Vulnerability Signatures Research Articles

RTID-08. MACHINE LEARNING TO UNCOVER SIGNATURES OF VULNERABILITY IN GLIOBLASTOMA UMBRELLA SIGNATURE TRIAL (GUST)

Abstract Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. A glioblastoma umbrella signature trial (GUST) posits multiple investigational treatment arms based on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of orthogonal signatures to classify patients into the likely-most-beneficial arm. Assigning optimal thresholds of vulnerability signatures to classify patients as “most-likely responders” for each specific treatment arm is a crucial task. We utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression, to predict vulnerability classification. By applying semi-supervised algorithms to the TCGA GBM cohort, we were able to transform the samples with the highest certainty of predicted response into a self-labeled dataset and thus augment the training data. In this case, we developed a predictive model with a larger sample size and potential better performance. Our GUST design currently includes four treatment arms for GBM patients: Arsenic Trioxide, Methoxyamine, Selinexor and Pevonedistat. Each treatment arm manifests its own signature developed by the customized machine learning pipelines based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability, we also developed a multi-class/label classification ensemble model that’s capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. Such a multi-class model would also enable us to rank each arm and provide sequential treatment planning. By expansion to four independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models correctly placed 4 out of 6 models into the “responder” group. Our utilization of multiple vulnerability signatures in a GUST trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM. Surgical Therapies

CLRM-01. MACHINE LEARNING TO UNCOVER SIGNATURES OF VULNERABILITY IN GLIOBLASTOMA UMBRELLA SIGNATURE TRIAL (GUST)

Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. A glioblastoma umbrella signature trial (GUST) posits multiple investigational treatment arms based on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of orthogonal signatures to classify patients into the likely-most-beneficial arm. Assigning optimal thresholds of vulnerability signatures to classify patients as “most-likely responders” for each specific treatment arm is a crucial task. We utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression, to predict vulnerability classification. By applying semi-supervised algorithms to the TCGA GBM cohort, we were able to transform the samples with the highest certainty of predicted response into a self-labeled dataset and thus augment the training data. In this case, we developed a predictive model with a larger sample size and potential better performance. Our GUST design currently includes four treatment arms for GBM patients: Arsenic Trioxide, Methoxyamine, Selinexor and Pevonedistat. Each treatment arm manifests its own signature developed by the customized machine learning pipelines based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability, we also developed a multi-class/label classification ensemble model that’s capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. Such a multi-class model would also enable us to rank each arm and provide sequential treatment planning. By expansion to four independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models correctly placed 4 out of 6 models into the “responder” group. Our utilization of multiple vulnerability signatures in a GUST trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM.

Abstract 2534: Underlying mechanism of response to neddylation inhibition in a subset of glioblastoma

Abstract Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor in adults with a 5-year survival rate of ~3%. The ubiquitin/proteasome system maintains intracellular homeostasis via degradation of unwanted proteins. Bortezomib is a first-in-class, non-specific proteasome inhibitor exploiting this system, although it has poor penetration across the blood brain barrier, and its lack of specificity is accompanied by adverse events. Neddylation is a specific pathway within the ubiquitin/proteasome system that is overactive in glioblastoma (GBM), and whose upregulation has been associated with glioma progression and worse survival. Pevonedistat is a first-in-class small-molecule neddylation inhibitor shown to impact protein degradation, leading to elevated abundance of some tumor suppressor proteins (Wee1, others), which then inhibit growth of GBM cells in culture and orthotopic xenografts. Because the molecular heterogeneity within and across GBM patients obscures therapeutic targets and obfuscates signals of efficacy in clinical trials, we propose the use of molecular “signatures of vulnerability” to targeted agents in subsets of preclinical models. We and others have shown that pevonedistat interferes with the growth of multiple types of cancers, including GBM, but the determinants of vulnerability are not fully understood. Here, we report a selective vulnerability to pevonedistat in a subset of GBM, specifically, instances with mutations or copy number deletions of PTEN are associated with de novo resistance to pevonedistat. Time-course studies of sensitive and non-sensitive GBM cells using transcriptomics and proteomics/phosphoproteomics enable independent discovery and testing for determinants of response to pevonedistat. Our results demonstrate that in GBM, resistance to pevonedistat is driven by reduced PTEN-chromatin binding (loss-of-function or lower expression) that is also independent of PTEN's lipid phosphatase activity (i.e., PI3K/AKT signaling). Across 25 glioma cell lines, we found that PTEN signaling, DNA replication, and chromatin instability pathways are the most significant differentiators between pevonedistat sensitive vs. non-sensitive models. In GBM models with modest to low sensitivity to pevonedistat, TOP2A expression was elevated. Combination treatment with the TOP2A inhibitor, etoposide, proved synergistic with pevonedistat. We report, for the first time, that PTEN status both serves as a novel biomarker for GBM sensitivity to pevonedistat and reveals a synergistic vulnerability of TOP2A inhibitors in combination with pevonedistat. Paired use of GBM PDX models of varying sensitivity with drug development testing allows the advancement of a promising agent as well as a patient-enrollment “signature of vulnerability” likely to increase the likelihood of demonstrating therapeutic efficacy in early clinical trials. Citation Format: Shayesteh Ferdosi, Brett Taylor, Matthew Lee, Nanyun Tang, Sen Peng, Rita Bybee, George Reid, Lauren Hartman, Krystine Garcia-Mansfield, Ritin Sharma, Patrick Pirrotte, Frank Furnari, Harshil Dhruv, Michael Berens. Underlying mechanism of response to neddylation inhibition in a subset of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2534.

