Biology subjects

Jose, A.

Publications and source records attributed to Jose, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Hollow homes: Roost selection and role of roost morphometrics in the cavity-roosting Bamboo Bat (Genus Tylonycteris) from the Western Ghats, southern India

Bat species occupy diverse day-roosting habitats, ranging from rock-based to plant-based substrates. Depending on the properties of roosting substrates and the ability of bats to adapt to them, roosts can be stable or ephemeral over time. Bamboo bats (genus Tylonycteris) occupy bamboo cavities and possess adaptations such as a flattened skull, thickened thumb pads and modified soles of the feet, making them specialised for roosting within often ephemeral bamboo internodes. The Western Ghats host a bamboo bat species that has traditionally been considered a subspecies of Southeast Asia's Tylonycteris pachypus. However, its precise taxonomic status is unresolved, and recent analyses suggest this population could be a potential new species. Nevertheless, their roosting ecology and the role of bamboo properties in occupancy remain unexplored. By comparing properties of used and unused cavities by the bamboo bats in the Western Ghats, we investigated how physical roost characteristics are associated with cavity occupancy and how the roost properties and space available differed between solitary and non-solitary roosts. We found that only the area of the cavity mouth was associated with occupancy, and all tested properties, including roost volume, were similar for solitary and non-solitary roosts. Our findings are similar to patterns reported from other Tylonycteris species, but these findings contribute to the comparative understanding of bamboo bat roost selection across the tropics. It also opens up future avenues to investigate the understudied ecological continuum in the Indian tropics that involves bats, bamboo, pest insects, cavity-dwelling taxa and bamboo harvesters in further detail.

ecology↗

bioRxiv2026

Optimizing High Parameter T Cell Immunophenotyping Through Direct Comparison of Conventional and Spectral Flow Cytometry

High-parameter flow cytometry is essential for dissecting the intricate landscape of T-cell diversity. In this study, we directly compare conventional flow cytometry (CFC) and spectral flow cytometry (SFC) for high-dimensional T-cell phenotyping, assessing how spectral detection and panel-design strategies influence analytical performance. Using peripheral blood mononuclear cells from healthy donors stained with both an established (v1) and an optimized (v2) fluorochrome-labelled antibody panel, and analyzed through manual gating and unsupervised approaches, we found that CFC reliably identified major T-cell subsets. However, spectral acquisition consistently delivered clear technical advantages, including improved signal-to-noise ratios, higher staining index values, and superior resolution of low-intensity and co-expressed markers. These improvements translated into more sharply delineated multidimensional clusters and a markedly enhanced resolution of T-cell differentiation states. Moreover, the optimized spectral panel enhanced the unsupervised detection of rare populations, such as cytotoxic CD4 T-cells (PD-1GZMB). However, despite the overall increase in data quality achieved with SFC, the selection of antibody clones may influence the measured frequencies of the identified populations. Finally, SFC - particularly when coupled with rational panel optimization and the use of advanced fluorophores - consistently delivers superior, higher-quality measurements and improved multidimensional resolution, thereby substantially enhancing the robustness and sensitivity of high-parameter T-cell phenotyping for comprehensive immunological studies.

immunology↗

bioRxiv2025

TIF1γ drives oral cancer recurrence by the transcriptional regulation of self-renewal genes, such as HES1

