferative expansion in response to Sonic hedgehog (Shh) signaling, prior to differentiation and migration to their final niche. Aberrations in this tightly controlled process can tip the balance toward persistent proliferation leading to medulloblastoma, or toward arrest, apoptosis or premature differentiation leading to cerebellar malformations. Here, we report that Mdm2, the principal inhibitor of the p53 tumor suppressor protein, is required for the development of the cerebellum as well as for the initiation of medulloblastoma tumors. We employed a novel hypomorphic allele Mdm2puro that expresses reduced levels of Mdm2. Mdm2puro/Δ7-9 mice expressing ~30% of the wild-type Mdm2 show reduced cerebellar size, foliation and reduction in GNPs. This phenotype can be attributed in part to high levels of p53-dependent apoptosis. Additionally, through fate mapping studies we have shown that loss of Mdm2 also promotes the premature migration of GNPs. Global transcriptome analyses of the Mdm2 puro/Δ7-9 cerebellum supports dysregulation of the transcriptional programs that regulate GNP differentiation. A decrease in Shh target gene expression and decreased levels Gli1 and Gli2 proteins, the transducers of Shh signaling, suggest a role for p53 in attenuation of Shh signaling. These studies reveal that Mdm2 is required to limit p53 activation during GNP development and precocious p53 activation can lead to massive apoptosis and acceleration migration. Using Shh-responsive primary GNP cultures from wild-type mice we demonstrate that Shh stimulation of GNPs results in an increase in the steady state level of Mdm2 and an increase in Mdm2 phosphorylation at Ser 166, a modification known to enhance the anti-p53 function of Mdm2. These findings suggest that Shh signaling may upregulate Mdm2 to prevent p53 activation during GNP proliferation. In keeping with parallel mechanisms governing GNP proliferation and medulloblastoma tumor formation, we also observed increased levels of p-Mdm2SER166 in Shh-induced MB tumors and preneoplastic lesions in a mouse model of MB (Ptch1+/-). Notably, 70% decrease in Mdm abrogates the formation of PNLs while 50% reduction in Mdm2 (Mdm2+/Δ7-9) decreases PNLs two-fold. Sustained Shh signaling in Ptch1+/- mice promotes proliferation and survival of GNPs in the EGL from which PNLs arise. However, loss of Mdm2 decreases proliferation and increases apoptosis in GNPs, restoring them back to wild-type levels. We further demonstrate restoration of p53 levels and it\u27s activated form p53SER15 in EGL of Ptch1+/- Mdm2+/Δ7-9 as compared to Ptch1+/- mice. Overall, our results suggest that Mdm2 plays in important role in cerebellum development specifically in survival and migration of GNPs. This mechanism maybe hijacked by deregulated Shh signaling in certain medulloblastoma tumors resulting in Mdm2 dependent p53 inactivation

Bedömning av Parkinsons sjukdomstremor: Utnyttja smartphones för mätning avsymptom

