Biology subjects

Nadeem, S.

Publications and source records attributed to Nadeem, S..

14 recordsLinked to original sources

bioRxiv2026

A dominant frontoparietal beta oscillatory brain state in the days after psilocybin and 5-MeO-DMT

Background. Serotonergic psychedelics show promise for treating psychiatric disorders, with symptom improvements lasting for weeks after a single dose. Clarifying the neural basis of these effects would benefit from an identification of empirical biomarkers of such lasting shifts in brain function. Resting state EEG offers a rapid (<5 minute), low-cost window into functional brain networks. However, connectivity is not static, but cycles between recurring semi-stable patterns that vary across frequency bands. Here we employed a dynamic function connectivity (dFC) framework to identify frequency-specific connectivity states and examine how they change in the weeks following psychedelic use. Methods. We collected resting-state EEG from individuals who had used one of two serotonergic psychedelic subclasses within the prior three weeks, psilocybin/LSD (typical; n=14) or 5-MeO-DMT (atypical; n=12), and age- and sex-matched controls (n=16). Frequency-band-specific spatial connectivity states (phase-lag index) were estimated across the full sample (5 per band). Groups were compared on proportion and dwell-time (per state) and state-to-state transitions. Results. A right frontoparietally-distributed beta synchrony state was dominant after both typical and atypical psychedelics use (proportion/dwell-time). This effect correlated with the number of days since using psychedelics. A globally-distributed theta-band state was prominent in recent users of typical psychedelics but occurred less often in atypical users. In contrast, neural entropy (Lempel-Ziv complexity; known to increase acutely during psychedelic dosing) was not altered in recent users of either subclass. Conclusion. These results reveal a beta-band signature of altered neural dynamics in the week following a psychedelic dose, consistent with a relaxation of brain network hierarchy after psychedelics.

neuroscience↗

bioRxiv2026

Glucose selectively drives a rapid oxidative burst and immunometabolic reprogramming in human neutrophils during Mycobacterium tuberculosis infection

Neutrophil functions have been linked to tuberculosis (TB)-associated tissue damage; however, the mechanisms driving immunopathology in the human TB lung remain poorly understood, due partly to the scarcity of human tissue for study. Here, we examine the metabolic and bioenergetic reprogramming of human neutrophils in response to Mycobacterium tuberculosis (Mtb) infection. In human necrotic TB granulomas, levels of NETosis-associated proteins are increased and co-localize with GLUT3, linking nutrient uptake to tissue damage. In vitro, Mtb elicits an immediate, contact-dependent oxidative burst in human neutrophils, and the magnitude of this response is carbon source-dependent. Glucose enables the most robust responses, indicating that glucose metabolism is a key driver of neutrophil-mediated inflammatory damage during TB. Mtb-induced responses are distinct from those induced by PMA, non-tuberculous mycobacteria, or other pathogenic intracellular bacteria, and are mediated through multiple neutrophil surface receptors. Notably, our data show that while the oxidative burst is carbon source-dependent, cytokine production is not. Further, Mtb infection reprograms neutrophil metabolism from glycolysis to the pentose phosphate pathway (PPP), generating NADPH required for the oxidative burst. Inhibiting G6PD, NADPH oxidase, or PAD4 significantly reduces this response, highlighting the PPP as a promising host target for mitigating TB immunopathology.