TIF1{gamma} is an E3 ubiquitin ligase and key mediator of the noncanonical TGF-{beta} signaling pathway. Initially characterized for its developmental functions, TIF1{gamma} is essential for maintaining the pluripotency of adult stem cells, including long-term hematopoietic stem cells. Although TGF-{beta} signaling contributes to cancer progression and recurrence, TIF1{gamma} has traditionally been regarded as a tumor suppressor due to its inhibition of oncogenes involved in epithelial-mesenchymal transition. However, clinical reports have associated high TIF1{gamma} expression at advanced cancer stages with poor prognosis. To elucidate the mechanism underlying this paradox, we identify a previously unrecognized role of TIF1{gamma} in promoting the self-renewal capacity of oral cancer cells, thereby contributing to disease recurrence. Phosphoproteomic profiling of self-renewal-enriched cells revealed activation of a noncanonical TGF-{beta} pathway. Using extreme limiting dilution assays, an ALDH1A1-DsRed2 cancer stem cell reporter, multiple oral cancer cell lines, primary 3D cultures on alginate matrix, and orthotopic mouse models, we demonstrate that TIF1{gamma} depletion significantly reduces self-renewal and prolongs disease-free survival. Immunoprecipitation (IP)-LC/MS/MS analysis identified transcriptional regulators within the TIF1{gamma} interactome, including TRRAP and H2A.Z, which were validated by IP and FRET assays. ChIP and IP studies further revealed that during self-renewal enrichment, TRRAP acetylates H2A.Z, decreasing the chromatin occupancy of its unacetylated form. Acetylated H2A.Z is subsequently recognized by TIF1{gamma}, which monoubiquitinates H2B at promoters of self-renewal genes such as HES1, initiating transcription. In alignment with findings from mouse neocortical development, where a Notch-independent Hes1-expressing (NIHes1) population defines primitive quiescent stem cells, we show that TIF1{gamma} acts as an acetylation reader specifically at the NIHES1 promoter region of HES1. TIF1{gamma} depletion drives NIHES1 cells toward a Notch-dependent HES1 (NDHES1) identity. RNA-seq confirmed reversal of 100 NIHES1-specific genes following TIF1{gamma} loss, along with downregulation of pluripotency-associated genes found in embryonic stem cells, supporting a critical role for TIF1{gamma} in maintaining primitive cancer stem cell states. Consistent with our in vivo findings, primary oral cancer samples showed that increased frequencies of TIF1{gamma}+/TRRAP+/H2A.Z- cells strongly predict recurrence. Given that the histone acetylation-reader function of TIF1{gamma} drives poor prognosis, our findings suggest the TIF1{gamma} bromodomain as a potential therapeutic target requiring further investigation.

cancer biology↗

bioRxiv2025

Rho signaling promotes cell excitability and synaptic transmission

Gq signaling through Trio RhoGEF and Phospholipase C effectors promotes neurotransmitter release at synapses. Whether activated Rho and Phospholipase C signaling acts through shared or independent pathways remains unclear. We use the egg-laying circuit of C. elegans, which is regulated positively by Gq signaling and negatively by Go signaling, to determine the role of Rho GTPase in G-protein regulation of neural circuit excitability. We previously showed that Trio signals in both the presynaptic Hermaphrodite-Specific command Neurons (HSNs) and the postsynaptic egg-laying vulval muscles they innervate to promote egg laying. Here we show that its effector Rho similarly signals both pre- and post-synaptically. Expression of constitutively active Rho-1(G14V) in either the HSNs or vulval muscles increased their Ca2+ activity and stimulated egg laying. Conversely, reducing Rho signaling in the HSNs via expression of Rho-1(T19N) or C3 transferase expression inhibited HSN Ca2+ activity and egg laying. Animals lacking Rho function in HSN still had vulval muscle Ca2+ activity that supported contraction and egg release, although this Ca2+ activity lacked the enhancement associated with HSN neurotransmitter release, suggesting it was largely driven by stretch-dependent feedback. Together, these results show that Rho signals in both the HSNs and vulval muscles to promote excitability and egg laying. Article SummaryRho-type G-proteins have been extensively studied for their role in neural development, but accumulating evidence points to a role in regulating synaptic transmission and neural circuit activity. Using powerful in vivo imaging and genetic techniques in the nematode Caenorhabditis elegans, the authors show that Rho signals in both presynaptic neurons and postsynaptic muscles to promote circuit activity and behavior. These findings solidify Rho as a key regulator of neurotransmitter signaling.

