A zinc finger transcription factor enables social behaviors while controlling transposable elements and immune response in prefrontal cortex.

The neurobiological origins of social behaviors are incompletely understood. Here we utilized synthetic biology approaches to reprogram the function of ZFP189, a transcription factor whose expression and function in rodent prefrontal cortex was previously demonstrated to be protective against stress-induced social deficits. We created novel synthetic ZFP189 transcription factors including ZFP189VPR, which activates the transcription of target genes and therefore exerts opposite functional control from the endogenous, transcriptionally repressive ZFP189WT. Following viral delivery of these synthetic ZFP189 transcription factors to mouse prefrontal cortex, we observe that ZFP189-mediated transcriptional control promotes mature dendritic spine morphology on transduced pyramidal neurons. Interestingly, inversion of ZFP189-mediated transcription in this brain area, achieved by viral delivery of synthetic ZFP189VPR, precipitates social behavioral deficits in terms of social interaction, motivation, and the cognition necessary for the maintenance of social hierarchy, without other observable behavioral deficits. RNA sequencing of virally manipulated prefrontal cortex tissues reveals that ZFP189 transcription factors of opposing regulatory function (ZFP189WT versus ZFP189VPR) have opposite influence on the expression of genetic transposable elements as well as genes that participate in adaptive immune functions. Collectively, this work reveals that ZFP189 function in the prefrontal cortex coordinates structural and transcriptional neuroadaptations necessary for complex social behaviors while regulating transposable element-rich regions of DNA and the expression of immune-related genes. Given the evidence for a co-evolution of social behavior and the brain immune response, we posit that ZFP189 may have evolved to augment brain transposon-associated immune function as a way of enhancing an animal's capacity for functioning in social groups.

Histone H1x in mouse ventral hippocampus correlates with, but does not cause behavioral adaptations to stress.

Prior research has identified differential protein expression levels of linker histone H1x within the ventral hippocampus (vHipp) of stress-susceptible versus stress-resilient mice. These mice are behaviorally classified based on their divergent responses to chronic social stress. Here, we sought to determine whether elevated vHipp H1x protein levels directly contribute to these diverging behavioral adaptations to stress. First, we demonstrate that stress-susceptible mice uniquely express elevated vHipp H1x protein levels following chronic stress. Given that linker histones coordinate heterochromatin compaction, we hypothesize that elevated levels of H1x in the vHipp may impede pro-resilience transcriptional adaptations and prevent development of the resilient phenotype following social stress. To test this, 8-10-week-old male C57BL/6J mice were randomly assigned to stressed and unstressed groups undergoing 10 days of chronic social defeat stress (CSDS) or single housing respectively. Following CSDS, mice were classified as susceptible versus resilient based on their social interaction behaviors. We synthesized a viral overexpression (OE) vector for H1x and transduced experimental mice with either H1x or control GFP within vHipp. Following viral delivery, we conducted social, anxiety-like, and memory-reliant behavior tests on distinct cohorts of mice. We found no behavioral adaptations following H1x OE compared to GFP controls in susceptible, resilient, or unstressed mice. In sum, although we confirm vHipp protein levels of H1x correlate with susceptibility to social stress, we observe no significant behavioral consequence of H1x OE. Thus, we conclude elevated levels of H1x are correlated with, but are not singularly sufficient to drive development of behavioral adaptations to stress.

A zinc finger transcription factor tunes social behaviors by controlling transposable elements and immune response in prefrontal cortex.

The neurobiological origins of social behaviors are incompletely understood. Here we utilized synthetic biology approaches to reprogram the function of ZFP189, a transcription factor whose expression and function in the rodent prefrontal cortex was previously determined to be protective against stress-induced social deficits. We created novel synthetic ZFP189 transcription factors including ZFP189VPR, which activates the transcription of target genes and therefore exerts opposite functional control from the endogenous, transcriptionally repressive ZFP189WT. Upon viral delivery of these synthetic ZFP189 transcription factors to mouse prefrontal cortex, we observe that ZFP189-mediated transcriptional control promotes mature dendritic spine morphology on transduced pyramidal neurons. Interestingly, dysregulation of ZFP189-mediated transcription in this brain area, achieved by delivery of synthetic ZFP189VPR, precipitates social behavioral deficits in terms of social interaction, motivation, and the cognition necessary for the maintenance of social hierarchy, without other observable behavioral deficits. By performing RNA sequencing in virally manipulated prefrontal cortex tissues, we discover that ZFP189 transcription factors of opposing regulatory function have opposite influence on the expression of genetic transposable elements as well as genes that participate in immune functions. Collectively, this work reveals that ZFP189 function in the prefrontal cortex coordinates structural and transcriptional neuroadaptations necessary for social behaviors by binding transposable element-rich regions of DNA to regulate immune-related genes. Given the evidence for a co-evolution of social behavior and the brain immune response, we posit that ZFP189 may have evolved to augment brain transposon-associated immune function as a way of enhancing an animal's capacity for functioning in social groups.