microbiology↗

bioRxiv2024

A lasting impact of serotonergic psychedelics on visual processing and behavior

Serotonergic psychedelics (e.g., psilocybin, LSD) have potential to treat psychiatric disorders, with therapeutic effects lasting days to weeks after a single dose. Prominent theories suggest that psychedelics have a lasting effect on hierarchical brain circuits, reducing top-down influence on information processing to facilitate an unbiased, bottom-up reassessment of the world, but direct and concrete evidence for such an effect is lacking. Here we directly tested this hypothesis in both humans and mice, assessing predictive processing in the fronto-visual system in the days after a single psychedelic exposure. Individuals who recently (<3 weeks) used 5-HT2AReceptor agonist psychedelics (psilocybin, LSD) were assessed via electroencephalography (EEG) and electrooculography recordings during a saccadic prediction task and compared to age- and sex-matched non-users. Compared to non-users, recent psychedelic users produced fewer fast saccades and less suppression of EEG delta/theta power to predictively presented stimuli, pointing to a disruption of predictive processing. These changes correlated with time since psychedelic use and were replicated in a second cohort taking a different serotonergic psychedelic (5-MeO-DMT). Direct recordings of primary visual cortex (V1) in mice administered psilocybin (1 mg/kg) evinced a similar loss of predictive suppression 24-hrs after the dose. This coincided with weakened top-down modulation of V1 from anterior cingulate area (ACa), a subregion of medial prefrontal cortex, along with clear spine growth in ACa neurons that project to V1. These results suggest that psychedelic-induced neural plasticity serves to reorganize feedback circuits in the cortex and relax top-down influence on bottom-up sensory processing - an effect that persists beyond the acute exposure period and may underlie a therapeutic window.

neuroscience↗

bioRxiv2023

Detection of Mycobacterium tuberculosis in human tissue via RNA in situ hybridization

RationaleAccurate TB diagnosis is hampered by the variable efficacy of the widely-used Ziehl-Neelsen (ZN) staining method to identify Mycobacterium tuberculosis (Mtb) acid-fast bacilli (AFB). Here, we sought to circumvent this current limitation through direct detection of Mtb mRNA. ObjectivesTo employ RNAscope to determine the spatial distribution of Mtb mRNA within tuberculous human tissue, to appraise ZN-negative tissue from confirmed TB patients, and to provide proof-of-concept of RNAscope as a platform to inform TB diagnosis and Mtb biology. MethodsWe examined ante- and postmortem human TB tissue using RNAscope to detect Mtb mRNA and a dual ZN/immunohistochemistry staining approach to identify AFB and bacilli producing antigen 85B (Ag85B). Measurements and main resultsWe adapted RNAscope for Mtb and identified intact and disintegrated Mtb bacilli and intra- and extracellular Mtb mRNA. Mtb mRNA was distributed zonally within necrotic and non-necrotic granulomas. We also found Mtb mRNA within, and adjacent to, necrotic granulomas in ZN-negative lung tissue and in Ag85B-positive bronchial epithelium. Intriguingly, we observed accumulation of Mtb mRNA and Ag85B in the cytoplasm of host cells. Notably, many AFB were negative for Ag85B staining. Mtb mRNA was observed in ZN-negative antemortem lymph node biopsies. ConclusionsRNAscope has diagnostic potential and can guide therapeutic intervention as it detects Mtb mRNA and morphology in ZN-negative tissues from TB patients, and Mtb mRNA in ZN-negative antemortem biopsies, respectively. Lastly, our data provide evidence that at least two phenotypically distinct populations of Mtb bacilli exist in vivo.

microbiology↗

bioRxiv2022

NAD(H) homeostasis is essential for host protection mediated by glycolytic myeloid cells in tuberculosis

Mycobacterium tuberculosis (Mtb) disrupts glycolytic flux in infected myeloid cells through an unclear mechanism. Flux through the glycolytic pathway in myeloid cells is inextricably linked to the availability of NAD+, which is maintained by NAD+ salvage and lactate metabolism. Using lung tissue from tuberculosis (TB) patients and myeloid deficient LDHA (LdhaLysM-/-) mice, we demonstrate that glycolysis in myeloid cells is essential for protective immunity in TB. Glycolytic myeloid cells are essential for the early recruitment of multiple classes of immune cells and the protective effects of IFN{gamma}. We identified NAD+ depletion as central to the glycolytic inhibition caused by Mtb. Lastly, we show that the NAD+ precursor nicotinamide exerts a host-directed, antimycobacterial effect, and that nicotinamide prophylaxis and treatment reduces Mtb lung burden in vivo. These findings provide new insight into how Mtb alters host metabolism through perturbation of NAD(H) homeostasis and reprogramming of glycolysis, highlighting this pathway as a potential therapeutic target.

microbiology↗

bioRxiv2021

Water-soluble tocopherol derivatives inhibit SARS-CoV-2 RNA-dependent RNA polymerase