neuroscience↗

bioRxiv2025

EPHA2-dependent Ephrin-B1 signaling supports self-renewal ability and recurrence of oral cancer

Eph-Ephrin pathway that drives a bidirectional signaling regulates a plethora of biological activities with its varied level of complexity. While the canonical trans-interaction of the ligand and receptor on neighboring cells initiates forward signaling that brings about biological activities, their cis-interaction on the same cell attenuates the forward signaling, modulating the biological effects. Yet, non-canonical cis-interactions with heterotypic surface proteins and kinases regulate certain biological effects. In cancer, the canonical signaling is believed to be tumor suppressive, while the ligand-independent non-canonical signaling drives tumor progression and poor prognosis. Self-renewal ability of cancer cells is a major underlying cause of recurrence and poor prognosis of cancer. Using SILAC-based proteomics, we identified Ephrin-B1 signaling as a crucial regulator of oral cancer self-renewal. Though Ephrin-B1 is known to regulate normal stem cells, its role in cancer remains underexplored. Our biochemical analyses show that Ephrin-B1 binds to a nonconventional receptor EPHA2, which is known to regulate cancer stem cells (CSCs), though the mechanism is less explored. Contrary to the belief that the cis-interaction of receptors and ligands is a means to block functional signaling, our immunoprecipitation, FRET facilitated photoswitching analysis, proximity ligation assay, and in vitro kinase assay provide evidence for the Ephrin-B1-EPHA2 cis-interaction leading to the phosphorylation of EphrinB1 at Y324/329 and Y317. Extreme limiting dilution assay in vitro and in vivo confirmed that this cis-interaction promotes CSC enrichment. Substantiating our in vitro results, mouse orthotopic models showed that Ephrin-B1/EPHA2 interaction regulates prognosis. The clinical relevance of the finding was validated using a TCGA data set and immunohistochemical analysis of tissue microarray using samples from oral cancer patients with recurrence in comparison to patients, who showed disease-free survival. Given that Ephrin-B interacts with EphB for normal stem cell homeostasis, this unconventional EPHA2/Ephrin-B1 cis-interaction, specifically manifested in CSC niches, might serve as an attractive target for therapy, warranting further validation.

cancer biology↗

bioRxiv2024

DeepGraphMut: A graph-based deep learning methodfor cancer prognosis using somatic mutation profile

Cancer remains a leading cause of morbidity and mortality worldwide. Despite advances in genomics, identifying clinically relevant subtypes of cancer remains challenging due to its complex and heterogeneous nature. In this work, we propose DeepGraphMut (DGM), a novel graph-based deep-learning pipeline that integrates somatic mutation data with protein-protein interaction (PPI) networks. By employing a graph autoencoder with a graph attention layer and a node-level attention decoder, DGM generates patient-specific clinically relevant encodings for unsupervised and supervised tasks. We demonstrate the effectiveness of DGM across 16 cancer types comprising of 7352 samples from The Cancer Genome Atlas (TCGA). Unsupervised clustering reveals distinct subtypes with significant survival differences in 11 cancer types. In supervised analysis using a Cox regression model, DGM demonstrates excellent performance in predicting survival outcomes, achieving a high concordance index (c-index) value in the range of 0.7 across most cancers, underscoring its robust predictive performance using only somatic mutation data. Furthermore, DGM outperforms its lightweight variant and the network-based stratification method in both unsupervised and supervised analyses. In summary, this study presents a promising approach for cancer subtype identification and prognosis, especially in resource-limited settings where multi-omics data may not be readily available. By leveraging the strengths of graph learning and network biology, DGM offers a valuable tool for advancing personalized medicine.

systems biology↗

bioRxiv2024

Targeting of SUMOylation leads to cBAF complex stabilization and disruption of the SS18::SSX transcriptome in Synovial Sarcoma

