Biology subjects

Mukherjee, S.

Publications and source records attributed to Mukherjee, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

LuxS/AI-2 signaling in Salmonella Typhimurium regulates phoP/phoQ to protect against acidic pH for enhanced survival in macrophages

The intestinal milieu is largely characterized by the complex array of chemical compounds produced through the metabolic activity of resident microbiota. Enteric pathogens like Salmonella, which have evolved refined mechanisms to persist within this environment, utilize these microbial metabolites and self-produce quorum molecules as molecular cues to identify ecological niches and modulate their survival and virulence strategies. Salmonella quorum sensing involves producing and detecting Autoinducer-2 (AI-2) signaling molecules. Our research reveals that Salmonella Typhimurium enhances AI-2 biosynthesis and transport under acidic conditions, aiding environmental adaptation and facilitating pathogenesis in macrophages. AI-2 signaling regulates the pH-sensing two-component system genes, phoP/phoQ, ensuring cytosolic pH homeostasis, survival, and acid tolerance. It also involves regulating the lysine/cadaverine-mediated acid tolerance response and maintaining bacterial cytosolic pH. Furthermore, we show that the repressor LsrR protein, apart from the lsr promoter, binds to only one strand of the phoP promoter via its Y25 and R43 amino acids to negatively modulate phoP expression. Additionally this signaling ameliorates the intracellular survival by modulating Salmonella Pathogenicity Island-2 (SPI-2) regulators (ssrA/ssrB) and SPI-2 effector expression via PhoP. Mouse models demonstrate that AI-2 signaling is essential for colonizing the primary and secondary infection sites, showcasing its critical role in pathogenesis and low pH survival mechanisms in Salmonella.

microbiology↗

bioRxiv2025

A Bitopic mTORC Inhibitor Reverses Neural Phenotypes in a Tuberous Sclerosis Complex Murine Model of SEGA-like Hamartomas

Neural stem cells (NSCs) of the ventricular-subventricular zone (V-SVZ) generate diverse cell types including striatal glia during the neonatal period. NSC progeny uncouple stem cell-related mRNA transcripts from being translated during differentiation. We previously demonstrated that Tsc2 inactivation, which occurs in the neurodevelopmental disorder Tuberous Sclerosis Complex (TSC), prevents this from happening. Loss of Tsc2 causes hyperactivation of the protein kinase mechanistic target of rapamycin complex 1 (mTORC1), altered translation, retention of stemness in striatal glia, and the production of misplaced cytomegalic neurons having hypertrophic dendrite arbors. These phenotypes model characteristics of TSC hamartomas called subependymal giant cell astrocytomas (SEGAs). mTORC1 inhibitors called rapamycin analogs (rapalogs) are currently used to treat TSC and to assess the role of mTORC1 in regulating TSC-related phenotypes. Rapalogs are useful for treating SEGAs. However, they require lifelong application, have untoward side effects, and resistance may occur. They also incompletely inhibit mTORC1 and have limited efficacy. Rapalink-1 is a bitopic inhibitor developed to overcome the limitations of rapalogs by linking rapamycin to a second-generation mTOR ATP competitive inhibitor, MLN0128. Here we explored the effect of Rapalink-1 on a TSC hamartoma model. The model is created by neonatal electroporation of mice having conditional Tsc2 genes. Prolonged Rapalink-1 treatment could be achieved with 1.5 or 3.0 mg/Kg injected intraperitoneally every five days. Rapalink-1 inhibited the mTORC1 pathway, decreased cell size, reduced neuron dendrite arbors, and reduced hamartoma size. In conclusion, these results demonstrate that cellular phenotypes in a TSC SEGA model are reversed by Rapalink-1 which may be useful to resolve TSC brain hamartomas.

neuroscience↗

bioRxiv2025

Magnetic resonance control of reaction yields through genetically-encoded protein:flavin spin-correlated radicals in a live animal