The recent emergence of a novel coronavirus, SARS-CoV-2, has led to the global pandemic of the severe disease COVID-19 in humans. While efforts to quickly identify effective antiviral therapies have focused largely on repurposing existing drugs1-4, the current standard of care, remdesivir, remains the only authorized antiviral intervention of COVID-19 and provides only modest clinical benefits5. Here we show that water-soluble derivatives of -tocopherol have potent antiviral activity and synergize with remdesivir as inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Through an artificial-intelligence-driven in silico screen and in vitro viral inhibition assay, we identified D--tocopherol polyethylene glycol succinate (TPGS) as an effective antiviral against SARS-CoV-2 and {beta}-coronaviruses more broadly that also displays strong synergy with remdesivir. We subsequently determined that TPGS and other water-soluble derivatives of -tocopherol inhibit the transcriptional activity of purified SARS-CoV-2 RdRp and identified affinity binding sites for these compounds within a conserved, hydrophobic interface between SARS-CoV-2 nonstructural protein 7 and nonstructural protein 8 that is functionally implicated in the assembly of the SARS-CoV-2 RdRp6. In summary, we conclude that solubilizing modifications to -tocopherol allow it to interact with the SARS-CoV-2 RdRp, making it an effective antiviral molecule alone and even more so in combination with remdesivir. These findings are significant given that many tocopherol derivatives, including TPGS, are considered safe for humans, orally bioavailable, and dramatically enhance the activity of the only approved antiviral for SARS-CoV-2 infection7-9.

microbiology↗

bioRxiv2021

DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Quantification

Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists semi-quantitative scoring. The code, the pre-trained models, along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.

bioinformatics↗

bioRxiv2021

ImPartial: Partial Annotations for Cell Instance Segmentation

Segmenting noisy multiplex spatial tissue images constitutes a challenging task, since the characteristics of both the noise and the biology being imaged differs significantly across tissues and modalities; this is compounded by the high monetary and time costs associated with manual annotations. It is therefore imperative to build algorithms that can accurately segment the noisy images based on a small number of annotations. Recently techniques to derive such an algorithm from a few scribbled annotations have been proposed, mostly relying on the refinement and estimation of pseudo-labels. Other techniques leverage the success of self-supervised denoising as a parallel task to potentially improve the segmentation objective when few annotations are available. In this paper, we propose a method that augments the segmentation objective via self-supervised multi-channel quantized imputation, meaning that each class of the segmentation objective can be characterized by a mixture of distributions. This approach leverages the observation that perfect pixel-wise reconstruction or denoising of the image is not needed for accurate segmentation, and introduces a self-supervised classification objective that better aligns with the overall segmentation goal. We demonstrate the superior performance of our approach for a variety of cancer datasets acquired with different highly-multiplexed imaging modalities in real clinical settings. Code for our method along with a benchmarking dataset is available at https://github.com/natalialmg/ImPartial.

bioengineering↗

bioRxiv2019

Optimal Mass Transport for Robust Texture Analysis

The emerging field of radiomics, which consists of transforming standard-of-care images to quantifiable scalar statistics, endeavors to reveal the information hidden in these macroscopic images. This field of research has found different applications ranging from phenotyping and tumor classification to outcome prediction and treatment planning. Texture analysis, which often consists of reducing spatial texture matrices to summary scalar features, has been shown to be important in many of the latter applications. However, as pointed out in many studies, some of the derived texture statistics are strongly correlated and tend to contribute redundant information; and are also sensitive to the parameters used in their computation, e.g., the number of gray intensity levels. In the present study, we propose first to consider texture matrices, with an emphasis on gray-level co-occurrence matrix (GLCM), as a non-parametric multivariate objects. The proposed modeling approach avoids evaluating redundant and strongly correlated features and also prevents the feature processing steps. Then, via the Wasserstein distance from optimal mass transport theory, we propose to compare these spatial objects to identify computerized tomography slices with dental artifacts in head and neck cancer. We demonstrate the robustness of the proposed classification approach with respect to the GLCM extraction parameters and the size of the training set. Comparisons with the random forest classifier, which is constructed on scalar texture features, demonstrates the efficiency and robustness of the proposed algorithm.

bioengineering↗

bioRxiv2019

Glymphatic Optimal Mass Transport with Lagrangian Workflow Reveals Advective and Diffusion Driven Solute Transport