Synovial Sarcoma (SS) is driven by the SS18::SSX fusion oncoprotein. and is ultimately refractory to therapeutic approaches. SS18::SSX alters ATP-dependent chromatin remodeling BAF (mammalian SWI/SNF) complexes, leading to the degradation of canonical (cBAF) complex and amplified presence of an SS18::SSX-containing non-canonical BAF (ncBAF or GBAF) that drives an SS-specific transcription program and tumorigenesis. We demonstrate that SS18::SSX activates the SUMOylation program and SSs are sensitive to the small molecule SAE1/2 inhibitor, TAK-981. Mechanistically, TAK-981 de-SUMOylates the cBAF subunit SMARCE1, stabilizing and restoring cBAF on chromatin, shifting away from SS18::SSX-ncBAF-driven transcription, associated with DNA damage and cell death and resulting in tumor inhibition across both human and mouse SS tumor models. TAK-981 synergized with cytotoxic chemotherapy through increased DNA damage, leading to tumor regression. Targeting the SUMOylation pathway in SS restores cBAF complexes and blocks the SS18::SSX-ncBAF transcriptome, identifying a therapeutic vulnerability in SS, positioning the in-clinic TAK-981 to treat SS.

cancer biology↗

bioRxiv2024

DEEP LEARNING APPROACH FOR RENAL CELL CARCINOMA DETECTION,SUBTYPING, AND GRADING

We propose a comprehensive end-to-end pipeline designed for the detection, subtyping, and grading of tumors. Our proposed method-ology initiates the generation of a heat map, indicating the severity of the tumor. Subsequently, the identification of the most critical patches is conducted based on the probability scores. These identified patches are then directed to a grade prediction network. A distinctive aspect of our research lies in being the first to explore an end-to-end pipeline for both heat map generation and grading prediction. Our experiments were conducted leveraging the public, The Cancer Genome Atlas (TCGA) repository, focusing specifically on renal cancer. We introduced additional patch-level labels to improve the model performance. The generation of tumor heat maps targeted three primary cancer subtypes: clear cell, papillary, and chromophobe. To enhance our approach, we implemented center-loss and introduced a method aimed at refining the quality of patches. The experimental outcomes highlight superior performance compared to state-of-the-art method. This research contributes to the advancement of tumor detection and grading, emphasizing the significance of an integrated approach for heat map generation and grading pre-diction.

bioinformatics↗

bioRxiv2024

Identification of Mitosis Stages Using Artificial Neural Networks for 3D Time Lapse Cell Sequences

Cells, the fundamental units of life, are central to medical research, particularly in cancer studies due to their rapid, uncontrolled division. Understanding cell behavior is crucial, with a focus on mitosis, which has distinct cell division stages. However, precise detection of these phases, especially mitosis initiation in 3D, remains an underexplored research area. Our work explores 3D cell behavior, leveraging the increasing computational capabilities and prevalence of 3D imaging techniques. We introduce diverse 3D Convolutional Neural Network (CNN) architectures such as a base 3D CNN model, 3D CNN binary model, and 3D CNN pairwise model. An ensemble model based on the 3D CNN architectures shows higher classification accuracy on two time-series datasets. This research gives better insights into understanding cell behaviour in a multidimensional manner, contributing to medical research. To the best of our understanding, we are the first to delve into the utilization of Convolutional Neural Network architectures for the 3D classification of mitosis stages.

cell biology↗

bioRxiv2024

CELL CYCLE STATE PREDICTION USING GRAPH NEURAL NETWORKS

Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organisms health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semiautomated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques.

cell biology↗

bioRxiv2023

Automatic Detection of Cell-cycle Stages using Recurrent Neural Networks

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well, to quantify the amount of misclassification. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

bioinformatics↗

bioRxiv2023

Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

Training deep-learning models for biomedical images has always been a problem due to the lack of annotated data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcript, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from LiveCellMiner and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training.