Abstract 180: Identifying signatures of vulnerability through machine learning in an umbrella trial for glioblastoma

Abstract Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. An umbrella trial tests multiple investigational treatment arms depending on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of preferably orthogonal molecular biomarkers to classify patients into the likely-most-beneficial arm. Assigning thresholds of molecular signatures to classify a patient as a “most-likely responder” for one specific treatment arm is a crucial task. Gene Set Variation Analysis (GSVA) of specimens from a GBM clinical trial of methoxyamine associated differential enrichment in DNA repair pathways activities with patient response. However, the large number of DNA-repair related pathways confound confident “high” enrichment of responder, as well as obscuring to what degree each feature contributes to the likelihood of a patient's response. Here, we utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression (ERLR) to predict vulnerability classification. By first training all available data using semi-supervised algorithms we transformed unclassified TCGA GBM samples with highest certainty of predicted response into a self-labeled dataset. In this case, we developed a predictive model which has a larger sample size and potential better performance. Our umbrella trial design currently includes three treatment arms for GBM patients: arsenic trioxide, methoxyamine, and pevonedistat. Each treatment arm manifests its own signature developed by the above (or similar) machine learning pipeline based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability (with future more treatment arms), we also developed a multi-label classification ensemble model that's capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. By expansion to three, independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models placed 4 out of 6 models into a “high likelihood of response” regimen. Our utilization of multiple vulnerability signatures in an umbrella trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM. Citation Format: Matthew Eric Lee, Nanyun Tang, Manmeet Ahluwalia, Ekokobe Fonkem, Karen Fink, Harshil Dhurv, Harshil Dhurv, Michael E. Berens, Sen Peng. Identifying signatures of vulnerability through machine learning in an umbrella trial for glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 180.

Evaluation of the effects of altitude on biological signatures of inflammation and anxiety- and depressive-like behavioral responses

Over sixteen million people suffer from a depressive episode annually in the United States, with females affected at twice the rate of males. Little is known about the effects of exposure to high altitude on the risk of development of major depressive disorder, despite reports of higher suicide rates at higher altitudes. We hypothesize that exposure to hypobaric hypoxia at high altitude increases endophenotypes of self-directed suicidal violence, including biological signatures of chronic inflammation and vulnerability to anxiety-like and depressive-like behavioral responses in a sex-specific manner. Biological signatures of inflammation, including granulocyte:lymphocyte ratios, monocyte cell counts, and monocyte:lymphocyte ratios were assessed using complete blood count data, anhedonia, and anxiety- and depressive-like behavioral responses were evaluated. We assessed biological signatures of inflammation and behavioral responses in the open-field test, sucrose preference test, and modified Porsolt forced swim test in young adult male and female Long-Evans and Sprague Dawley rats. All tests were conducted near sea level (374 ft [114 m] elevation) and at moderate-high altitude (5430 ft [1655 m] elevation) during acclimation periods of one, two, three, four, and five weeks following shipment from a sea level animal breeding facility (N = 320, n = 8 per group). Exposure to moderate-high altitude induced a biological signature of increased inflammation, as evidenced by main effects of altitude for: 1) increased granulocyte:lymphocyte ratio; 2) increased count and relative abundance of circulating monocytes; and 3) increased monocyte:lymphocyte ratios. Exposure to moderate-high altitude also increased anhedonia as assessed in the sucrose preference test in both male and female rats, when data were collapsed across strain and time. Among male and female Long Evans rats, exposure to moderate-high altitude increased immobility in the forced swim test, without changing anxiety-like behaviors in the open-field test. Finally, granulocyte:lymphocyte ratios were correlated with anhedonia in the sucrose preference test. These data are consistent with the hypothesis that hypobaric hypoxia at moderate-high altitude induces persistent endophenotypes of self-directed suicidal violence including biological signatures of inflammation, anhedonia, and depressive-like behavioral responses.