Radio-frequency (RF) magnetic fields can influence reactions involving spin-correlated radical pairs. This provides a mechanism by which RF fields can influence living systems at the biomolecular level. Here we report the modification of the emission of various red fluorescent proteins (RFPs), in the presence of a flavin cofactor, induced by a combination of static and RF magnetic fields. Resonance features in the protein fluorescence intensity were observed near the electron spin resonance frequency at the corresponding static magnetic field strength. This effect was measured at room temperature both in vitro and in the nematode C. elegans, genetically modified to express the RFP mScarlet. These observations suggest that the magnetic field effects measured in RFP-flavin systems are due to quantum-correlated radical pairs. Our experiments demonstrate that RF magnetic fields can influence dynamics of reactions involving RFPs in biologically relevant conditions, and even within a living animal. These results have implications for the development of a new class of genetic tools based on RF manipulation of genetically-encoded quantum systems.

biophysics↗

bioRxiv2025

Addressing efficacy of everyday hygiene cleansing products in context of sustainable handwashing behavior in the post-pandemic era

Simple hygiene behaviour such as washing hands is key to improving health of individuals and reducing community transmission of communicable diseases such as respiratory and enteric infections. Consistent and relentless messaging by global and local health authorities had resulted in heightened hygiene awareness amongst the public during the COVID-19 pandemic. Unfortunately, many of the hygiene behaviours practiced during the pandemic have proven to be unsustainable in the immediate period following the pandemic. While CDC guidelines suggest washing hands with soap for a minimum of 20 seconds to prevent the spread of germs, a hygiene interventions effectiveness must be evaluated in context of the prevalent hygiene behaviour. Here we report our findings from an observational study conducted across India, Pakistan, Saudi Arabia, and United Arab Emirates focusing on handwashing habit of individuals in the post-pandemic period. Across all geographies, the time spent for lathering product on skin (contact time) for a significant majority of individuals recruited for the study was found to be 10 seconds and less. To ensure that marketed hygiene formulations such as liquid cleansers and sanitizers are efficacious in inactivating pathogens under conditions practiced by the majority as a part of their daily hand washing practices, we have investigated the in-vitro antimicrobial efficacy of several hygiene cleansing formulations at 10 seconds of contact time. Our results show that well-formulated cleansing solutions can reduce the input titre of both bacterial and viral pathogens by 99.9% or more, even with brief contact. With the global resurgence of both existing and emerging pathogens in the post-pandemic world, promoting sustainable handwashing practices for infection prevention remains a challenge as it is deeply rooted in the socio-economic fabric of many countries. Therefore, it is crucial to conduct regular studies like this to reassure end-users and combat complacency regarding the composition of everyday hygiene products. HighlightsO_LIStudies of handwashing behavior in 4 countries reveals significantly short lathering duration C_LIO_LIAntimicrobial efficacy testing of marketed products was conducted against a range of pathogens C_LIO_LISuitably formulated hygiene products can inactivate pathogens at short durations of contact C_LI

microbiology↗

bioRxiv2025

Monoclonal antibody to Filarial serpin Wb123 impedes the urokinase plasminogen activator receptor-mediated Alternative activation of macrophages

Macrophages play a multifaceted role in mounting an immune response against pathogens through phagocytosis, antigen presentation and cytokine production. Activated macrophages are critical for the initiation, maintenance, and resolution of inflammation. They can either be classically activated (Th1 response), which promotes inflammation or alternatively activated (Th2 response), which resolves inflammation. The alternate activation of macrophages, along with diminished T cell response, is thought to be an important immune evasion strategy employed by the filarial parasites. However, the mechanism behind filaria-induced alternate activation remains unclear. In this study, by using in-silico approach we identified filarial serpins which are highly expressed in the infective L3 larval stages. We characterized the filarial serine protease inhibitor, Wb123 and established its role in alternate activation. We observed that Wb123 impairs nitric oxide (NO) and reactive oxygen species (ROS) expression, which plays a crucial role in mounting a strong immune response. Surprisingly, we also observed elevated IL6 expression and phosphorylation of STAT3, indicating IL-6-dependent alternate activation. Our findings indicate that the urokinase plasminogen activator receptor (uPAR) is essential for Wb123-induced alternate activation. Furthermore, we demonstrated that the monoclonal antibody MAbG8 impedes the Wb123-induced alternate activation, as shown by reduced CD163 expression along with increased ROS and uPA expression in response to lipopolysaccharide (LPS) and interferon-gamma (IFN-{gamma}). Overall, our study identifies the filarial serpin Wb123 and highlights its potent role in alternative activation. This interaction reveals, for the first time a mechanism exploited by filarial parasites to evade a strong pro-inflammatory immune response.