bioinformatics↗

bioRxiv2022

END-TO-END CLASSIFICATION OF CELL-CYCLE STAGES WITH CENTER-CELL FOCUS TRACKER USING RECURRENT NEURAL NETWORKS

Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Consequently, a large number of manual and semi-automated time-lapse microscopy image analyses of mitosis have been carried out in recent years. In this paper, we propose a method for automatic detection of cell-cycle stages using a recurrent neural network (RNN). An end-to-end model with center-cell focus tracker loss, and classification loss is trained. The evaluation was conducted on two time-series datasets, with 6-stages and 3-stages of cell splitting labeled. The frame-to-frame accuracy was calculated and precision, recall, and F1-Score were measured for each cell-cycle stage. We also visualized the learned feature space. Image reconstruction from the center-cell focus module was performed which shows that the network was able to focus on the center-cell and classify it simultaneously. Our experiments validate the superior performance of the proposed network compared to a classifier baseline.

bioinformatics↗

bioRxiv2022

Insights into cargo sorting by SNX32 in neuronal and non-neuronal cells: physiological implications in neurite outgrowth

Sorting nexins (SNX) are a family of proteins containing the Phox homology domain, which shows a preferential endo-membrane association and regulates cargo sorting processes. Even with the vast amount of information unveiled systematically, the underlying mechanism of sorting remains elusive. Here, we established that SNX32, a SNX-BAR (Bin/Amphiphysin/Rvs) sub-family member, is associated with SNX4 via its BAR domain. We identified A226, Q259, E256, R366 of SNX32, and Y258, S448 of SNX4 at the interface of these two SNX proteins that are important for maintaining the association. Via its PX domain, SNX32 interacts with the Transferrin receptor (TfR) and Cation Independent Mannose-6-Phosphate Receptor (CIMPR). We showed that the conserved F131 in its PX domain is important in stabilising the above interactions. Silencing of SNX32 led to a defect in intracellular trafficking of TfR and CIMPR, which could be rescued by overexpressing shRNA-resistant snx32. We also showed that both individual domains play an essential role in trafficking. Our results indicate that SNX4, SNX32 and Rab11 may participate in a common pathway regulating transferrin trafficking; however, the existence of an independent pathway for Rab11 and SNX32 could not be completely ruled out. Further, we established that the PX domain of SNX32 could bind to PI(3)P and PI(4)P, suggesting a possible explanation for its sub-cellular localization. Taken together, our study showed that SNX32 mediate the trafficking of specific cargo molecules along distinct pathway via its PX domain-directed binding to phosphoinositides and its BAR domain-mediated association with other SNX family members. Further, using SILAC-based differential proteomics of the wild type and the mutant SNX32, impaired in cargo binding, we identified Basigin (BSG), an immunoglobulin super family member, as a potential interactor of SNX32 in SHSY5Y cells. We then demonstrated that SNX32 binds to BSG through its PX domain and facilitates its trafficking to the cell surface. In Neuro-Glial cell lines, the silencing of SNX32 led to defects in neuronal differentiation. Moreover, abrogation in lactate transport in the SNX32 depleted cells led us to propose that the SNX may contribute to maintaining the neuro-glial coordination via its role in BSG trafficking and the associated Monocarboxylate transporter activity.

cell biology↗

bioRxiv2022

Phosphotungstic acid (PTA) preferentially binds to collagen-rich regions of porcine carotid arteries and human atherosclerotic plaques using 3D micro-computed tomography (CE-μCT)

