Biology subjects

Tomer, R.

Publications and source records attributed to Tomer, R..

At least 19 recordsLinked to original sources

bioRxiv2025

AntiFP2: Genome and Metagenome-Wide Prediction of Antifungal Proteins

The identification of antifungal proteins (AFPs) is crucial for understanding microbial interactions and facilitating the discovery of antifungals on a large scale from genomic and metagenomic data. Most existing computational tools are developed for predicting antifungal peptides rather than proteins. To address this limitation, we developed AntiFP2. A manually curated dataset of experimentally validated AFPs was used to train and develop prediction models using cross-validation techniques. The ensemble strategy, which combined ESM2, BLAST, and MERCI, achieved the best results across independent validation. Beyond prediction, we implemented a complete pipeline for genome- and metagenome-wide AFP screening. AntiFP2 is freely available as a web server, standalone package, and Docker container, enabling scalable and reproducible antifungal protein discovery.

bioinformatics↗

bioRxiv2025

In vitro reconstitution of hippocampal cell assemblies

Three-dimensional neuronal cultures, such as brain organoids, have proven effective for in vitro modeling of brain development and disease. However, it remains unclear whether these systems can intrinsically generate the spatiotemporal network motifs that support cognitive function, particularly given their lack of sensory input. To address this, we developed a 3D long-range modular neuronal network approach to test whether dissociated embryonic hippocampal cells can self-organize into neuronal manifolds capable of generating network dynamics analogous to the in vivo hippocampal cell assemblies implicated in cognitive function. Using all-optical interrogation, we demonstrate in vitro recapitulation of cell assemblies and their hallmark features, including recurring sequential activation, hierarchical chaining, multi-day stability, resilience to perturbation, and attractor-like pattern completion. We further investigated the effects of acute ketamine exposure on these assemblies, revealing network-wide reconfiguration reminiscent of in vivo responses. Finally, we show that human iPSC-derived neurons can similarly self-organize into modular network architectures. Together, these findings are consistent with an intrinsically "inside-out" view of the brain, rather than a tabula rasa model in which sensory input is required for the emergence of cognitive network motifs, and open new avenues for in vitro modeling of network-level mechanisms relevant to neuropsychiatric disorders.

neuroscience↗

bioRxiv2025

Hybrid Solid-Liquid Optics Enable Scalable, High-Resolution, Multi-Immersion Light-Sheet Microscopy

Modern biology increasingly depends on data-driven discovery, requiring scalable and affordable high-content 3D imaging across molecular to organ scales. Although tissue clearing, expansion microscopy, and light-sheet microscopy (LSM) enable subcellular-resolution imaging of intact specimens, their scalability remains fundamentally limited by detection optics: immersion objectives deliver high-resolution, aberration-free imaging but with short working distances, high cost, and multi-immersion incompatibility, while air objectives offer long working distances and portability at lower cost but suffer from severe aberrations and reduced photon collection when imaging immersed samples. We introduce the Hybrid Solid-Liquid Immersion Lens (HySIL) framework, which pairs an off-the-shelf solid optical component with a refractive index-matched liquid to precompensate aberrations and enhance resolution. Building on HySIL, we developed SCOPE and Super-SCOPE, objective-agnostic imaging devices achieving submicron lateral resolution (<0.75 {micro}m) across centimeter-scale samples using inexpensive air objectives with >30 mm working distances. Integration with a low-cost LSM platform yielded a compact, scalable system demonstrated for multi-immersion, multi-color, subcellular-resolution mapping of cleared or expanded mouse, salamander, and cavefish brains, human iPSC-derived organoids, and 3D histopathology of breast tissue. HySIL and SCOPE establish an accessible foundation for scalable, high-resolution volumetric imaging, advancing data-driven biological discovery.