Neural Markers of Vulnerability to Anxiety Outcomes after Traumatic Brain Injury.

Anxiety outcomes after traumatic brain injury (TBI) are complex, and the underlying neural mechanisms are poorly understood. Here, we developed a multi-dimensional behavioral profiling approach to investigate anxiety-like outcomes in mice that takes into account individual variability. Departing from the tradition of comparing outcomes in TBI versus sham groups, we identified a subgroup within the TBI group that is vulnerable to anxiety dysfunction, and present increased exploration of the anxiogenic zone compared to sham controls or resilient injured animals, by applying dimensionality reduction, clustering, and post hoc validation to behavioral data obtained from multiple assays for anxiety at several post-injury time points. These vulnerable animals expressed distinct molecular profiles in the corticolimbic network, with downregulation in gamma-aminobutyric acid and glutamate and upregulation in neuropeptide Y markers. Indeed, among vulnerable animals, not resilient or sham controls, severity of anxiety-related outcomes correlated strongly with expression of molecular markers. Our results establish a foundational approach, with predictive power, for reliably identifying maladaptive anxiety outcomes after TBI and uncovering neural signatures of vulnerability to anxiety.

BIOM-24. IDENTIFYING VULNERABILITY SIGNATURES THROUGH MACHINE LEARNING IN AN UMBRELLA TRIAL FOR GLIOBLASTOMA

Abstract Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. An umbrella trial tests multiple treatment arms depending on corresponding biomarker signatures. A contingency of an umbrella trial is a suite of preferably orthogonal molecular biomarkers to classify patients into the likely-most-beneficial arm. Assigning thresholds of molecular signatures to classify a patient as a “most-likely responder” for one specific treatment arm is a crucial task. Gene Set Variation Analysis (GSVA) of specimens from a GBM clinical trial of methoxyamine associated differential enrichment in DNA repair pathways activities with patient response. The 44 DNA-repair related pathways confound confident “high” enrichment of responder, as well as obscuring to what degree each feature contributes to the likelihood of a patient’s response. Here, we utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression (ERLR) to predict classification. By first training all available data using semi-supervised algorithms we transformed unclassified TCGA GBM samples with highest certainty of predicted response into a self-labeled dataset. In this case, we developed the predictive model which has a larger sample size and potential better performance. Our umbrella trial design currently includes three treatment arms for GBM patients: arsenic trioxide, methoxyamine, and pevonedistat. Each treatment arm manifests its own signature developed by the above (or similar) machine learning pipeline based on selected gene mutation status and whole transcriptome data. By expansion to three, independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients binned 56% of all cases into a “high likelihood of response“ arm. Predicted vulnerability using genomic data from preclinical PDX models placed 4 out of 6 models into a “high likelihood of response” regimen. Our utilization of multiple vulnerability signatures in an umbrella trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM.