The presence of advection in neuropil is contested and solute transport is claimed to occur by diffusion only. To address this controversy, we implemented a regularized version of the optimal mass transport (rOMT) problem, wherein the advection/diffusion equation is the only a priori assumption required. rOMT analysis with a Lagrangian perspective of glymphatic system (GS) transport revealed that solute speed was faster in cerebrospinal fluid (CSF) compared to grey and white matter. rOMT analysis also demonstrated 2-fold differences in regional particle speed within the brain parenchyma. Collectively, these results imply that advective transport dominates in CSF while diffusion and advection both contribute to transport in parenchyma. In rats with chronic hypertension, solute transport in perivascular spaces (PVS) and PVS-to-tissue transfer was slower compared to normotension. Thus, the analytical framework of rOMT provides novel insights in local variation and dynamics of GS transport that may have implications for neurodegenerative diseases.

physiology↗

bioRxiv2019

Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

Ricci curvature is a method for measuring the robustness of networks. In this work, we use Ricci curvature to measure robustness of brain networks affected by autism spectrum disorder (ASD). Subjects with ASD are given a stem cell infusion and are imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantify both local and global changes in the brain networks correlated with the infusion. Our results find changes in regions associated with ASD that were not detected via traditional brain network analysis.

neuroscience↗

bioRxiv2019

Hierarchical Network Exploration using Gaussian MixtureModels

We present a framework based on optimal mass transport to construct, for a given network, a reduction hierarchy which can be used for interactive data exploration and community detection. Given a network and a set of numerical data samples for each node, we calculate a new computationally-efficient comparison metric between Gaussian Mixture Models, the Gaussian Mixture Transport distance, to determine a series of merge simplifications of the network. If only a network is given, numerical samples are synthesized from the network topology. The method has its basis in the local connection structure of the network, as well as the joint distribution of the data associated with neighboring nodes.\n\nThe analysis is benchmarked on networks with known community structures. We also analyze gene regulatory networks, including the PANTHER curated database and networks inferred from the GTEx lung and breast tissue RNA profiles. Gene Ontology annotations from the EBI GOA database are ranked and superimposed to explain the salient gene modules. We find that several gene modules related to highly specific biological processes are well-coordinated in such tissues. We also find that 18 of the 50 genes of the PAM50 breast-tumor prognostic signature appear among the highly coordinated genes in a single gene module, in both the breast and lung samples. Moreover these 18 are precisely the subset of the PAM50 recently identified as the basal-like markers.

bioinformatics↗

bioRxiv2019

Optimal Mass Transport Kinetic Modeling for Head and Neck DCE-MRI: Initial Analysis

Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and under-went DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ({Phi}F) and backward flux ({Phi}B). OMT-derived {Phi}F was compared with the volume transfer constant for CA, Ktrans, derived from the Extended Tofts Model (ETM). The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 {+/-} 0.0091 (min-1) whereas the mean advective component was 0.0052{+/-}0.0086 (min-1) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67-18.76% and 12.76-30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ({Phi}F) is comparable with the ETM-derived Ktrans. This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

systems biology↗

bioRxiv2018

Topological Data Analysis of PAM50 and 21-Gene Breast Cancer Assays

We introduce a classification of breast tumors into 7 classes which are more clearly defined by interpretable mRNA signatures along the PAM50 gene set than the 5 traditional PAM50 intrinsic subtypes. Each intrinsic subtype is partially concordant with one of our classes, and the 2 additional classes correspond to division of the classes concordant with the Luminal B and the Normal intrinsic subtypes along expression of the Her2 gene group. Our Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression (specificity: 80.8% and sensitivity: 82.8%), and exhibits the best overall survival (89.6% at 5 years). Though Luminal A tumors are traditionally considered the least aggressive, our analysis shows that only the Luminal A tumors which are now classified as myoepithelial have this phenotype, while tumors in our luminal class (concordant with Luminal A) may be more aggressive than previously thought. We also find that patients with Basal tumors surviving to 48 months exhibit favorable survival rates when certain markers for B-lymphocytes are present and poor survival rates when they are absent, which is consistent with recent findings.

cancer biology↗