Parkinson's disease (PD) is a progressive, chronic neurodegenerative disorder that impacts patients' quality of life. Hand tremor is a hallmark motor symptom of PD. However, current clinical tremor assessment methods are time-consuming and expensive and may not capture the full extent of tremor fluctuations. The built-in sensors in smartphones offer an accessible and cost-effective alternative for objective tremor assessment. This study presents a systematic approach to developing a quantitative algorithm for Parkinson's disease tremor assessment using Inertial Measurement Unit (IMU) data. This study begins with a comprehensive data visualisation and understanding phase, leading to the design decision to implement a multiple linear regression model for tremor severity prediction. The IMU data, collected from 10 patients, is pre-processed and normalised to ensure consistency and account for varying degrees of tremor severity. Feature extraction is conducted based on insights from literature, resulting in 16 unique features. These unique features are extracted for each of the acceleration and rotation rate data, resulting in 582 total features over both hands and all three tremor types. Recursive Feature Elimination with Cross-Validation (RFECV) is employed for feature selection, identifying the most relevant features contributing to tremor severity prediction. A multiple linear regression model is implemented and trained using the Leave-One-Out with Cross-Validation (LOOCV) method. The model's performance is evaluated resulting in a mean MSE of 0.88, a mean MAE of 0.69, and an R² of 0.88. The results indicate a strong correlation between predicted and actual tremor severity, suggesting the model's high validity. The selected features show a high correlation with the patient's MDS-UPDRS scores, further validating their relevance in predicting tremor severity. Greater results could be achieved, but sample size was the greatest limitation during this study. This study demonstrates the potential of using IMU data and multiple linear regression modelling for accurate PD tremor assessment within Mobistudy, contributing to the field of quantitative PD analysis.Parkinsons sjukdom (PD) är en progressiv, kronisk neurodegenerativ sjukdom som påverkar patienternas livskvalitet. Handtremor är ett framträdande motoriskt symptom på PD. Dock är nuvarande kliniska tremorbedömningsmetoder tidskrävande och dyra och kanske inte fångar hela omfattningen av tremorfluktuationer. Inbyggda sensorer i smartphones erbjuder ett tillgängligt och kostnadseffektivt alternativ för objektiv tremorbedömning. Denna studie presenterar en systematisk metod för att utveckla en kvantitativ algoritm för bedömning av Parkinsons sjukdomstremor med hjälp av data från Inertial Measurement Unit (IMU). Denna studie börjar med en omfattande data visualisering och förståelsefas, vilket leder till designbeslutet att implementera en multipel linjär regressionsmodell för förutsägelse av tremorseveritet. IMU-data, insamlad från 10 patienter, förbehandlas och normaliseras för att säkerställa konsekvens och ta hänsyn till varierande grad av tremorseveritet. Funktionsextraktion genomförs baserat på insikter från litteraturen, vilket resulterar i 16 unika funktioner. Dessa unika funktioner extraheras för var och en av accelerations- och rotationshastighetsdata, vilket resulterar i totalt 582 funktioner över båda händerna och alla tre tremortyper. Rekursiv funktionseleminering med korsvalidering (RFECV) används för funktionsval, vilket identifierar de mest relevanta funktionerna som bidrar till förutsägelse av tremorseveritet. En multipel linjär regressionsmodell implementeras och tränas med hjälp av Leave-One-Out med Cross-Validation (LOOCV) metoden. Modellens prestanda utvärderas vilket resulterar i ett genomsnittligt MSE på 0,88, ett genomsnittligt MAE på 0,69 och ett R² på 0,88. Resultaten indikerar en stark korrelation mellan förutsagd och faktisk tremorseveritet, vilket tyder på modellens höga validitet. De valda funktionerna visar en hög korrelation med patienternas MDS-UPDRS-poäng, vilket ytterligare validerar deras relevans i förutsägelse av tremorseveritet. Större resultat kunde uppnås, men provstorleken var den största begränsningen under denna studie. Denna studie visar potentialen att använda IMU-data och multipel linjär regressionsmodellering för noggrann PD-tremorbedömning inom Mobistudy, vilket bidrar till fältet för kvantitativ PD-analys

Bedömning av Parkinsons sjukdomstremor: Utnyttja smartphones för mätning avsymptom

Parkinson's disease (PD) is a progressive, chronic neurodegenerative disorder that impacts patients' quality of life. Hand tremor is a hallmark motor symptom of PD. However, current clinical tremor assessment methods are time-consuming and expensive and may not capture the full extent of tremor fluctuations. The built-in sensors in smartphones offer an accessible and cost-effective alternative for objective tremor assessment. This study presents a systematic approach to developing a quantitative algorithm for Parkinson's disease tremor assessment using Inertial Measurement Unit (IMU) data. This study begins with a comprehensive data visualisation and understanding phase, leading to the design decision to implement a multiple linear regression model for tremor severity prediction. The IMU data, collected from 10 patients, is pre-processed and normalised to ensure consistency and account for varying degrees of tremor severity. Feature extraction is conducted based on insights from literature, resulting in 16 unique features. These unique features are extracted for each of the acceleration and rotation rate data, resulting in 582 total features over both hands and all three tremor types. Recursive Feature Elimination with Cross-Validation (RFECV) is employed for feature selection, identifying the most relevant features contributing to tremor severity prediction. A multiple linear regression model is implemented and trained using the Leave-One-Out with Cross-Validation (LOOCV) method. The model's performance is evaluated resulting in a mean MSE of 0.88, a mean MAE of 0.69, and an R² of 0.88. The results indicate a strong correlation between predicted and actual tremor severity, suggesting the model's high validity. The selected features show a high correlation with the patient's MDS-UPDRS scores, further validating their relevance in predicting tremor severity. Greater results could be achieved, but sample size was the greatest limitation during this study. This study demonstrates the potential of using IMU data and multiple linear regression modelling for accurate PD tremor assessment within Mobistudy, contributing to the field of quantitative PD analysis.Parkinsons sjukdom (PD) är en progressiv, kronisk neurodegenerativ sjukdom som påverkar patienternas livskvalitet. Handtremor är ett framträdande motoriskt symptom på PD. Dock är nuvarande kliniska tremorbedömningsmetoder tidskrävande och dyra och kanske inte fångar hela omfattningen av tremorfluktuationer. Inbyggda sensorer i smartphones erbjuder ett tillgängligt och kostnadseffektivt alternativ för objektiv tremorbedömning. Denna studie presenterar en systematisk metod för att utveckla en kvantitativ algoritm för bedömning av Parkinsons sjukdomstremor med hjälp av data från Inertial Measurement Unit (IMU). Denna studie börjar med en omfattande data visualisering och förståelsefas, vilket leder till designbeslutet att implementera en multipel linjär regressionsmodell för förutsägelse av tremorseveritet. IMU-data, insamlad från 10 patienter, förbehandlas och normaliseras för att säkerställa konsekvens och ta hänsyn till varierande grad av tremorseveritet. Funktionsextraktion genomförs baserat på insikter från litteraturen, vilket resulterar i 16 unika funktioner. Dessa unika funktioner extraheras för var och en av accelerations- och rotationshastighetsdata, vilket resulterar i totalt 582 funktioner över båda händerna och alla tre tremortyper. Rekursiv funktionseleminering med korsvalidering (RFECV) används för funktionsval, vilket identifierar de mest relevanta funktionerna som bidrar till förutsägelse av tremorseveritet. En multipel linjär regressionsmodell implementeras och tränas med hjälp av Leave-One-Out med Cross-Validation (LOOCV) metoden. Modellens prestanda utvärderas vilket resulterar i ett genomsnittligt MSE på 0,88, ett genomsnittligt MAE på 0,69 och ett R² på 0,88. Resultaten indikerar en stark korrelation mellan förutsagd och faktisk tremorseveritet, vilket tyder på modellens höga validitet. De valda funktionerna visar en hög korrelation med patienternas MDS-UPDRS-poäng, vilket ytterligare validerar deras relevans i förutsägelse av tremorseveritet. Större resultat kunde uppnås, men provstorleken var den största begränsningen under denna studie. Denna studie visar potentialen att använda IMU-data och multipel linjär regressionsmodellering för noggrann PD-tremorbedömning inom Mobistudy, vilket bidrar till fältet för kvantitativ PD-analys