bioengineering↗

bioRxiv2025

PEPlife2: A Updated Repository of the Half-life of Peptides

This manuscript presents an updated version of PEPlife, a manually curated database that provides comprehensive information on peptide half-life. The updated version, PEPlife2, includes 4,412 entries, compared to 2,229 entries in the previous version. Each entry contains detailed information that include experimental methods used to determine half-life, chemical modifications, biological activity, routes of administration, and other relevant data. The database encompasses 402 proteins and 1,781 peptides, derived from 105 unique assays. In addition to natural peptide sequences, PEPlife2 includes cyclic peptides and chemically modified peptides, such as those with N- and C-terminal modifications. To provide structural insights, peptide and protein structures are sourced from the Protein Data Bank (PDB) or predicted using PEPstrMOD. To support the scientific community, we have developed a user-friendly interface and integrated advanced analytical tools, including BLAST, Smith-Waterman, GGSEARCH, CLUSTALW, and MUSTANG. The updated database is accessible at: https://webs.iiitd.edu.in/raghava/peplife2/. Authors BiographyO_LIUrooj Alam is currently pursuing a Masters degree in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIKunal Chaudhary is working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIRitu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2025

AntiCP3: Prediction of Anticancer Proteins Using Evolutionary Information from Protein Language Models

A number of computational methods have been developed in the past for predicting anticancer peptides, including AntiCP and AntiCP2 from our group. While these tools have been widely used by the scientific community, they are not suitable for predicting anticancer proteins. In this study, we present AntiCP3, the first dedicated method for the prediction of anticancer proteins. All models were trained using five-fold cross-validation and evaluated on an independent dataset not used during training. Our initial analysis revealed distinct compositional differences between anticancer peptides and proteins, justifying the need for a separate prediction framework. We first implemented similarity-based approaches, which yielded moderate performance. Subsequently, we developed machine learning and deep learning models using conventional protein features, achieving a maximum AUC of 0.72. The performance improved to an AUC of 0.79 with the incorporation of evolutionary information through PSSM profiles. Further enhancement was observed when embeddings from a fine-tuned protein language model ESM-t33 were used, leading to a best AUC of 0.90. Finally, a hybrid approach combining BLAST with our machine learning model achieved an AUC of 0.91. To facilitate the scientific community, we have implemented AntiCP3 as both a web server and standalone software for the prediction of anticancer proteins (https://webs.iiitd.edu.in/raghava/anticp3/). We have also deployed our model at hugging face https://huggingface.co/raghavagps-group/anticp3. Highlights Existing methods have been developed for predicting anticancer peptides. AntiCP3 is specifically optimised for predicting anticancer proteins. PSI-BLAST used to obtain evolutionary information in form of PSSM profile. Hybrid methods developed using alignment free and alignment based approach. A web server and standalone tool have been developed to assist the community.

bioinformatics↗

bioRxiv2025

IL4Pred2: Prediction of Interleukin-4 Inducing Peptides in Human and Mouse

In 2013, our group developed IL4pred, a host-independent method for predicting interleukin-4 (IL-4) inducing peptides, which has been widely used by the scientific community. In this study, we present a second-generation method, IL4Pred2, which is a host-specific approach designed to predict IL-4 inducing peptides separately for human and mouse hosts. All models were trained, tested, and benchmarked on experimentally validated data obtained from the IEDB. We employed a wide range of state-of-the-art techniques for prediction, including similarity-based approaches, machine learning, deep learning methods, and large language models. Our best model achieved highest AUC 0.80 with MCC 0.45 for human and AUC 0.82 with MCC 0.50 for mouse on independent set of main datasets. All models were trained, test and optimized on training dataset. We validate our final model on an independent dataset which is not used in training or hyperparameter optimization of models. In this study, we developed models on three types of datasets called Main, Alternate1 and Alternate2 for predicting IL-4 inducing peptides. This abstract show performance of our models on Main dataset, performance of models on other datasets have been discussed in manuscript. One of the major objectives of this study is to facilitate research community in the area of immunotherapy and vaccine development. Thus, we developed, a web server and standalone software IL4pred2 for predicting, designing and scanning IL-4 inducing peptides in proteins (https://webs.iiitd.edu.in/raghava/il4pred2/ and https://github.com/raghavagps/il4pred2).