CREB Binding at the Zfp189 Promoter Within Medium Spiny Neuron Subtypes Differentially Regulates Behavioral and Physiological Adaptations Over the Course of Cocaine Use.

BACKGROUND: Over the course of chronic drug use, brain transcriptional neuroadaptation is thought to contribute to a change in drug use behavior over time. The function of the transcription factor CREB (cAMP response element binding protein) within the nucleus accumbens (NAc) has been well documented in opposing the rewarding properties of many classes of drugs, yet the gene targets through which CREB causally manifests these lasting neuroadaptations remain unknown. Here, we identify zinc finger protein 189 (Zfp189) as a CREB target gene that is transcriptionally responsive to acute and chronic cocaine use within the NAc of mice. METHODS: To investigate the role of the CREB-Zfp189 interaction in cocaine use, we virally delivered modified clustered regularly interspaced short palindromic repeats (CRISPR)/dCas9 constructs capable of selectively localizing CREB to the Zfp189 gene promoter in the NAc of mice. RESULTS: We observed that CREB binding to the Zfp189 promoter increased Zfp189 expression and diminished the reinforcing responses to cocaine. Furthermore, we showed that NAc Zfp189 expression increased within D1 medium spiny neurons in response to acute cocaine but increased in both D1- and D2-expressing medium spiny neurons in response to chronic cocaine. CREB-mediated induction of Zfp189 potentiated electrophysiological activity of D1- and D2-expressing medium spiny neurons, recapitulating the known effect of CREB on these neurons. Finally, targeting CREB to the Zfp189 promoter within NAc Drd2-expressing neurons, but not Drd1-expressing neurons, was sufficient to diminish cocaine-conditioned behaviors. CONCLUSIONS: Together, these findings point to the CREB-Zfp189 interaction within the NAc Drd2+ neurons as a molecular signature of chronic cocaine use that is causal in counteracting the reinforcing effects of cocaine.

A Case of Hepatitis C Related Mixed Cryoglobulinemia Syndrome.

Hepatitis C virus (HCV) is an RNA virus that preferentially infects hepatocytes and is transmitted through infected blood contact. Chronic hepatitis C can result in serious life-threatening conditions like fibrosis, cirrhosis, and liver cancer. Additionally, it can result in extrahepatic conditions including lymphoproliferative disease and mixed cryoglobulinemic vasculitis. Mixed cryoglobulinemic vasculitis occurs as a result of immune system dysfunction leading to immunoglobulin deposits into different blood vessels in the body. The main manifestations commonly seen are purpura, weakness, arthralgias. Other symptoms include peripheral neuropathy, arthritis, vasculitic skin ulcers, liver, and renal involvement. This case highlights a 57-year-old male with a medical history of substance use disorder, bilateral lower extremity ulcers, and chronic hepatitis C infection who presented with complaints of bilateral lower extremity wounds, abdominal distension, and scrotal swelling. Our patient was confirmed to have new-onset cirrhotic liver secondary to intravenous drug use, with worsening renal function. Further investigations confirmed the diagnosis of mixed cryoglobulinemia secondary to hepatitis C virus.

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. METHODS: Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. FINDINGS: The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. INTERPRETATION: This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. FUNDING: IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

Chronic stress and antidepressant treatment alter purine metabolism and beta oxidation within mouse brain and serum.