p53 in the CNS: Perspectives on Development, Stem Cells, and Cancer

The p53 tumor suppressor potently limits the growth of immature and mature neurons under conditions of cellular stress. Although loss of p53 function contributes to the pathogenesis of central nervous system (CNS) tumors, excessive p53 function is implicated in neural tube defects, embryonic lethality, and neuronal degeneration. Thus, p53 function must be tightly controlled. The anti-proliferative properties of p53 are mediated, in part, through the induction of apoptosis, cell cycle arrest, and senescence. Although there is still much to be learned about the role of p53 in these processes, recent evidence supports exciting new roles for p53 in a wide range of processes, including neural precursor cell self-renewal, differentiation, and cell fate decisions. Understanding the full repertoire of p53 function in CNS development and tumorigenesis may provide us with novel points of therapeutic intervention for human diseases of the CNS

Characteristics of non-fasting patients with diabetes type 2 in the DAR global surveys of 2020 and 2022

Introduction: The decision to fast or not is quite complex. Personal, medical and religious matters may influence it for individuals with diabetes. However, a diagnosis of diabetes does not constitute an automatic exemption from Ramadan fasting. We aimed to evaluate the characteristics of the non-fasting cohort and explore the potential reasons during Ramadan through the global Ramadan surveys of 2020 and 2022. Method: The Diabetes and Ramadan (DaR) Global Study is an observational retrospective survey conducted between 2020 and 2022, which included adult patients (18 and older) who fasted and those who opted not to fast. The survey captured demographic data and patient characteristics (co-morbidities, duration and type of diabetes, diabetes-related complications and medications, development of acute complications during Ramadan, hospitalisations and ER visits) for both groups. Results: Of 12,059 patients, 1822 (14.5 %) did not fast during Ramadan; that population leaned towards females (54.6 %). They had an average age of 60.58 years (SD=12.12) with a statistically significant difference from the fasting population, averaging 54.29±11.45 (p = 0.000). Also of note was the more significant average duration of diabetes in the non-fasting cohort (12.54 years vs 9.44). There are notable regional differences in rates of fasting that ranged between 6.3 % and up to 51.2 % of patients opting not fast in certain regions (Chart 1). The risk factors that showed apparent differences of high statistical significance (p ≤ 0.001) included: long duration of diabetes of over ten years, age above 60+ years, HbA1c value over 9 %, use of insulin therapy, and being affected by one or more vascular complications (these include CKD, CVD, and diabetic foot problems). Conclusions: Many factors and comorbidities might influence patients’ decisions when planning Ramadan fasting. The non-fasting population's demographic and clinical profiles reveal distinctive features, emphasising a need for tailored risk assessments. Furthermore, regional disparities in the decision to fast underscore the multifaceted nature of this decision-making process. The new IDF-DAR risk assessment tool can help to stratify patients’ risk during Ramadan fasting and bridge the gap among different populations and cultures