Behavioral and brain signatures of substance use vulnerability in childhood

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology

Several labels, such as neuroticism, negative emotionality, and dispositional negativity, indicate a broad dimension of psychopathology. However, largely separate, often disorder-specific research lines have developed that focus on different cognitive and affective characteristics that are associated with this dimension, such as perseverative cognition (worry, rumination), reduced autobiographical memory specificity, compromised fear learning, and enhanced somatic-symptom reporting. In this article, we present a theoretical perspective within a predictive-processing framework in which we trace these phenotypically different characteristics back to a common underlying “better-safe-than-sorry” processing strategy. This implies information processing that tends to be low in sensory-perceptual detail, which allows threat-related categorical priors to dominate conscious experience and for chronic uncertainty/surprise because of a stagnated error-reduction process. This common information-processing strategy has beneficial effects in the short term but important costs in the long term. From this perspective, we suggest that the phenomenally distinct cognitive and affective psychopathological characteristics mentioned above represent the same basic processing heuristic of the brain and are only different in relation to the particular type of information involved (e.g., in working memory, in autobiographical memory, in the external and internal world). Clinical implications of this view are discussed.

Sex Differences in Circulating T-Tau Trajectories After Sports-Concussion and Correlation With Outcome

Sex differences in molecular biomarkers after sports-related concussion (SRC) could steadily advance our understanding of injury heterogeneity and complexity, and help capture phenotypic characteristics, by unveiling sex-dependent pathobiological processes and disease mechanisms. Such knowledge will help improve diagnosis, clinical management, and prognosis. Total-tau (t-tau) has recently emerged as a promising blood marker showing sex-associated differences in neurodegenerative diseases. Nonetheless, to date, little is known about the potential influence of sex on its injury-related concentration and dynamics after SRC. We hypothesized that measurements of circulating levels of t-tau over time would reflect a differential vulnerability signature, providing insights into the sex-related phenotypes and their relationship with clinical outcomes. To test this hypothesis, plasma levels of t-tau were measured using an ultrasensitive immunoassay up to 7 days after injury, in 46 concussed athletes (20 males, 26 females). We used trajectory analysis to generate two distinct temporal profiles of t-tau, which were then compared with gender and return to play (RTP). The majority of subjects (~63%) started with low t-tau concentrations that further declined within the first 48 h; while the remaining (“maximal decliners”) started with concentrations comparable to the baseline levels that also fell over time, but persisting markedly higher compared with the first profile. The maximal decliner group was primarily composed of female subjects (p = 0.007) and was significantly associated with poor outcome (RTP ≥ 10 days after concussion) (p = 0.011). Taken together, our data provide evidence for the existence of sex-related biosignatures following sports-related concussions, possibly indicating a differential effect as a result of distinct brain vulnerability and inherent injury response. Future studies will be required to further elucidate underlying sex-based biological and pathophysiological mechanisms, and determine the value of t-tau signatures for management and therapeutic decision-making in sports-related concussions.

Transcriptomic signatures of brain regional vulnerability to Parkinson\u2019s disease

The molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson’s disease remain poorly understood. Here, we identified transcriptomic signatures across brain regions involved in Braak Lewy body stages in non-neurological adults from the Allen Human Brain Atlas. Among the genes that are indicative of regional vulnerability, we found known genetic risk factors for Parkinson’s disease: SCARB2, ELOVL7, SH3GL2, SNCA, BAP1, and ZNF184. Results were confirmed in two datasets of non-neurological subjects, while in two datasets of Parkinson’s disease patients we found altered expression patterns. Co-expression analysis across vulnerable regions identified a module enriched for genes associated with dopamine synthesis and microglia, and another module related to the immune system, blood-oxygen transport, and endothelial cells. Both were highly expressed in regions involved in the preclinical stages of the disease. Finally, alterations in genes underlying these region-specific functions may contribute to the selective regional vulnerability in Parkinson’s disease brains.

Multiple axes of ecological vulnerability to climate change.

Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms-niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)-each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.

Serotonergic Plasticity in the Dorsal Raphe Nucleus Characterizes Susceptibility and Resilience to Anhedonia.