Major depressive disorder (MDD) is a complex condition with unclear pathophysiology. Molecular disruptions within limbic brain regions and the periphery contribute to depression symptomatology and a more complete understanding the diversity of molecular changes that occur in these tissues may guide the development of more efficacious antidepressant treatments. Here, we utilized a mouse chronic social stress model for the study of MDD and performed metabolomic, lipidomic, and proteomic profiling on serum plus several brain regions (ventral hippocampus, nucleus accumbens, and medial prefrontal cortex) of susceptible, resilient, and unstressed control mice. To identify how commonly used tricyclic antidepressants impact the molecular composition in these tissues, we treated stress-exposed mice with imipramine and repeated our multi-OMIC analyses. Proteomic analysis identified three serum proteins reduced in susceptible animals; lipidomic analysis detected differences in lipid species between resilient and susceptible animals in serum and brain; and metabolomic analysis revealed dysfunction of purine metabolism, beta oxidation, and antioxidants, which were differentially associated with stress susceptibility vs resilience by brain region. Antidepressant treatment ameliorated stress-induced behavioral abnormalities and affected key metabolites within outlined networks, most dramatically in the ventral hippocampus. This work presents a resource for chronic social stress-induced, tissue-specific changes in proteins, lipids, and metabolites and illuminates how molecular dysfunctions contribute to individual differences in stress sensitivity.

Fornix lesions impair place-, but not response-learning in the open-field tower maze.

The purpose of the present study was to examine hippocampal function for spatial learning in a land-based circular maze (i.e., the open-field tower maze [OFTM]). The OFTM, a task designed to be non-stressful, has been previously used to demonstrate the influence of gonadal hormones on spatial learning. Thus, determination of brain function for navigating in the OFTM provides an important extension to previous knowledge. Fornix lesions were used in the present experiment to disrupt hippocampal processing. After initial pre-training, rats received either an electrolytic fornix lesion surgery or a sham surgery. The rats from each surgical group were given either place- or response-training in the OFTM. The results showed that (1) lesioned place-learners required more trials than sham place-learners to solve the OFTM and (2) lesioned response-learners solved the OFTM at the same rate as sham response-learners. Our findings support the hypothesis that the hippocampus is necessary for place-, but not response-learning in the OFTM task. The OFTM is an appetitive task that does not depend on a choice between restricted directions that a rat would be required to make in a T-maze or a radial arm-maze, and does not include aversive components inherent to a Morris Water Maze or Barnes Maze. Thus, the OFTM can be used to investigate the manipulations of hippocampus-dependent spatial learning without confounding variables related to an animal's stress level.

Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures.

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) for sequential decoding of EEG events with deep learning-based post-processing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts, and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application.

Applications of UBMs and I-vectors in EEG subject verification.

The processing of electroencephalograms (EEGs) is a growing field where mature speech processing techniques are able to rapidly progress development and understanding of the associated neuroscience. I-vectors and Joint Factor Analysis (JFA), along with their foundational universal background models (UBMs) have progressed to a level of understanding that makes them prime for transition to the EEG community. To prove the capability of these techniques they are tested against two contrasting EEG data sets, PhysioNet's EEG Motor Movement/Imagery Dataset and the Temple University Hospital EEG Corpus, to highlight the effectiveness of the techniques with minimal domain knowledge modifications. The initial results, presented as equal error rates as low as 20%, support the development of these techniques as a viable approach to addressing subject verification within and across subjects.

Detecting paroxysmal coughing from pertussis cases using voice recognition technology.

BACKGROUND: Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. METHODS: We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. RESULTS: After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. CONCLUSION: Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.

Fatigue estimation using voice analysis.

In the present article, we present a means to remotely and transparently estimate an individual's level of fatigue by quantifying changes in his or her voice characteristics. Using Voice analysis to estimate fatigue is unique from established cognitive measures in a number of ways: (1) speaking is a natural activity requiring no initial training or learning curve, (2) voice recording is a unobtrusive operation allowing the speakers to go about their normal work activities, (3) using telecommunication infrastructure (radio, telephone, etc.) a diffuse set of remote populations can be monitored at a central location, and (4) often, previously recorded voice data are available for post hoc analysis. By quantifying changes in the mathematical coefficients that describe the human speech production process, we were able to demonstrate that for speech sounds requiring a large average air flow, a speaker's voice changes in synchrony with both direct measures of fatigue and with changes predicted by the length of time awake.