Biology subjects

Jose, J.

Publications and source records attributed to Jose, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Heterotrimeric Gβ subunit orchestrates flavonoid metabolism governing drought-tolerance in groundnut (Arachis hypogaea L.)

Groundnut (Arachis hypogaea) is an important oilseed crop, and its production is severely affected by drought worldwide. Improving the drought-tolerance of groundnut without compromising its agronomic traits is a major challenge for researchers. Here, a meta-transcriptome analysis identified tandemly duplicated G{beta} subunits (AhG{beta}1 and AhG{beta}3) as early signaling genes under water deficit conditions. Heterotrimeric G-protein {beta}-subunit is a major signaling element that can regulate diverse responses in plants. Heterologous complementation studies in Arabidopsis G{beta} mutant (agb1-2) demonstrated evolutionary conservation of AhG{beta} function, while the transgenic overexpression and RNAi knockdown in groundnut established AhG{beta}1 as a positive regulator of plant growth, yield, and drought tolerance. Integrated transcriptomic, transcription factor regulatory network, and metabolomic analyses demonstrated that AhG{beta}1 coordinates drought-responsive transcriptional and metabolic reprogramming characterized by suppression of cell wall-associated processes and activation of phenylpropanoid, flavonoid, and isoflavonoid biosynthesis. This reprogramming resulted in enhanced accumulation of antioxidant flavonoids, including quercetin and rutin, reduced ROS accumulation, and improved drought tolerance. Furthermore, exogenous application of these flavonoids enhanced drought recovery, highlighting their role as protective downstream metabolites. Collectively, our findings establish AhG{beta}1 as a conserved regulator linking early G-protein signaling with transcriptional and metabolic reprogramming to maintain redox homeostasis during drought stress and identify promising molecular and metabolic targets for improving drought resilience in groundnut.

plant biology↗

bioRxiv2024

Ancestral aryl sulfatases in the alkaline phosphatase family allow for greater increases in catalytic efficiency than extant variants

Characterizing the dynamics and functional shifts during protein evolution is essential, both for understanding protein evolution and for rationalizing efficient strategies for e.g. enzymes with desired and effective functions. Most proteins organize in families, sets of divergent sequences which share a common ancestor and have a similar structural fold. We study aryl sulfatases (ASs), a subfamily of the large and evolutionary old alkaline phosphatase superfamily. In this manuscript we present how ultrahigh-throughput droplet micro-fluidics can be used for studying aryl sulfatases and their computationally reconstructed putative common ancestors. We compare the evolvability and robustness of three ancestors and four extant ASs which all exhibit catalytic promiscuity towards a range of substrate classes. Fourteen libraries with varying mutation rates were expressed in single-cell microdroplets. In general, higher mutation rates resulted in wider distribution of active variants but fewer improved variants overall. However, the impact of mutation rate differed between enzymes, with some benefiting from higher and others from lower mutation rate, underscoring the need to test diverse mutagenesis regimes.

evolutionary biology↗

bioRxiv2024

Comprehensive Unbiased Analysis of Vascular Tissue Changes in Accelerated Atherosclerosis Using High-Resolution Ultrasound combined with Photoacoustic Imaging