Chronic stress induces anhedonia in susceptible but not resilient individuals, a phenomenon observed in humans as well as animal models, but the molecular mechanisms underlying susceptibility and resilience are not well understood. We hypothesized that the serotonergic system, which is implicated in stress, reward, and antidepressant therapy, may play a role. We found that plasticity of the serotonergic system contributes to the differential vulnerability to stress displayed by susceptible and resilient animals. Stress-induced anhedonia was assessed in adult male rats using social defeat and intracranial self-stimulation, while changes in serotonergic phenotype were investigated using immunohistochemistry and in situ hybridization. Susceptible, but not resilient, rats displayed an increased number of neurons expressing the biosynthetic enzyme for serotonin, tryptophan-hydroxylase-2 (TPH2), in the ventral subnucleus of the dorsal raphe nucleus (DRv). Further, a decrease in the number of DRv glutamatergic (VGLUT3+) neurons was observed in all stressed rats. This neurotransmitter plasticity is activity-dependent, as was revealed by chemogenetic manipulation of the central amygdala, a stress-sensitive nucleus that forms a major input to the DR. Activation of amygdalar corticotropin-releasing hormone (CRH)+ neurons abolished the increase in DRv TPH2+ neurons and ameliorated stress-induced anhedonia in susceptible rats. These findings show that activation of amygdalar CRH+ neurons induces resilience, and suppresses the gain of serotonergic phenotype in the DRv that is characteristic of susceptible rats. This molecular signature of vulnerability to stress-induced anhedonia and the active nature of resilience could be targeted to develop new treatments for stress-related disorders like depression.SIGNIFICANCE STATEMENT Depression and other mental disorders can be induced by chronic or traumatic stressors. However, some individuals are resilient and do not develop depression in response to chronic stress. A complete picture of the molecular differences between susceptible and resilient individuals is necessary to understand how plasticity of limbic circuits is associated with the pathophysiology of stress-related disorders. Using a rodent model, our study identifies a novel molecular marker of susceptibility to stress-induced anhedonia, a core symptom of depression, and a means to modulate it. These findings will guide further investigation into cellular and circuit mechanisms of resilience, and the development of new treatments for depression.

Supersaturated proteins are enriched at synapses and underlie cell and tissue vulnerability in Alzheimer's disease

Neurodegenerative disorders progress across the brain in characteristic spatio-temporal patterns. A better understanding of the factors underlying the specific cell and tissue vulnerability responsible for such patterns could help identify the molecular origins of these conditions. To investigate these factors, based on the observation that neurodegenerative disorders are closely associated with the presence of aberrant protein deposits, we made the hypothesis that the vulnerability of cells and tissues is associated to the overall levels of supersaturated proteins, which are those most metastable against aggregation. By analyzing single-cell transcriptomic and subcellular proteomics data on healthy brains of ages much younger than those typical of disease onset, we found that the most supersaturated proteins are enriched in cells and tissues that succumb first to neurodegeneration. Then, by focusing the analysis on a metastable subproteome specific to Alzheimer's disease, we show that it is possible to recapitulate the pattern of disease progression using data from healthy brains. We found that this metastable subproteome is significantly enriched for synaptic processes and mitochondrial energy metabolism, thus rendering the synaptic environment dangerous for aggregation. The present identification of protein supersaturation as a signature of cell and tissue vulnerability in neurodegenerative disorders could facilitate the search for effective treatments by providing clearer points of intervention.

Whole blood transcriptomic profiles can differentiate vulnerability to chronic low back pain.

The mechanisms underlying the transition from acute to chronic pain remain unclear. Here, we sought to characterize the transcriptome associated with chronic low back pain as well as the transcriptome of the transition from acute to chronic low back pain. For the analysis, we compared the whole blood transcriptome of: (a) patients at the onset of low back pain who no longer had pain within 6 weeks after onset (acute) with patients who developed chronic low back pain at 6 months (chronic T5); and, (b) patients at the onset of low back pain (chronic T1) who developed chronic pain at 6 months with healthy pain-free (normal) controls. The majority of differentially expressed genes were protein coding. We illustrate a unique chronic low back pain transcriptome characterized by significant enrichment for known pain genes, extracellular matrix genes, and genes from the extended major histocompatibility complex (MHC) genomic locus. The transcriptome of the transition from acute to chronic low back pain was characterized by significant upregulation of antigen presentation pathway (MHC class I and II) genes and downregulation of mitochondrial genes associated with oxidative phosphorylation, suggesting a unique genomic signature of vulnerability to low back pain chronicity.