bioinformatics↗

bioRxiv2025

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

Rheumatoid arthritis (RA) is an autoimmune disorder in which the immune system mounts an abnormal response to self-antigens, leading to chronic inflammation and joint damage. Therefore, identifying antigenic regions in a protein that trigger RA is crucial for developing protein-based therapeutics. In this study, we developed models for predicting RA-inducing peptides using a dataset comprising of 291 experimentally confirmed RA-inducing peptides and 165 RA non-inducing peptides. Our initial analysis revealed that certain residues, such as glycine, proline, and tyrosine, are significantly enriched in RA-inducing peptides. While alignment-based techniques like BLAST and MERCI offered high precision, they suffered from limited coverage. We developed machine/deep learning based prediction and obtained highest performance (AUC = 0.75) using XGboost on an independent dataset. We also developed prediction methods using large language models and achieved highest performance (AUC 0.72) using ProtBERT. Our ensemble model achieved highest performance (AUC = 0.80 & MCC = 0.45) on an independent dataset that combine XGBoost and MERCI-derived motifs. All models were rigorously evaluated on an independent dataset not used during training or testing of models. This study will be valuable for assessing the risk of proteins used in probiotics, genetically modified foods, and protein-based therapeutics. Our most effective approach has been implemented in RAIpred, a web server and standalone software tool for predicting and scanning RA-inducing peptides. (https://webs.iiitd.edu.in/raghava/raipred/). HighlightsO_LIRheumatoid arthritis (RA), an incurable chronic joint disorder with diverse systemic complications. C_LIO_LIAn attempt to identify antigenic regions in a protein which trigger this severe disease. C_LIO_LIUtilizing sequence composition based features for developing models. C_LIO_LIImplementation of ML, DL and LLM based models for prediction of RA-inducing peptides. C_LIO_LIDevelopment of webserver, standalone, pypi and GitHub package for users. C_LI

bioinformatics↗

bioRxiv2025

Identification of Multiple Prognostic Biomarker Sets for Risk Stratification in SKCM

Most of the existing studies have identified a single profile of prognostic biomarkers for predicting high-risk cancer patients using transcriptomics data. In this study, we propose multiple distinct sets of prognostic biomarkers for predicting high-risk Skin Cutaneous Melanoma (SKCM) patients. Our primary analysis reveals that the expression of certain genes, such as CREG1, PCGF5, and VPS13C, strongly correlates with overall survival (OS) in SKCM patients. We developed machine learning-based prognostic models to predict 1-, 3-, and 5-year overall survival using gene expression profiles. State-of-the-art feature selection techniques were employed to identify the first prognostic biomarkers set consisting of 20 genes. Machine learning models built to predict high-risk patients using this set of biomarkers and achieved an AUC of 0.90 and a Kappa of 0.58 in distinguishing high-risk SKCM patients. Similarly, a second independent set of 20 prognostic genes was identified, with no overlap with the first set. The best model trained on this second set achieved an AUC of 0.89 with a Kappa of 0.56, while the fifth biomarker set achieved the highest performance with an AUC of 0.91 and a Kappa of 0.64. This process was repeated to obtain a total of seven distinct prognostic biomarker sets, each containing 20 unique genes. The predictive performance of these models varied between 0.84 and 0.91 for AUC and 0.48 to 0.64 for Kappa on the validation dataset. These findings demonstrate that it is possible to identify multiple independent sets of prognostic biomarkers, each capable of accurately predicting high-risk SKCM patients. HighlightsO_LIPrediction of high-risk SKCM patients from transcriptomics data C_LIO_LIIdentification of multiple set of prognostic biomarkers C_LIO_LIIdentification of set of biomarkers using feature selection technique C_LIO_LIDevelopment of machine learning based models for prediction C_LIO_LIPathway analysis of selected set of biomarker genes C_LI

bioinformatics↗

bioRxiv2025

NfκBin: A Machine Learning based Method for Screening Nf-κB Inhibitors.