Venous bypass grafts are commonly used to circumvent complex coronary or peripheral artery occlusions. The patency rates, however, are hampered due to accelerated buildup of atherosclerotic lesions in the vein graft wall. Identification of unstable plaques is crucial to guide clinical decision making. In this study, we employ advanced high-resolution ultrasound (US) coupled with spectral photoacoustic imaging (sPAI) to enhance the accurate visualization and analysis of tissue composition in vivo. By applying unbiased spectral analysis, we investigate the composition and plaque instability in a murine vein graft model. MethodMale hypercholesterolemic ApoE3*Leiden mice and normocholesterolemic C57BL/6 mice underwent vein graft surgery in which a caval vein from a donor mouse was interpositioned into the arterial circulation of a recipient at the sight of the right common carotid artery. US imaging with sPAI was conducted on 7, 14, 21, and 28 days after surgery. Spectral curves from the near-infrared (NIR) I region, spanning 680 to 970nm, were extracted using a data-driven approach. Component discovery and cross-correlation analysis were performed with Matlab, and ImageJ reconstructed the components within 3D images. At the endpoint histological analysis of the vein grafts was performed. ResultsAnalysis of the NIRI region revealed distinct components, with 7 and 10 components tested in the cross-correlation map. Relative abundance values identified melanin, oxidized hemoglobin, deoxygenized hemoglobin, lipids, and collagen. Lipids and collagen spectra accurately identified lipid and collagen-rich tissues in vivo. The sPAI analysis of of the vein graft wall in vivo resulted in a 8.7% lipids in the vein graft wall compared to 1.8% lipids in the histological analysis at t=28d. For vein grafts from ApoE*3-Leiden mice no differences in the lipid positive area was observed between the sPAI analysis or histological quantification. The percentages collagen present in the vein graft walls from both strains analyzed via sPAI and histological showed comparable results at t=28d. ConclusionOur study demonstrates that sPAI can be utilized for compositional analysis of murine tissue in an unbiased manner. This methodology can be used to enhance our understanding of vein graft dynamics and holds promise to advance non-invasive characterization of vascular diseases to ultimately guide clinical decision making.

bioinformatics↗

bioRxiv2024

Immunity and bacterial recruitment in plant leaves are parallel processes whose link shapes sensitivity to temperature stress

Rising global temperatures necessitate developing resilient crops with better adaptability to changing climates. Under elevated temperatures, plant immunity is downregulated, increasing risk of foliar pathogen attack. Manipulating plant defense hormones is one way to mitigate this detrimental effect. However, it is unclear how plant immunity interacts with plant microbiome assembly and how temperature will thus affect overall plant health and stability. In this study, we compared two Arabidopsis thaliana genotypes that feature divergent strategies for recruitment of commensal bacteria from natural soil. NG2, an A. thaliana ecotype we collected from Jena, Germany, was grown in its native soil and compared to CLLF, a genotype that recruits higher bacterial loads and higher bacterial diversity but without any dysbiotic phenotype. CLLF hyperaccumulates salicylic acid (SA) and jasmonates, has constitutively upregulated innate defenses, and shows increased resistance to necrotrophic fungal and hemi-biotrophic bacterial pathogens, indicating that pathogen immunity and non-pathogen recruitment function in parallel. Some of its leaf bacteria can utlize SA as a carbon source, suggesting that immunity and recruitment may even be linked by chemical hormones. CLLF exhibits high tolerance to heat stress in comparison to the NG2, with SA-associated defense processes remaining active under heat. Synthetic community (SynCom) experiments revealed that when the taxonomic diversity of bacteria available to CLLF is artificially reduced, resilience to heat stress is compromised, leading to dysbiosis. However, this dysbiosis does not occur in CLLF with a full SynCom or in the NG2 with any SynCom. These findings suggest that the downregulation of defenses in response to heat may contribute to the avoidance of dysbiosis caused by certain leaf bacteria, while full bacteriome taxonomic diversity can help maintain balance.

microbiology↗

bioRxiv2024

Unveiling genetic anchors in Saccharomyces cerevisiae: QTL mapping identifies IRA2 as a key player in ethanol tolerance and beyond

Ethanol stress in Saccharomyces cerevisiae is a well-studied phenomenon, but pinpointing specific genes or polymorphisms governing ethanol tolerance remains a subject of ongoing debate. Naturally found in sugar-rich environments, this yeast has evolved to withstand high ethanol concentrations, primarily produced during fermentation in the presence of suitable oxygen or sugar levels. Originally a defense mechanism against competing microorganisms, yeast-produced ethanol is now a cornerstone of brewing and bioethanol industries, where customized yeasts require high ethanol resistance for economic viability. However, yeast strains exhibit varying degrees of ethanol tolerance, ranging from 8% to 20%, making the genetic architecture of this trait complex and challenging to decipher. In this study, we introduce a novel QTL mapping pipeline to investigate the genetic markers underlying ethanol tolerance in an industrial bioethanol S. cerevisiae strain. By calculating missense mutation frequency in a prominent QTL region within a population of 1011 S. cerevisiae strains, we uncovered rare occurrences in gene IRA2. Following molecular validation, we confirmed the significant contribution of this gene to ethanol tolerance, particularly in concentrations exceeding 12% of ethanol. IRA2 pivotal role in stress tolerance due to its participation in the Ras-cAMP pathway was further supported by its involvement in other tolerance responses, including thermotolerance, low pH tolerance, and resistance to acetic acid. Understanding the genetic basis of ethanol stress in S. cerevisiae holds promise for developing robust yeast strains tailored for industrial applications.