cell biology↗

bioRxiv2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool - a tetranucleotide-informed metagenome stability diagram - that is publicly available in the Integrated Microbial Genomes and Microbiomes (IMG/M) platform for metagenome-ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments, and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. ImportanceMicrobes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

ecology↗

bioRxiv2025

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUCs). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like Principal Component Analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating acquisition of a more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets and additional computational overhead. In this work we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly 6-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution, and subsequently using the upscaled spectra to accurate predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.

biophysics↗

bioRxiv2025

Distinct region-specific neutralization profiles of contemporary HIV-1 clade C against best-in-class broadly neutralizing antibodies

Broadly neutralizing antibodies (bnAb) have been clinically proven to be an excellent choice for HIV-1 prevention. However, the relative effectiveness of best-in-class bnAbs against regionally relevant circulating HIV-1 forms is not clear. In the present study, we compared the degree of neutralization sensitivity of contemporary HIV-1 Indian clade C with that of South African origin. Phylogenetic analysis revealed that these clade C viruses continue to evolve distinctly from one another. Env-pseudotyped viruses prepared using contemporary HIV-1 clade C env genes (N=115) obtained from nine geographically distinct sites in India (between 2020-2023) were found to be most sensitive to V3-directed bnAbs 10-1074 and BG18, and second generation CD4 binding site (CD4bs) directed bnAbs (VRC07, N6 and 1-18), however they were found to be significantly resistant to V1/V2 apex directed bnAbs. Moreover, we observed that the degree of sensitivity varied between contemporary Indian and South African clade C viruses. Differences in degree of neutralization susceptibility were associated with differences observed in key residues that form bnAb contact sites, gp120 loop lengths and the number of N-linked glycans in the V4 hypervariable region. Interestingly, the second generation CD4bs bnAbs (VRC07, N6, 1-18) showed neutralization of VRC01 and 3BNC117 resistant viruses but with 2-7-fold reduced potency compared to the VRC01 sensitive counterparts, likely due to the enrichment of resistance associated residues observed in loop D. Predictive analysis indicated that combination of BG18, N6 and PGDM1400 can provide over 95% neutralization coverage at 1g/mL of contemporary India clade C, an observation found to be distinct to that reported for the Africa clade C viruses. Taken together, we found distinct neutralization patterns and env signatures associated with resistance to key bnAbs. Our study highlights that towards achieving clinical effectiveness, both the complementarity of bnAb classes and the regionally relevant HIV forms need to be considered. Author summaryWhile the development of vaccines to prevent HIV infection remains a global priority, their potential effectiveness is limited by the extraordinarily diversified circulating forms of HIV-1. The prospect of best-in-class bnAbs as potential prevention option has been demonstrated in several studies including the Phase II Antibody Mediated Prevention (AMP) trial; however, to be broadly applicable, bnAbs will need to overcome the substantial variability of HIV env. The present study highlights that the contemporary HIV-1 clade C viruses are evolving to be less sensitive to the best-in-class bnAbs and HIV-1 clade C that predominates in India and South Africa vary in their degree of susceptibility to best-in-class clinically relevant bnAbs. This indicates differences in the antigenic properties between globally circulating HIV-1 clade C at a population level. Overall, the outcome of this study highlights the need for periodic assessment of sequence and neutralization profiles of the circulating regionally relevant HIV-1 forms towards prioritizing the bnAb combination suitable for effective intervention.

