Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro–/– female mice with doxorubicin (DOX) because Ptpro–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.</p

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro–/– female mice with doxorubicin (DOX) because Ptpro–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.</p

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment.

Chemotherapy-related cognitive impairment (CRCI) or chemo brain is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro-/- female mice with doxorubicin (DOX) because Ptpro-/- female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro-/- female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro-/- female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro–/– female mice with doxorubicin (DOX) because Ptpro–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.</p

A Weakened Transcriptional Enhancer Yields Variegated Gene Expression

Identical genes in the same cellular environment are sometimes expressed differently. In some cases, including the immunoglobulin heavy chain (IgH) locus, this type of differential gene expression has been related to the absence of a transcriptional enhancer. To gain additional information on the role of the IgH enhancer, we examined expression driven by enhancers that were merely weakened, rather than fully deleted, using both mutations and insulators to impair enhancer activity. For this purpose we used a LoxP/Cre system to place a reporter gene at the same genomic site of a stable cell line. Whereas expression of the reporter gene was uniformly high in the presence of the normal, uninsulated enhancer and undetectable in its absence, weakened enhancers yielded variegated expression of the reporter gene; i.e., the average level of expression of the same gene differed in different clones, and expression varied significantly among cells within individual clones. These results indicate that the weakened enhancer allows the reporter gene to exist in at least two states. Subtle aspects of the variegation suggest that the IgH enhancer decreases the average duration (half-life) of the silent state. This analysis has also tested the conventional wisdom that enhancer activity is independent of distance and orientation. Thus, our analysis of mutant (truncated) forms of the IgH enhancer revealed that the 250 bp core enhancer was active in its normal position, ∼1.4 kb 3′ of the promoter, but inactive ∼6 kb 3′, indicating that the activity of the core enhancer was distance-dependent. A longer segment – the core enhancer plus ∼1 kb of 3′ flanking material, including the 3′ matrix attachment region – was active, and the activity of this longer segment was orientation-dependent. Our data suggest that this 3′ flank includes binding sites for at least two activators

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR &lt; 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.</p

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR &lt; 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.</p

Soft Sensor Modeling Based on Multiple Gaussian Process Regression and Fuzzy C-mean Clustering

In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase and dead phase, the training samples are classified into 4 subcategories by using fuzzy C- mean clustering algorithm. For each sub-category, the samples are trained using the Gaussian process regression and the corresponding soft-sensing sub-model is established respectively. For a new sample, the membership between this sample and sub-models are computed based on the Euclidean distance, and then the prediction output of soft sensor is obtained using the weighting sum. Taking the Lysine fermentation as example, the simulation and experiment are carried out and the corresponding results show that the presented method achieves better fitting and generalization ability than radial basis function neutral network and single Gaussian process regression model