genetics↗

bioRxiv2024

Missense mutation in the activation segment of the kinase CK2 models Okur Chung neurodevelopmental disorder and alters the hippocampal glutamatergic synapse

Exome sequencing has enabled the identification of causative genes of monogenic forms of autism, amongst them, in 2016, CSNK2A1, the gene encoding the catalytic subunit of the kinase CK2, linking this kinase to Okur-Chung Neurodevelopmental Syndrome (OCNDS), a newly described neurodevelopmental condition with many symptoms resembling those of autism spectrum disorder. Thus far, no preclinical model of this condition exists. Here we describe a knockin mouse model that harbors the K198R mutation in the activation segment of the kinase. This region is a mutational hotspot, representing one-third of patients. These mice exhibit behavioral phenotypes that mirror patient symptoms. Homozygous knock-in (KI) mice die mid-gestation while heterozygous KI mice are born at half of the expected mendelian ratio and are smaller in weight and size than wildtype littermates. Heterozygous KI mice showed alterations in cognition and memory-assessing paradigms, enhanced stereotypies, altered circadian activity patterns, and nesting behavior. Phosphoproteome analysis from brain tissue revealed alterations in the phosphorylation status of major pre- and postsynaptic proteins of heterozygous KI mice. In congruence, we detect reduced synaptic maturation in hippocampal neurons and attenuated long-term potentiation in the hippocampus of KI mice. Taken together, K198R KI mice exhibit significant face validity, presenting ASD-relevant phenotypes, synaptic deficits and alterations in synaptic plasticity, all of which strongly validate this line as a mouse model of OCNDS.

neuroscience↗

bioRxiv2024

ASPYRE-Lung: Validation of a simple, fast, robust and novel method for multi-variant genomic analysis of actionable NSCLC variants in tissue

Genomic variant testing of tumors is a critical gateway for patients to access the full potential of personalized oncology therapeutics. Current methods such as next-generation sequencing are costly and challenging to interpret, while PCR assays are limited in the number of variants they can cover. We developed ASPYRE(R) (Allele-Specific PYrophosphorolysis REaction) technology to address the urgent need for rapid, accessible and affordable diagnostics informing actionable genomic target variants of a given cancer. The targeted ASPYRE-Lung panel for non-small cell carcinoma covers 114 variants in 11 genes (ALK, BRAF, EGFR, ERBB2, KRAS, RET, ROS1, MET & NTRK1/2/3) to robustly inform clinical management. The assay detects single nucleotide variants, insertions, deletions, and gene fusions from tissue-derived DNA and RNA simultaneously. We tested the limit of detection, specificity, analytical accuracy and analytical precision of ASPYRE-Lung using FFPE lung tissue samples from patients with non-small cell lung carcinoma, variant-negative FFPE tissue from healthy donors, and FFPE-based contrived samples with controllable variant allele fractions. The sensitivity of ASPYRE-Lung was determined to be [≤] 3% variant allele fraction for single nucleotide variants and insertions or deletions, 100 copies for fusions, and 200 copies for MET exon 14 skipping. The specificity was 100% with no false positive results. The analytical accuracy test yielded no discordant calls between ASPYRE-Lung and expected results for clinical samples (via orthogonal testing) or contrived samples, and results were replicable across operators, reagent lots, runs, and real-time PCR instruments with a high degree of precision. The technology is simple and fast, requiring only four reagent transfer steps using standard laboratory equipment (PCR and qPCR instruments) with analysis via a cloud-based analysis algorithm. The ASPYRE-Lung assay has the potential to be transformative in facilitating access to rapid, actionable molecular profiling of tissue for patients with non-small cell carcinoma.