immunology↗

bioRxiv2024

A single Citrobacter rodentium infection in Pink1 knockout and wild type mice leads to regional blood-brain-barrier perturbation and glial activation without dopamine neuron axon terminal loss

A growing body of research suggests a link between immune system activation and the development of Parkinsons disease (PD). Previous work showed that repeated gastrointestinal infection with Citrobacter rodentium can induce PD-like motor dysfunction in Pink1 knockout (KO) mice, along with immune cell infiltration into the brain. To better understand mechanisms underlying immune-mediated brain attack in this model, we tested whether mild infections are sufficient to increase blood-brain barrier (BBB) permeability and trigger brain inflammation. Pink1 wild-type (WT) and KO mice were infected with C. rodentium, and gadolinium-enhanced magnetic resonance imaging (MRI) was performed at days 13 and 26 post-infection to assess BBB integrity. Quantitative MRI analysis revealed increased BBB permeability at day 26 in both WT and KO mice, particularly in the striatum, dentate gyrus, somatosensory cortex, and thalamus. Notably, this permeability was not associated with changes in tight junction protein expression or dopamine system markers in the striatum at either time point. However, persistent microglial activation was observed at day 26 post-infection, along with elevated levels of inflammatory mediators such as eotaxin, IFN-{gamma}, CXCL9, IL-17, and MIP-2 in the striatum. Additionally, serum levels of IL-17 and CXCL1 were increased in infected Pink1 KO mice. Flow cytometry revealed neutrophil infiltration in the brain at day 26 post-infection. Finally, a bulk RNA-seq transcriptome analysis revealed that gene sets related to synaptic function were particularly influenced by the infection and that inflammation-related genes were upregulated by the infection in the Pink1 KO mice. These findings support the hypothesis that even mild gastrointestinal infections can increase BBB permeability, disrupt brain homeostasis, and promote chronic neuroinflammation. In genetically susceptible individuals, such as those with Pink1 deficiency, this may represent a first hit that contributes to subsequent induction of PD pathology with aging. Author summaryWe hypothesize that immune system activation is linked to the development of Parkinsons disease (PD). Previous work revealed that repeated gastrointestinal infections with Citrobacter rodentium causes PD-like symptoms and immune cell invasion in the brain of Pink1 knockout (KO) mice. In the current study, we tested whether a single mild gut infection alters blood-brain barrier (BBB) permeability and causes brain inflammation. We infected Pink1 WT and KO mice with Citrobacter rodentium and used gadolinium-enhanced MRI to detect BBB permeability changes at 13- and 26-days post-infection. Results showed increased BBB permeability in specific brain regions at 26 days. While tight-junction and dopamine (DA)-related proteins remained unchanged, we observed altered expression of synaptic genes, chronic microglial activation, elevated inflammatory markers, and neutrophil infiltration in the brain. Our findings suggest that even mild gastrointestinal infections can increase BBB permeability, potentially enabling immune cell infiltration into the brain and exacerbating pathways implicated in the development of PD, particularly among individuals with genetic risk factors.

neuroscience↗

bioRxiv2024

Causal considerations can determine the utility of machine learning assisted GWAS

Machine Learning (ML) is increasingly employed to generate phenotypes for genetic discovery, either by imputing existing phenotypes into larger cohorts or by creating novel phenotypes. While these ML-derived phenotypes can significantly increase sample size, and thereby empower genetic discovery, they can also inflate the false discovery rate (FDR). Recent research has focused on developing estimators that leverage both true and machine-learned phenotypes to properly control the type-I error. Our work complements these efforts by exploring how the true positive rate (TPR) and FDR depend on the causal relationships among the inputs to the ML model, the true phenotypes, and the environment. Using a simulation-based framework, we study architectures in which the machine-learned proxy phenotype is derived from biomarkers (i.e. inputs) either causally upstream or downstream of the target phenotype. We show that no inflation of the false discovery rate occurs when the proxy phenotype is generated from upstream biomarkers, but that false discoveries can occur when the proxy phenotype is generated from downstream biomarkers. Next, we show that power to detect variants truly associated with the target phenotype depends on its heritability and correlation with the proxy phenotype. However, the source of the correlation is key to evaluating a proxy phenotype's utility for genetic discovery. We demonstrate that evaluating machine-learned proxy phenotypes using out-of-sample predictive performance (e.g. phenotypic correlation) provides a poor lens on utility. This is because overall predictive performance does not differentiate between genetic and environmental correlation. In addition to parsing these properties of machine-learned phenotypes via simulations, we further illustrate them using real-world data from the UK Biobank.

