Faculty Bibliography

INTRODUCTION/BACKGROUND:Disturbances in microvascular flow dynamics are hypothesized to precede the symptomatic phase of Alzheimer's disease (AD). However, evidence in presymptomatic AD remains elusive, underscoring the need for therapies targeting these early vascular changes. METHODS:We employed a multimodal approach, combining in vivo optical imaging, molecular techniques, and ex vivo magnetic resonance imaging, to investigate early capillary dysfunction in C57BL/6-Tg(Thy1-APPSwDutIowa)BWevn/Mmjax (Tg-SwDI) mice without memory impairment. We also assessed the efficacy of carbonic anhydrase inhibitors (CAIs) in preventing capillary flow disturbances. RESULTS:Our study revealed capillary flow disturbances associated with alterations in capillary morphology, adhesion molecule expression, and amyloid beta (Aβ) load in 9- to 10-month-old Tg-SwDI mice without memory impairment. CAI treatment ameliorated these capillary flow disturbances, enhanced oxygen availability, and reduced Aβ load. DISCUSSION/CONCLUSIONS:These findings underscore the importance of capillary flow disturbances as early biomarkers in presymptomatic AD and highlight the potential of CAIs for preserving vascular integrity in the early stages of AD. HIGHLIGHTS/CONCLUSIONS:Uncovered early capillary dysfunction in a presymptomatic Alzheimer's disease (AD) mouse model. Evidence linking capillary stalls and capillary dysfunction with oxygen delivery issues in AD. Novel use of carbonic anhydrase inhibitors to prevent early capillary flow disturbances in AD.

The relative ease of generation and proliferation of omics datasets has moved considerably faster than the effective dissemination of these data to the scientific community. Despite advancements in making raw data publicly available, many researchers struggle with data analysis and integration. We propose sharing analyzed data through user-friendly platforms to enhance accessibility. Here, we present a free, online tool, for sharing basic omics data in a searchable and user-friendly format. Importantly, it requires no coding or prior computational knowledge to build-only a data spreadsheet. Overall, this tool facilitates the exploration of transcriptomic, proteomic, and metabolomics data, which is crucial for understanding glial diversity and function. This initiative underscores the importance of accessible molecular data in advancing neuroscience research.

Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach ("MF-TFCCA") to assess the strength and driving frequency of spectral information flow. We validate the approach with extensive computer simulations on MF time series under various interaction conditions and further assess statistical significance of the estimate with surrogate data. In various benchmark comparisons, MF-TFCCA consistently outperforms the traditional parametric MF-VAR model in both computational efficiency and detection accuracy, and recovers the dominant driving frequencies. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient nonparametric approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.

Active avoidance is a core behavior for human coping, and its excess is common across psychiatric diseases. The decision to actively avoid a threat is influenced by cost and reward. Yet, threat, avoidance, and reward have been studied in silos. We discuss behavioral and brain circuits of active avoidance and the interactions with fear and threat. In addition, we present a neural toggle switch model enabling fear-to-anxiety transition and approaching reward vs. avoiding harm decision. To fully comprehend how threat, active avoidance, and reward intersect, it is paramount to develop one shared experimental approach across phenomena and behaviors, which will ultimately allow us to better understand human behavior and pathology.

BACKGROUND:The diagnosis and treatment of tumors often depend on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. METHODS:Here, we introduce Ultra-Rapid droplet digital PCR (UR-ddPCR), a technology that achieves the fastest measurement, to date, of mutation burdens in tissue samples, from tissue to result in 15 min. Our workflow substantially reduces the time from tissue biopsy to molecular diagnosis and provides a highly accurate means of quantifying residual tumor infiltration at surgical margins. FINDINGS/RESULTS: = 0.995). CONCLUSIONS:The technology and workflow developed here enable intraoperative molecular-genetic assays with unprecedented speed and sensitivity. We anticipate that our method will facilitate novel point-of-care diagnostics and molecularly guided surgeries that improve clinical outcomes. FUNDING/BACKGROUND:This study was funded by the National Institutes of Health and NYU Grossman School of Medicine institutional funds. Reagents and instruments were provided in kind by Bio-Rad.