cancer biology↗

bioRxiv2023

PandoraRLO: DQN and Graph convolution based method for optimized ligand pose

Predicting how proteins interact with small molecules is a complex and challenging task in the field of drug discovery. Two important aspects in this are shape complementarity and inter molecular interactions which are highly driven by the binding site and the ultimate pose of the ligand in which it interacts with the protein. Various state of the art methods exist which provide a range of ligand poses that are potentially a good fit for a given specific receptor, these are usually compute intensive and expensive. In this study, we have designed a method that provides a single optimized ligand pose for a specific receptor. The method is based on reinforcement learning where when exposed to a diverse protein ligand data set the agent is able to learn the underlying complex biochemistry of the protein ligand pair and provide an optimized pair. As a first study on usage of reinforcement learning for optimized ligand pose, the PandoraRLO model is able to predict pose within a range of 0.5[A] to 4[A] for a large number of test complexes. This indicates the potential of reinforcement learning in uncovering the inherent patterns of protein-ligand pair in 3D space.

bioinformatics↗

bioRxiv2023

Comparative Genomics of Firmicutes reveals probable adaptations for xylose fermentation in Thermoanaerobacterium saccharolyticum

Second-generation (2G) ethanol is one potential biofuel that could be used to achieve the goal of reducing greenhouse gas emissions. Many challenges still need to be overcome for the feasibility of this technology, most of them related to consumption of xylose, a pentose sugar not easily metabolized by industrial microorganisms. Thus, exploring genes, pathways and other organisms that can ferment xylose is a strategy implemented to solve industrial bottlenecks. Thermoanaerobacterium saccharolyticum (T. sac) is an organism from the firmicutes phylum, capable of naturally fermenting compounds of industrial interest, such as xylan and xylose. Understanding evolutionary adaptations may help not only to solidify this bacterium as a potential substitute to the yeast Saccharomyces cerevisiae in industry, but also bring novel genes and information that can be used for yeast, enhance its fermenting capabilities, and increase production of current bio-platforms. This study presents a deep evolutionary study of members of the firmicutes clade, focusing on adaptations that may be related to overall fermentation metabolism, especially for xylose fermentation. One highlight is the finding of positive selection on a xylose binding protein of the xylFGH operon, close to the annotated sugar binding site, with this protein already being found to be expressed in xylose fermenting conditions in a previous study. Results from this study can serve as basis for searching for candidate genes to use in industrial strains or to improve T. sac as a new microbial cell factory, which may help to solve current problems found in the biofuels industry.

evolutionary biology↗

bioRxiv2022

PandoraRL: DQN and Graph Convolution based ligand pose learning for SARS-COV1 Mprotease

The ability to predict the correct ligand binding pose for proteinligand complex is vital for drug design. Recently several machine learning methods have suggested knowledge based scoring functions for binding energy prediction. In this study, we propose a reinforcement learning (RL) based model, PandoraRL, where the RL agent helps the ligand traverse to the optimal binding pose. The underlying representation of molecules utilizes generalized graph convolution to represent the protein ligand complex with various atomic and spatial features. The representation consists of edges formed on the basis of inter molecular interactions such as hydrogen bonds, hydrophobic interactions, etc, and nodes representing atomic features. This study presents our initial model which can train on a protein-ligand pair and predict optimal binding pose for a different ligand with the same protein. To the best of our knowledge, this is the first time an RL based approach has been put forward for predicting optimized ligand pose. CCS CONCEPTSO_LIComputing methodologies [->] Reinforcement learning. C_LI

bioinformatics↗

bioRxiv2022

Shelf-Life Specific Moisture Variation In Chitosan Of Genus Fenneropenaeus Distributed Along Arabian Sea, India

Chitosan is a characteristic polysaccharide, naturally extracted from the crustaceans shells. The stability and shelf-life of chitosan are affected by a few parameters, basically the moisture content. In this study, three species of shrimps such as Indian white shrimp (Fenneropenaeus indicus), banana shrimp (F. merguensis), and tiger prawn (F. monodon) were utilized for the extraction of chitosan. The extraction strategy included the method of demineralization, deproteinization, and deacetylation. Later the yield and moisture of chitosan were examined from three diverse species. The yield of shell waste ranged between 46% to 50% (on moist weight premise) and the chitosan was between 18.0 to 20.2%. Moisture content always plays a critical part in chitosan shelf life and stability and was between 5.2% to 6.8 %.