genetics↗

bioRxiv2024

mTOR signaling governs the formation of epithelial apical projection via S6K1-RhoA and aPKC-Lgl2 axes

In metazoans, epithelia perform functions of absorption, diffusion, and secretion. The actin-based apical projections on the epithelial cells, contribute to these functions and are formed via cell-autonomous mechanisms that control cell polarity, intracellular transport, and the cytoskeleton. However, the cues that function upstream of these cell-autonomous regulators remain poorly known. Using microridges on zebrafish epithelial cells as a paradigm, we show that mTOR, a metabolic sensor, regulates the formation of apical projections. Mechanistically, mTORC1 controls the RhoA-ROCK activity via S6K1 to prevent the overactivation of Non-muscle Myosin II (NMII) to restrict microridge elongation. Furthermore, genetic, biochemical, and molecular dynamics simulation analyses reveal that mTORC2 regulates microridge pattern by modulating the activity of aPKC via its differential phosphorylation at two conserved sites. We propose that mTOR integrates the developmental and/or metabolic status of epithelial cells with cell autonomously acting RhoA and aPKC to regulate tissue wide formation of apical projections.

cell biology↗

bioRxiv2024

SMAS: Structural MRI-Based AD Score using Bayesian VAE

This study introduces the Structural MRI-based Alzheimers Disease Score (SMAS), a novel index intended to quantify Alzheimers Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DEL-CODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DEL-CODE: r=0.50; ADNI: r=0.28), hippocampal volume (DEL-CODE: r=-0.44; ADNI: r=-0.66), and total grey matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies suggest that SMAS may be useful for early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over a 36-month period, SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. Relevance map analysis revealed significant morphological changes in key AD-related brain regions--including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex--highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.

neuroscience↗

bioRxiv2024

G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes

G-quadruplexes (G4s) are the four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of the guanine tetramers. They are involved in a wide range of biological roles because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bio-informatics algorithms were introduced to predict the active G4s regions in vitro based on the canonical G4 sequence elements, G-richness, and G-skewness, as well as the non-canonical sequence features. Recently, sequencing techniques like G4-seq and G4-ChIP-seq were developed to map the G4s in vitro, and in vivo respectively at a few hundred base resolution. Subsequently, several machine learning and deep learning approaches were developed for predicting the G4 regions using the existing databases. However, their prediction models were simplistic, and the prediction accuracy was notably poor. In response, here, we propose a novel convolutional neural network with Bi-LSTM and attention layers, named G4-Attention, to predict the G4 forming sequences with improved accuracy. G4-Attention achieves high accuracy and attains state-of-the-art results in the G4 propensity and mismatch score prediction task in comparison to other available benchmark models in the literature. Besides the balanced dataset, the developed model can predict the G4 regions accurately in the highly class-imbalanced datasets. Furthermore, the model achieves a significant improvement in the cell-type-specific G4 prediction task. In addition, G4-Attention trained on the human genome dataset can be applied to any non-human genomic DNA sequences to predict the G4 formation propensities accurately. We have also added interpretability analysis of our model to gain further insights. Author summaryG-quadruplex, a non-canonical secondary nucleic acid structure, has emerged as a potential pharmacological target because of its significant implication in several human diseases including cancer, aging, neurological disorders, etc. Despite numerous computational algorithm developments, the prediction of G4 regions accurately in different organisms including humans still remains a challenging task. To address this, in this work, we have presented a novel advanced deep learning architecture called G4-Attention for predicting DNA G-quadruplexes in different organisms including humans. To the best of our knowledge, we are the first to incorporate Bi-LSTM and attention layers on top of a CNN architecture in a deep learning model (G4-Attention) for predicting G4-forming sequences. Our developed model outperforms existing algorithms and achieves current state-of-the-art (SOTA) results in G4 propensity and mismatch score prediction tasks. In addition, the developed model achieves superior results across non-human genomes, class-imbalanced datasets, and cell line-specific datasets. Lastly, G4-Attention can identify key features for understanding the G4 formation mechanism.