Background and aimsAtherosclerotic plaque rupture in the carotid artery can cause small emboli to travel to cerebral arteries, causing blockages and preventing blood flow leading to stroke. Contrast enhanced micro computed tomography (CECT) using a novel stain, phosphotungstic acid (PTA) can provide insights into the microstructure of the vessel wall and atherosclerotic plaque, and hence their likelihood to rupture. Furthermore, it has been suggested that collagen content and orientation can be related to mechanical integrity. This study aims to build on existing literature and establish a robust and reproducible staining and imaging technique to non-destructively quantify the collagen content within arteries and plaques as an alternative to routine histology. MethodsPorcine carotid arteries and human atherosclerotic plaques were stained with a concentration of 1% PTA staining solution and imaged using MicroCT to establish the in-situ architecture of the tissue and measure collagen content. A histological assessment of the collagen content was also performed from picrosirius red (PSR) staining. ResultsPTA stained arterial samples highlight the reproducibility of the PTA staining and MicroCT imaging technique used with a quantitative analysis showing a positive correlation between the collagen content measured from CECT and histology. Furthermore, collagen-rich areas can be clearly visualised in both the vessel wall and atherosclerotic plaque. 3D reconstruction was also performed showing that different layers of the vessel wall and various atherosclerotic plaque components can be differentiated using Hounsfield Unit (HU) values. ConclusionsThe work presented here is unique as it offers a quantitative method of segmenting the vessel wall into its individual components and non-destructively quantifying the collagen content withing these tissues, whilst also delivering a visual representation of the fibrous structure using a single contrast agent. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioengineering↗

bioRxiv2022

Modulation of the gut microbiome by novel synthetic glycans for the production of propionate and the reduction of cardiometabolic risk factors

BackgroundIncreasing evidence indicates that an altered gut microbiome participates in the development of cardiometabolic syndrome and associated risk factors, such as insulin resistance, dyslipidemia, and obesity, and that targeting the gut microbiome is a promising strategy to lower the risk for cardiometabolic diseases. Part of this reduction is mediated by specific metabolites generated by the gut microbiome. Propionate, a short-chain fatty acid (SCFA) produced from dietary glycans by certain gut microbes is known to exert multiple beneficial metabolic effects. Here, we identify KB39, a novel gut microbiome-targeting synthetic glycan selected for its strong propionate-producing capacity, and demonstrate its effects in vivo to reduce cardiometabolic disease using western diet-fed LDL receptor knock-out mice. MethodsEx vivo fermentation screening of a large library of synthetic glycan ensembles was performed using gut microbiome communities from healthy subjects and overweight patients with type 2 diabetes. A synthetic glycan identified for its high propionate-producing capacity (KB39) was then tested in vivo for effects on systemic, blood and cecal metabolic parameters in Ldlr-/- mice fed a western diet. ResultsEx vivo screening of ~600 synthetic glycans using human gut microbiota from healthy subjects and patients with type 2 diabetes identified a novel glycan (KB39) with high propionate-producing capacity that increased propionate contribution to total SCFA and propionate-producing bacterial taxa compared to negative control. In western diet-fed Ldlr-/- mice, KB39 treatment resulted in an enrichment in propiogenic bacteria and propionate biosynthetic genes in vivo and an increase in absolute and relative amounts of propionate in the cecum. This also resulted in significant decreases in serum total cholesterol, LDL-cholesterol, and insulin levels, as well as reduced hepatic triglycerides and cholesterol content compared to non-treated animals. Importantly, KB39 treatment significantly reduced atherosclerosis, liver steatosis and inflammation, upregulated hepatic expression of genes involved in fatty acid oxidation and downregulated transcriptional markers of inflammation, fibrosis and insulin resistance with only a mild lowering of body weight gain. ConclusionsOur data show that KB39, a novel synthetic glycan supporting a high propionate-producing microbiome, can reduce cardiometabolic risk factors and disease in mice and suggest this approach could be of benefit for the prevention or treatment of cardiometabolic diseases in humans. CLINICAL PERSPECTIVEWhat is new? O_LIA novel synthetic glycan, KB39, was selected from a library of compounds for its high propionate-producing capacity and beneficial effects on the human gut microbiome composition C_LIO_LIKB39 modulates the gut microbiome for high propionate production and significantly reduces cardiometabolic risk factors and disease in a murine model of cardiometabolic diseases C_LI What are the clinical implications? O_LIKB39, delivered orally, could be of benefit for the prevention or treatment of cardiometabolic diseases in humans C_LIO_LIThe efficacy of KB39 in mice compared to the clinical drug fenofibrate justifies further study in humans C_LI