Nuclear Factor kappa B (NF-{kappa}B) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-{kappa}B-related diseases, we developed a computational approach to predict drugs capable of inhibiting NF-{kappa}B signaling pathways. In this study, we utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques. Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved an AUC of 0.75 on the validation dataset. All models were trained using five-fold cross-validation and rigorously evaluated on an independent validation dataset, which was not used during training or hyperparameter optimization. Finally, our best-performing model was employed to screen FDA-approved drugs for potential NF-{kappa}B inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the models predictive reliability. Our best- performing models have been integrated into a standalone software and web server, Nf{kappa}Bin, to enable the scientific community to perform high-throughput screening of Nf-{kappa}B inhibitors from chemical libraries. (https://webs.iiitd.edu.in/raghava/nfkbin/). Highlights1) Inhibitors of Nf-{kappa}B can be used to manage disease like rheumatoid arthritis, asthma. 2) A novel tool Nf{kappa}Bin developed for screening Nf-{kappa}B inhibitors from chemical library. 3) Cutting-edge feature selection techniques used to identify the most relevant chemical descriptors. 4) Models were developed and validated on a robust dataset of inhibitors and non-inhibitors. 5) Integrated best-performing models into the Nf{kappa}Bin software and web server.

bioinformatics↗

bioRxiv2024

Squidiff: Predicting cellular development and responses to perturbations using a diffusion model

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Studies involving physical stimuli, such as radiotherapy, or chemical stimuli, like drug testing, demand labor-intensive experimentation, hindering mechanistic insight and drug discovery. Here we present Squidiff, a diffusion model-based generative framework that predicts transcriptomic changes across diverse cell types in response to environmental changes. We demonstrate Squidiffs robustness across cell differentiation, gene perturbation, and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model blood vessel organoid development and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of molecular landscapes, facilitating rapid hypothesis generation and providing valuable insights for precision medicine.

bioinformatics↗

bioRxiv2024

ClearScope: a fully integrated light sheet theta microscope for sub-cellular resolution imaging without lateral size constraints

Three-dimensional (3D) ex vivo imaging of cleared intact brains of animal models and large human and non-human primate postmortem brain specimens is important for understanding the physiological neural network connectivity patterns and the pathological alterations underlying neuropsychiatric and neurological disorders. Light-sheet microscopy has emerged as a highly effective imaging modality for rapid high-resolution imaging of large cleared samples. However, the orthogonal arrangements of illumination and detection optics in light sheet microscopy limits the size of specimen that can be imaged. Recently developed light sheet theta microscopy (LSTM) technology addressed this by utilizing a unique arrangement of two illumination light paths oblique to the detection light path, while allowing perpendicular arrangement of the detection light path relative to the specimen surface. Here, we report development of a next-generation, fully integrated, and user-friendly LSTM system for rapid sub-cellular resolution imaging uniformly throughout a large specimen without constraining the lateral (XY) size. In addition, we provide a seamlessly integrated workflow for image acquisition, data storage, pre- and post-processing, enhancement, and quantitative analysis. We demonstrate the system performance by high-resolution 3D imaging of intact mouse brains and human brain samples, and complete data analysis including digital neuron tracing, vessel reconstruction and design-based stereological analysis in 3D. This technically enhanced and user-friendly LSTM implementation will enable rapid quantitative mapping of molecular and cellular features of interests in diverse types of very large samples.

neuroscience↗

bioRxiv2024

Capillary connections between sensory circumventricular organs and adjacent parenchyma enable local volume transmission