bioinformatics↗

bioRxiv2024

Potassium ferric oxalate nanoparticles prevent human blood clotting and thrombosis in a mouse model

Blood clot creates occlusion in the veins and arteries, which leads to pernicious effects. Here, the anticoagulation properties of potassium ferric oxalate nanoparticles (KFeOx-NPs) in human blood were demonstrated for blood clot management. The mechanism involves the chelation of calcium ions from the blood by the oxalate present in the KFeOx-NPs. Various commercial assays were used to determine the clotting time for the KFeOx-NPs and identified the hindrance in activating factor XII in the intrinsic pathway. We used animal models to show toxicity and biodistribution profiles and determined the safety and efficacy. Intravenously injected KFeOx-NPs increased clotting time and thrombosis prevention in a mouse model confirmed by ultrasound and the power Doppler images. Coating catheters with KFeOx-NPs prevents clot formation with reduced protein attachment when incubated with blood, enhancing blood flow properties. In biological applications, KFeOx-NPs may improve the long-term prevention of blood clot formation and enhance the efficiency of medical devices. TOC O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/621820v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1292afeorg.highwire.dtl.DTLVardef@1c5ba81org.highwire.dtl.DTLVardef@576f23org.highwire.dtl.DTLVardef@4186e9_HPS_FORMAT_FIGEXP M_FIG C_FIG PVP-stabilized novel potassium ferric oxalate nanoparticles were synthesized for the application of blood clot management and thrombosis prevention.

pathology↗

bioRxiv2024

Low RT-based Genome Editing Fidelity in Mouse Hepatocytes: Challenges and Solutions

Abstract/SummaryIntegrase-mediated Programmable Genomic Integration (I-PGI) uses a Cas9 nickase (nCas9) with a reverse transcriptase (RT), to write a large serine integrase (LSI) target site (attB/P, here called "beacon") in a programmed location. Co-delivery of the LSI and a DNA template containing the cognate recognition site results in precise integration of the template in a specific genomic location. While we were able to achieve high-fidelity beacon placement in a range of primate cycling and non-dividing cells, when translating our technology into an in vivo rodent model (liver) we surprisingly observed very low beacon fidelity, with the vast majority of beacons being unsuitable for integration. This phenomenon was independent of mouse strain, but was specific to non-dividing cells, as a cycling mouse hepatocyte cell line (Hepa1-6) demonstrated very high levels of fidelity. To address this issue we utilized neonatal mice, which have a much higher proportion of proliferating hepatocytes than adult mice. This resulted in a significant increase in the placement of high-fidelity beacons, and achieved functional gene expression after I-PGI in a therapeutically relevant target site. In an alternate approach, we engineered transgenic mice with intact beacons placed in specific genomic locations, allowing us to optimize integrase and DNA template dosing and kinetics. In summary, we have identified a previously undescribed challenge when using RT-based editing to write long sequences (~40 bp) in non-dividing rodent hepatocytes. This phenomenon was specific to rodents and was not observed in primate dividing or non-dividing cells. This previously unidentified challenge using RTs will limit the use of I-PGI in mouse models, however here we describe two methods that address this issue.

bioengineering↗

bioRxiv2024

Curative levels of endogenous gene replacement achieved in non-human primate liver using programmable genomic integration