microbiology↗

bioRxiv2021

Improved Genome Packaging Efficiency of AAV Vectors Using Rep Hybrids

Recombinant Adeno-associated viruses (rAAVs) are one of the most commonly used vectors for a variety of gene therapy applications. In the last two decades research focused primarily on the characterization and isolation of new cap genes resulting in hundreds of natural and engineered AAV capsid variants while the rep gene, the other major AAV open reading frame, has been less studied. This is due to the fact that the rep gene from AAV serotype 2 (AAV2) enables the ssDNA packaging of recombinant genomes into most AAV serotype and engineered capsids. However, a major byproduct of all vector productions is empty AAV capsids, lacking the encapsidated vector genome, especially for non-AAV2 vectors. Despite the packaging process being considered the rate-limiting step for rAAV production, none of the rep genes from the other AAV serotypes have been characterized for their packaging efficiency. Thus, in this study AAV2 rep was replaced with the rep gene of a select number of AAV serotypes. However, this led to a lowering of capsid protein expression, relative to the standard AAV2-rep system. In further experiments the 3end of the AAV2 rep gene was reintroduced to promote increased capsid expression and a series of chimeras between the different AAV Rep proteins were generated and characterized for their vector genome packaging ability. The utilization of these novel Rep hybrids increased the percentage of genome containing (full) capsids ~2-4-fold for all of the non-AAV2 serotypes tested. Thus, these Rep chimeras could revolutionize rAAV production. ImportanceA major byproduct of all Adeno-associated virus (AAV) vector production systems are "empty" capsids, void of the desired therapeutic gene, and thus do not provide any curative benefit for the treatment of the targeted disease. In fact, empty capsids can potentially elicit additional immune responses in vivo gene therapies if not removed by additional purification steps. Thus, there is a need to increase the genome packaging efficiency and reduce the number of empty capsids from AAV biologics. The novel Rep hybrids from different AAV serotypes described in this study are capable of reducing the percentage of empty capsids in all tested AAV serotypes and improve overall yields of genome-containing AAV capsids at the same time. They can likely be integrated easily into existing AAV manufacturing protocols to optimize the production of the generated AAV gene therapy products.

molecular biology↗

bioRxiv2019

Adverse childhood experiences in families with multiple members diagnosed to have psychiatric illnesses

ObjectiveAdverse Childhood Experiences (ACEs) are linked to the development of a number of psychiatric illnesses in adulthood. Our study examined the pattern of ACEs and their relation to the age of onset (AAO) of major psychiatric conditions in individuals from families that had [≥] 2 first degree relatives with major psychiatric conditions (multiplex families) identified as part of an ongoing longitudinal study.\n\nMethodsOur sample consisted of 509 individuals from 215 families. Of these, 268 were affected i.e diagnosed with bipolar disorder (BPAD) (n=61), obsessive-compulsive disorder (OCD) (n=58), schizophrenia (n=52), substance dependence (SUD) (n=59), or co-occurring diagnoses (n=38); while 241 were at-risk first degree relatives (FDRs) who were either unaffected (n=210) or had other depressive or anxiety disorders (n=31). All individuals were evaluated using the Adverse Childhood Experiences - International Questionnaire (ACE-IQ) and ACE binary and frequency scores were calculated.\n\nResultsIt was seen that affected males, as a group, had the greatest ACE scores in our sample. A cox mixed-effects model fit by gender revealed that higher ACE binary and frequency scores were associated with significantly increased risk for an earlier AAO of psychiatric diagnoses in males. A similar model that evaluated the effect of diagnosis revealed an earlier AAO in OCD and SUD, but not in schizophrenia and BPAD.\n\nConclusionsOur study indicates that ACEs brought forward the onset of major psychiatric conditions in men and in individuals diagnosed with OCD and SUD. Ongoing longitudinal assessments in FDRs from these families are expected to identify mechanisms underlying this relationship.

neuroscience↗