Among contributors to diffusible signaling are portal systems which join two capillary beds through connecting veins (Dorland 2020). Portal systems allow diffusible signals to be transported in high concentrations directly from one capillary bed to the other without dilution in the systemic circulation. Two portal systems have been identified in the brain. The first was discovered almost a century ago and connects the median eminence to the anterior pituitary gland (Popa & Fielding 1930). The second was discovered a few years ago, and links the suprachiasmatic nucleus to the organum vasculosum of the lamina terminalis, a sensory circumventricular organ (CVO) (Yao et al. 2021). Sensory CVOs bear neuronal receptors for sensing signals in the fluid milieu (McKinley et al. 2003). They line the surface of brain ventricles and bear fenestrated capillaries, thereby lacking blood brain barriers. It is not known whether the other sensory CVOs, namely the subfornical organ (SFO), and area postrema (AP) form portal neurovascular connections with nearby parenchymal tissue. This has been difficult to establish as the structures lie at the midline and protrude into the ventricular space. To preserve the integrity of the vasculature of CVOs and their adjacent neuropil, we combined iDISCO clearing and light-sheet microscopy to acquire volumetric images of blood vessels. The results indicate that there is a portal pathway linking the capillary vessels of the SFO and the posterior septal nuclei, namely the septofimbrial nucleus and the triangular nucleus of the septum. Unlike the latter arrangement, the AP and the nucleus of the solitary tract share their capillary beds. Taken together, the results reveal that all three sensory circumventricular organs bear specialized capillary connections to adjacent neuropil, providing a direct route for diffusible signals to travel from their source to their targets.

neuroscience↗

bioRxiv2024

Human vascular organoids with a mosaic AKT1 mutation recapitulate Proteus syndrome

Vascular malformation, a key clinical phenotype of Proteus syndrome, lacks effective models for pathophysiological study and drug development due to limited patient sample access. To bridge this gap, we built a human vascular organoid model replicating Proteus syndromes vasculature. Using CRISPR/Cas9 genome editing and gene overexpression, we created induced pluripotent stem cells (iPSCs) embodying the Proteus syndrome-specific AKTE17K point mutation for organoid generation. Our findings revealed that AKT overactivation in these organoids resulted in smaller sizes yet increased vascular connectivity, although with less stable connections. This could be due to the significant vasculogenesis induced by AKT overactivation. This phenomenon likely stems from boosted vasculogenesis triggered by AKT overactivation, leading to increased vascular sprouting. Additionally, a notable increase in dysfunctional PDGFR{beta} + mural cells, impaired in matrix secretion, was observed in these AKT-overactivated organoids. The application of AKT inhibitors (ARQ092, AZD5363, or GDC0068) reversed the vascular malformations; the inhibitors effectiveness was directly linked to reduced connectivity in the organoids. In summary, our study introduces an innovative in vitro model combining organoid technology and gene editing to explore vascular pathophysiology in Proteus syndrome. This model not only simulates Proteus syndrome vasculature but also holds potential for mimicking vasculatures of other genetically driven diseases. It represents an advance in drug development for rare diseases, historically plagued by slow progress.

bioengineering↗

bioRxiv2023

Aberrant pace of cortical neuron development in brain organoids from patients with 22q11.2 deletion syndrome and schizophrenia

Adults and children afflicted with the 22q11.2 deletion syndrome (22q11.2DS) exhibit cognitive, social, and emotional impairments, and are at significantly heightened risk for schizophrenia (SCZ). The impact of this deletion on early human brain development, however, has remained unclear. Here we harness organoid models of the developing human cerebral cortex, cultivated from subjects with 22q11.2DS and SCZ, as well as unaffected control samples, to identify cell-type-specific developmental abnormalities arising from this genomic lesion. Leveraging single-cell RNA-sequencing in conjunction with experimental validation, we find that the loss of genes within the 22q11.2 locus leads to a delayed development of cortical neurons. This impaired development was reflected in an increased proportion of actively proliferating neural progenitor cells and a reduced fraction of more mature neurons. Furthermore, we identify perturbed molecular imprints linked to neuronal maturation, observe the presence of sparser neurites, and note a blunted amplitude in glutamate-induced Ca2+ transients. The aberrant transcription program underlying impaired development contains molecular signatures significantly enriched in neuropsychiatric genetic liability. MicroRNA profiling and target gene investigation suggest that microRNA dysregulation due to DGCR8 deficiency may drive perturbations of genes governing the pace at which maturation unfolds. Using protein-protein interaction network analysis we define complementary effects stemming from other genes residing within the deleted locus. Our study uncovers reproducible neurodevelopmental and molecular alterations resulting from 22q11.2 deletions, with findings that could advance disease modeling and drive the development of therapeutic interventions.