The ability to efficiently place a large piece of DNA in a specific genomic location has been a goal for the gene therapy field since its inception; however, despite significant advances in gene editing technology, this had yet to be achieved. Here we describe two methods of programmable genomic integration (PGI) that overcome some of the limitations of current approaches. Using a combination of clinically validated delivery technologies (LNP, AAV), we demonstrate the ability to specifically integrate large (>2 kb) DNA sequences into endogenous introns in the liver of non-human primates (NHP). PGI was effective across multiple genomic locations and transgenes, and insertion led to expression from the endogenous promoter. PGI was highly efficient, achieving expression in >50% of liver cells after a single course of treatment, which would be curative for most monogenic recessive liver diseases. This is the first report of clinically curative level of gene insertion at endogenous loci in NHP.

bioengineering↗

bioRxiv2024

Traumatic Brain Injury Exacerbates Alcohol Consumption and Neuroinflammation with Decline in Cognition and Cholinergic Activity

Traumatic brain injury (TBI) is a global health challenge, responsible for 30% of injury-related deaths and significantly contributing to disability. Annually, over 50 million TBIs occur worldwide, with most adult patients at emergency departments showing alcohol in their system. TBI is also a known risk factor for alcohol abuse, yet its interaction with alcohol consumption remains poorly understood. In this study, we demonstrate that the fluid percussion injury (FPI) model of TBI in mice significantly increases alcohol consumption and impairs cognitive function. At cellular levels, FPI markedly reduced the number and activity of striatal cholinergic interneurons (CINs) while increasing microglial cells. Notably, depleting microglial cells provided neuroprotection, mitigating cholinergic loss and enhancing cholinergic activity. These findings suggest that TBI may promote alcohol consumption and impair cognitive abilities through microglia activation and consequently reduced cholinergic function. Our research provides critical insights into the mechanisms linking TBI with increased alcohol use and cognitive deficits, potentially guiding future therapeutic strategies.

neuroscience↗

bioRxiv2024

The binary protein interactome mapping of the Giardia lamblia proteasome lid reveals extra proteasomal functions of GlRpn11

Giardia lamblia does not encode Rpn12 and Sem1, two proteins crucial for assembling the proteasome lid. To understand how the interactions between the giardial proteasome lid subunits may have changed to compensate for their absence, we used the yeast two-hybrid assay to generate a binary protein interaction map of the Giardia lid subunits. Most interactions within the Giardia proteasome lid are stronger than those within the Saccharomyces cerevisiae lid. These may compensate for the absence of Rpn12 and Sem1. A notable exception was the weaker interaction between GlRpn11 and GlRpn8, compared to the strong interaction between Rpn11-Rpn8 of yeast. The Rpn11-Rpn8 dimer provides a platform for lid assembly and their interaction involves the insertion of a methionine residue of Rpn11 into a hydrophobic pocket of Rpn8. Molecular modeling indicates that GlRpn8s pocket is wider, reconciling the experimental observation of its weak interaction with GlRpn11. This weaker interaction may have evolved to support extra proteasomal functions of GlRpn11, which localizes to multiple subcellular regions where other proteasome subunits have not been detected. One such location is the mitosome. Functional complementation in yeast shows that GlRpn11 can influence mitochondrial function and distribution. This, together with its mitosomal localization, indicates that GlRpn11 functions at the mitosome. Thus, this parasites proteasome lid has a simpler subunit architecture and structural attributes that may support dual functionalities for GlRpn11. Such parasite-specific proteasome features could provide new avenues for controlling the transmission of Giardia. 1 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/613619v1_ufigs1.gif" ALT="Figure 0"> View larger version (33K): org.highwire.dtl.DTLVardef@989e69org.highwire.dtl.DTLVardef@1c8134aorg.highwire.dtl.DTLVardef@6a9794org.highwire.dtl.DTLVardef@5ac20_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIGiardia genome does not encode two proteasomal lid subunits: Rpn12 and Sem1 C_LIO_LIUnique interactions within the lid may compensate for the absence of these two C_LIO_LIGlRpn8:GlRpn11 weakly interacts to support GlRpn11s extra-proteasomal distribution C_LIO_LIGlRpn11 localizes at mitosomes, OZ of VD, and to the VFP C_LIO_LIThe 182-218 fragment of GlRpn11 may regulate mitosomal function C_LI

cell biology↗