zoology↗

bioRxiv2021

Conjugation of palbociclib with MHI-148 has an increased cytotoxic effect for breast cancer cells and an altered mechanism of action

The CDK4/6 inhibitor palbociclib, combined with endocrine therapy, has been shown to be effective in postmenopausal women with estrogen receptor positive, HER2-negative advanced or metastatic breast cancer. However, palbociclib is not as effective in the highly aggressive triple-negative breast cancer that lacks sensitivity to chemotherapy or endocrine therapy. We hypothesized that conjugation of the near-infrared dye MHI-148 with palbociclib can produce a potential theranostic in triple-negative as well as estrogen receptor positive breast cancer cells. In our study, the conjugate was found to have enhanced activity in all mammalian cell lines tested in vitro. However, the conjugate was cytotoxic and did not induce G1 cell cycle arrest in breast cancer cells suggesting the mechanism of action differed from the parent compound palbociclib. The study highlights the importance of investigating the mechanism of conjugates of near-infrared dyes to therapeutic compounds as conjugation can potentially result in a change of mechanism or target, with an enhanced cytotoxic effect in this case.

cancer biology↗

bioRxiv2021

Plant-associated microbiomes promote nutrient turnover in impoverished substrates of a biodiversity hotspot

The substrates of the Brazilian campos rupestres have extremely low concentrations of key nutrients, mainly phosphorus, imposing severe restrictions to plant growth. Regardless, this ecosystem harbors enormous biodiversity which raises the question of how nutrients are cycled and acquired by the biosphere. To uncover the nutrient turnover potential of plant-associated microorganisms in the campos rupestres, we investigated the compositions and functions of microbiomes associated with two species of the Velloziaceae family that grow over distinct substrates (soil and rock). Amplicon, metagenomic, and metagenome-assembled genome sequence data showed that the campos rupestres harbor a novel assemblage of plant-associated prokaryotes and fungi. Compositional analysis revealed that the plant-associated soil and rock communities differed in taxonomic structure but shared a core of highly efficient colonizers that were strongly coupled with nutrient mobilization. Investigation of functional and abundance data revealed that the plant hosts actively recruit communities by exuding organic compounds and that the root-associated microbiomes possess a diverse repertoire of phosphorus turnover mechanisms. We also showed that the microbiomes of both plant species encompass novel populations capable of mobilizing nitrogen and that the substrate strongly influences the dynamics of this cycle. Our results show that the interplay between plants and their microbiomes shapes nutrient turnover in the campos rupestres. We highlight that investigation of microbial diversity is fundamental to understand plant fitness in stressful environments.

genomics↗

bioRxiv2021

Reinforcement Learning Based Approach for Ligand Pose Prediction

Identification of the potential binding site and the correct ligand pose are two crucial steps among the various steps in protein ligand interaction for a novel or known target. Currently most of the deep learning methods work on protein ligand pocket datasets for various predictions. In this study, we propose a reinforcement learning (RL) based method for predicting the optimized ligand pose where the RL agent also identifies the binding site based on its training. In order to apply various reinforcement learning techniques, we suggest a novel approach to represent the protein ligand complex using graph CNN which would help utilize both atomic and spatial features. To the best of our knowledge, this is the first time an RL based approach has been put forward for predicting optimized ligand pose.

bioinformatics↗

bioRxiv2021

The Prolyl-tRNA Synthetase Inhibitor Halofuginone Inhibits SARS-CoV-2 Infection

Summary ParagraphWe identify the prolyl-tRNA synthetase (PRS) inhibitor halofuginone1, a compound in clinical trials for anti-fibrotic and anti-inflammatory applications2, as a potent inhibitor of SARS-CoV-2 infection and replication. The interaction of SARS-CoV-2 spike protein with cell surface heparan sulfate (HS) promotes viral entry3. We find that halofuginone reduces HS biosynthesis, thereby reducing spike protein binding, SARS-CoV-2 pseudotyped virus, and authentic SARS-CoV-2 infection. Halofuginone also potently suppresses SARS-CoV-2 replication post-entry and is 1,000-fold more potent than Remdesivir4. Inhibition of HS biosynthesis and SARS-CoV-2 infection depends on specific inhibition of PRS, possibly due to translational suppression of proline-rich proteins. We find that pp1a and pp1ab polyproteins of SARS-CoV-2, as well as several HS proteoglycans, are proline-rich, which may make them particularly vulnerable to halofuginones translational suppression. Halofuginone is orally bioavailable, has been evaluated in a phase I clinical trial in humans and distributes to SARS-CoV-2 target organs, including the lung, making it a near-term clinical trial candidate for the treatment of COVID-19.