Resting-state mapping of neural signatures of vulnerability to depression relapse

BackgroundPatients with major depressive disorder (MDD) can frequently develop new depressive episodes after remission. However, the neural mechanisms underlying the increased risk for depressive relapse remain unclear. Herein, we aimed to explore whether the specific changes to regional and inter-regional spontaneous brain activities within DMN are associated with the course of episodes in pooled MDD patients. MethodsResting-state functional magnetic resonance imaging was performed on patients with single-episode MDD (SEMDD, n = 30) and multiple-episode MDD (MEMDD, n = 54), and 71 age-, gender-, and educational level-matched healthy controls (HCs). We then accessed the differences in both the fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity by using the right precuneus as the seed among different groups. ResultsCompared to the MEMDD and HC groups, the SEMDD group exhibited increased fALFF values in the right subgenual anterior cingulate cortex and right middle temporal gyrus. Decreased fALFF values in the right thalamus in the MEMDD group were also identified relative to the SEMDD and HC group. The peak values of fALFF in the right precuneus showed a negative correlation with the number of depressive episodes across the entire pool of MDD patients. No correlation was identified between functional connectivity using the right precuneus as the seed and the number of depressive episodes for the pooled MDD patients. LimitationsMedication, a relatively small sample size, and hypothesis driven study. ConclusionsOur neuroimaging results identified depression relapse-associated neural signatures and also indicated the role of reduced emotional appraisals in the thalamus. It is now possible to believe that the regional activity not inter-regional connectivity within the DMN may be involved in the pathology of depression relapse.

The myeloarchitecture of impulsivity: premature responding in youth is associated with decreased myelination of ventral putamen.

Impulsivity has been suggested as a neurocognitive endophenotype conferring risk across a number of neuropsychiatric conditions, including substance and behavioural addictions, eating disorders, and attention deficit/hyperactivity disorder. We used a paradigm with interspecies translation validity (the four-choice serial reaction time task, 4CSRTT) to assess 'waiting' impulsivity in a youth sample (N = 99, aged 16-26 years). We collected magnetization prepared two rapid acquisition gradient echo (MP2RAGE) scans, which enabled us to measure R1, the longitudinal relaxation rate, a parameter closely related to tissue myelin content, as well as quantify grey matter volume. We also assessed inhibitory control (commission errors) on a Go/NoGo task and measured decisional impulsivity (delay discounting) using the Monetary Choice Questionnaire (MCQ). We found R1 of the bilateral ventral putamen was negatively correlated with premature responding, the index of waiting impulsivity on the 4CSRTT. Heightened impulsivity in youth was significantly and specifically associated with lower levels of myelination in the ventral putamen. Impulsivity was not associated with grey matter volume. The association with myelination was specific to waiting impulsivity: R1 was not associated with decisional impulsivity on the MCQ or inhibitory control on the Go/NoGo task. We report that heightened waiting impulsivity, measured as premature responding on the 4CSRTT, is specifically associated with lower levels of ventral putaminal myelination, measured using R1. This may represent a neural signature of vulnerability to diseases associated with excessive impulsivity and demonstrates the added explanatory power of quantifying the mesoscopic organization of the human brain, over and above macroscopic volumetric measurements.

Association of Coronary Wall Shear Stress With Atheromatous Plaque Vulnerability: A Systematic Review

Abstract Acute coronary syndromes are usually triggered by the erosion or rupture of a vulnerable coronary plaque. A vulnerable plaque (VP) is an atheromatous plaque which, after suffering different transformations, is prone to rupture causing an acute coronary event. Such a VP carries inside several biomarkers considered as “signatures of vulnerability”, which, if identified, can prompt timely initiation of therapeutic measures in order to prevent the development of an acute myocardial infarction. The most freqeuntly used techniques for identification of vulnerability markers are computed tomographic angiography (CTA), intravascular ultrasound and optical coherence tomography. Endothelial shear stress (ESS) represents a new promising biomarker associated with plaque vulnerability. Determination of ESS is nowadays possible using noninvasive imaging techniques, based on complex computational post-processing of multiple datasets extracted from CTA images and advanced computational fluid dynamics technologies. The aim of this systematic review was to evaluate the role of the coronary ESS, determined using advanced computational techniques for image post-processing, as a feature associated with CTA-derived biomarkers of atheromatous plaque vulnerability, underlining the conceptual differences between high ESS and low ESS as promotors of vulnerability.

A Hippocampal Signature of Posttraumatic Stress Disorder Vulnerability

A Hippocampal Signature of Posttraumatic Stress Disorder Vulnerability

Vulnerability Signatures with Application Classification & Performance Enhancement for IDS/IPS

Vulnerability Signatures with Application Classification & Performance Enhancement for IDS/IPS