neuroscience↗

bioRxiv2023

Cell arrangement impacts metabolic activity and antibiotic tolerance in Pseudomonas aeruginosa biofilms

Cells must access resources to survive, and the anatomy of multicellular structures influences this access. In diverse multicellular eukaryotes, resources are provided by internal conduits that allow substances to travel more readily through tissue than they would via diffusion. Microbes growing in multicellular structures, called biofilms, are also affected by differential access to resources and we hypothesized that this is influenced by the physical arrangement of the cells. In this study, we examined the microanatomy of biofilms formed by the pathogenic bacterium Pseudomonas aeruginosa and discovered that clonal cells form striations that are packed lengthwise across most of a mature biofilms depth. We identified mutants, including those defective in pilus function and in O-antigen attachment, that show alterations to this lengthwise packing phenotype. Consistent with the notion that cellular arrangement affects access to resources within the biofilm, we found that while the wild type shows even distribution of tested substrates across depth, the mutants show accumulation of substrates at the biofilm boundaries. Furthermore, we found that altered cellular arrangement within biofilms affects the localization of metabolic activity, the survival of resident cells, and the susceptibility of subpopulations to antibiotic treatment. Our observations provide insight into cellular features that determine biofilm microanatomy, with consequences for physiological differentiation and drug sensitivity.

microbiology↗

bioRxiv2023

Scalable projected Light Sheet Microscopy for high-resolution imaging of large samples

Light sheet fluorescence microscopy (LSFM) is a widely used imaging technique for living and large cleared samples. However, high-performance LSFM systems are often prohibitively expensive and not easily scalable for high-throughput applications. Here, we introduce a cost-effective, scalable, and versatile high-resolution imaging framework, called projected Light Sheet Microscopy (pLSM), which repurposes readily available off-the-shelf consumer-grade components and an over-the-network control architecture to achieve high-resolution imaging of living and cleared samples. We extensively characterize the pLSM framework and showcase its capabilities through high-resolution, multi-color imaging and quantitative analysis of mouse and post-mortem human brain samples cleared using various techniques. Moreover, we show the applicability of pLSM for high-throughput molecular phenotyping of human induced pluripotent cells (iPSC)-derived brain and vessel organoids. Additionally, we utilized pLSM for comprehensive live imaging of bacterial pellicle biofilms at the air-liquid interface, uncovering their intricate layered architecture and diverse cellular dynamics across different depths. Overall, the pLSM framework has the potential to further democratize LSFM by making high-resolution light sheet microscopy more accessible and scalable.

neuroscience↗

bioRxiv2023

Whole-brain mapping reveals the divergent impact of ketamine on the dopamine system

Ketamine is a multifunctional drug with clinical applications as an anesthetic, as a pain management medication and as a transformative fast-acting antidepressant. It is also abused as a recreational drug due to its dissociative property. Recent studies in rodents are revealing the neuronal mechanisms that mediate the complex actions of ketamine, however, its long-term impact due to prolonged exposure remains much less understood with profound scientific and clinical implications. Here, we develop and utilize a high-resolution whole-brain phenotyping approach to show that repeated ketamine administration leads to a dosage-dependent decrease of dopamine (DA) neurons in the behavior state-related midbrain regions and, conversely, an increase within the hypothalamus. Congruently, we show divergently altered innervations of prefrontal cortex, striatum, and sensory areas. Further, we present supporting data for the post-transcriptional regulation of ketamine-induced structural plasticity. Overall, through an unbiased whole-brain analysis, we reveal the divergent brain-wide impact of chronic ketamine exposure on the association and sensory pathways.

neuroscience↗

bioRxiv2022

Focused ultrasound-mediated brain genome editing

Gene editing in the mammalian brain has been challenging because of the restricted transport imposed by the blood-brain barrier (BBB). Current approaches rely on local injection to bypass the BBB. However, such administration is highly invasive and not amenable to treating certain delicate regions of the brain. We demonstrate a safe and effective gene editing technique by using focused ultrasound (FUS) to transiently open the BBB for the transport of intravenously delivered CRISPR/Cas9 machinery to the brain.

bioengineering↗