microbiology↗

bioRxiv2021

Adaptive evolution of Moniliophthora PR-1 proteins towards its pathogenic lifestyle

Moniliophthora perniciosa and Moniliophthora roreri are hemibiotrophic fungi that harbor a large number of Pathogenesis-Related 1 genes, many of which are induced in the biotrophic interaction with Theobroma cacao. Here, we provide evidence that the evolution of PR-1 in Moniliophthora was adaptive and potentially related to the emergence of the parasitic lifestyle in this genus. Phylogenetic analysis revealed conserved PR-1 genes, shared by many Agaricales saprotrophic species, that have diversified in new PR-1 genes putatively related to pathogenicity in Moniliophthora, as well as in recent specialization cases within both species. PR-1 families in Moniliophthora with higher evolutionary rates exhibit induced expression in the biotrophic interaction and positive selection clues, supporting the hypothesis that these proteins accumulated adaptive changes in response to host-pathogen arm race. Furthermore, we show that the highly diversified MpPR-1 genes are not induced by two phytoalexins, suggesting detoxification might not be their main function as proposed before.

evolutionary biology↗

bioRxiv2020

A Synthetic Defective Interfering SARS-CoV-2

Viruses thrive by exploiting the cells they infect but must also produce viral proteins to replicate and infect other cells. As a consequence, they are also susceptible to exploitation by defective versions of themselves that do not produce such proteins. A defective viral genome with deletions in protein-coding genes could still replicate in cells coinfected with full-length viruses, and even replicate faster due to its shorter size, interfering with the replication of the virus. We have created a synthetic defective interfering version of SARS-CoV-2, the virus causing the recent Covid-19 pandemic, assembling parts of the viral genome that do not code for any functional protein but enable it to be replicated and packaged. This synthetic defective genome replicates three times faster than SARS-CoV-2 in coinfected cells, and interferes with it, reducing the viral load of a cell by half in 24 hours. The synthetic genome is transmitted as efficiently as the full-length genome, confirming the location of the putative packaging signal of SARS-CoV-2. A version of such a synthetic construct could be used as a self-promoting antiviral therapy: by enabling replication of the synthetic genome, the virus promotes its own demise. O_FIG O_LINKSMALLFIG WIDTH=153 HEIGHT=200 SRC="FIGDIR/small/393587v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@60aa77org.highwire.dtl.DTLVardef@57a965org.highwire.dtl.DTLVardef@132574dorg.highwire.dtl.DTLVardef@18e2e_HPS_FORMAT_FIGEXP M_FIG Graphic summary C_FIG

synthetic biology↗

bioRxiv2020

Evaluation of Safety and Immunogenicity of an Adjuvanted, TH-1 Skewed, Whole Virion Inactivated SARS-CoV-2 Vaccine - BBV152

We report the development and evaluation of safety and immunogenicity of a whole virion inactivated SARS-COV-2 vaccine (BBV152), adjuvanted with aluminium hydroxide gel (Algel), or a novel TLR7/8 agonist adsorbed Algel. We used a well-characterized SARS-CoV-2 strain and an established vero cell platform to produce large-scale GMP grade highly purified inactivated antigen, BBV152. Product development and manufacturing were carried out in a BSL-3 facility. Immunogenicity was determined at two antigen concentrations (3g and 6g), with two different adjuvants, in mice, rats, and rabbits. Our results show that BBV152 vaccine formulations generated significantly high antigen-binding and neutralizing antibody titers, at both concentrations, in all three species with excellent safety profiles. The inactivated vaccine formulation containing TLR7/8 agonist adjuvant-induced Th1 biased antibody responses with elevated IgG2a/IgG1 ratio and increased levels of SARS-CoV-2 specific IFN-{gamma}+ CD4 T lymphocyte response. Our results support further development for Phase I/II clinical trials in humans.

immunology↗