The capacity of the brain to compensate for insults during development depends on the type of cell loss, whereas the consequences of genetic mutations in the same neurons are difficult to predict. We reveal powerful compensation from outside the mouse cerebellum when the excitatory cerebellar output neurons are ablated embryonically and demonstrate that the main requirement for these neurons is for motor coordination and not basic learning and social behaviors. In contrast, loss of the homeobox transcription factors Engrailed1/2 (EN1/2) in the cerebellar excitatory lineage leads to additional deficits in adult learning and spatial working memory, despite half of the excitatory output neurons being intact. Diffusion MRI indicates increased thalamo-cortico-striatal connectivity in En1/2 mutants, showing that the remaining excitatory neurons lacking En1/2 exert adverse effects on extracerebellar circuits regulating motor learning and select non-motor behaviors. Thus, an absence of cerebellar output neurons is less disruptive than having cerebellar genetic mutations.

Cis-regulatory elements (CREs), such as enhancers and promoters, are fundamental regulators of gene expression and, across different cell types, the MYC locus utilizes a diverse regulatory architecture driven by multiple CREs. To better understand differences in CRE function, we perform pooled CRISPR inhibition (CRISPRi) screens to comprehensively probe the 2.8 Mb topologically-associated domain containing MYC in 6 human cancer cell lines with nucleotide resolution. We map 32 CREs where inhibition leads to changes in cell growth, including 8 that overlap previously identified enhancers. Targeting specific CREs decreases MYC expression by as much as 60%, and cell growth by as much as 50%. Using 3-D enhancer contact mapping, we find that these CREs almost always contact MYC but less than 10% of total MYC contacts impact growth when silenced, highlighting the utility of our approach to identify phenotypically-relevant CREs. We also detect an enrichment of lineage-specific transcription factors (TFs) at MYC CREs and, for some of these TFs, find a strong, tumor-specific correlation between TF and MYC expression not found in normal tissue. Taken together, these CREs represent systematically identified, functional regulatory regions and demonstrate how the same region of the human genome can give rise to complex, tissue-specific gene regulation.

Monosodium glutamate (MSG) has become one of the most widely used food additives in the global food supply. Although it has been classified for decades as a food ingredient that is generally recognized as safe, concerns about the health impacts of chronic MSG use, especially its potential effect on weight, are still ongoing. This comprehensive review summarizes the available human and animal evidence, highlighting potential mechanisms linking MSG use to weight gain or obesity, and discusses challenges and future research directions. Because of MSG intake worldwide as well as hidden MSG in food labeling, there is a pressing need for a mechanistic understanding of the health impacts of MSG use especially on weight. To generate robust scientific evidence and to clarify public concerns, rigorous mechanistic studies and randomized controlled clinical trials are warranted.

For COVAIL recipients of a coronavirus disease 2019 (COVID-19) Sanofi booster vaccine, neutralizing antibody titers were assessed as a correlate of risk (CoR) of COVID-19. Peak and exposure-proximal titers were inverse CoRs with covariate-adjusted hazard ratios (95% confidence intervals) 0.30 (0.11, 0.78) and 0.25 (0.07, 0.85) per 10-fold increase in weighted average titer.

Inhibitory interneurons are highly heterogeneous circuit elements often characterized by cell biological properties, but how these factors relate to specific roles underlying complex behavior remains poorly understood. Using chronic silicon probe recordings, we demonstrate that distinct interneuron groups perform different inhibitory roles within HVC, a song production circuit in the zebra finch forebrain. To link these functional subtypes to molecular identity, we performed two-photon targeted electrophysiological recordings of HVC interneurons followed by post hoc immunohistochemistry of subtype-specific markers. We find that parvalbumin-expressing interneurons are highly modulated by sensory input and likely mediate auditory gating, whereas a more heterogeneous set of somatostatin-expressing interneurons can strongly regulate activity based on arousal. Using this strategy, we uncover important cell-type-specific network functions in the context of an ethologically relevant motor skill.