Biology subjects

Yadav, P.

Publications and source records attributed to Yadav, P..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne's disease

Johnes disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic enteric disease with significant economic impacts on the dairy industry. To elucidate molecular mechanisms underlying MAP infection in desi cattle (Bos indicus), we conducted a comprehensive transcriptomic study. This revealed 1,905 protein-coding and 3,123 lncRNA genes as differentially expressed in MAP-infected samples, alongside the first comprehensive annotation of 45,947 lncRNAs in desi cattle. Weighted Gene Co-expression Network Analysis identified 11 co-expressed gene modules, with the turquoise module comprising 870 protein- coding genes and 934 lncRNAs, highly correlated with clinical traits. Functional enrichment analysis of this key module revealed significant involvement in defense response, inflammatory processes, and NK cell-mediated immunity and cytotoxicity. GSE analysis revealed suppressed pathways, including NK cell immunity and lectin response, facilitating bacterial persistence in chronic infection, while activated pathways included G-protein-coupled receptor signaling and metabolic pathways. Network approach identified 12 hub genes (IL7R, TLR4, KLRK1, IFNG, TGFB1, CD68, CXCR6, GZMB, KLRG1, MMP9, GZMA, SELL) associated with immune suppression, inflammation, and tissue remodeling. Additionally, 22 lncRNAs co-expressed with hub genes suggesting roles in modulating immune regulation. By identifying critical genes and lncRNAs, our study offers potential targets for developing innovative diagnostic and therapeutic strategies for JD.

systems biology↗

bioRxiv2025

Sensory Appendage Protein triggers alarm to pyrethroid in Indian malarial vector Anopheles culicifacies

Sensory appendage proteins (SAPs) serve as molecular arms of odorant-binding protein family members and mediate chemical communication from the external to the internal environment of the insect body. We recently reported that SAP members might have an important role in blood-feeding and/or insecticide resistance-associated behavioral physiologies, but the mechanism remains unclear. Here we show how SAP contributes (i) to the host attraction during navigation for blood-feeding, and (ii) triggers an alert via integrated actions of eyes and peripheral sensory organs to circumvent the toxic effect of insecticide in the susceptible strain of An. culicifacies. Among the six identified SAP members, AcSAP1 and AcSAP2 are abundantly expressed in the olfactory and legs, and their expression is significantly modulated at night in naive adult female mosquitoes. We noticed that AcSAP1 silencing significantly impairs the host-attraction properties of the mosquitoes. Its strong binding affinity to the synthetic pyrethroid prompted us to evaluate the AcSAP1 response to insecticide exposure. Confocal microscopy and molecular profiling data reveal a heightened modulation in the expression of AcSAP1 in eyes, and other peripheral sensory organs including the olfactory (****p<0.00001), legs (**p < 0.004), wings (***p < 0.0004). These findings collectively suggest a crucial role of AcSAP1 in facilitating mosquito navigation, and/or insecticide avoidance. Unraveling the basis of SAP interactions with peripheral sensory systems could pave the way for the development of novel vector control tools.

animal behavior and cognition↗

bioRxiv2024

Schistosome esophageal gland factor MEG-8.2 drives host cell lysis and interacts with host immune proteins

Schistosomes are blood flukes that ingest large amounts of host blood during their intra-mammalian stage. The ingested blood contains leukocytes that can be harmful, yet the parasites survive inside the host for decades, reflecting superb immune evasion mechanisms that remain poorly understood. Our previous work discovered that FoxA, a forkhead transcription factor, drives the production of the esophageal gland, an anterior digestive organ essential for degrading the ingested leukocytes and for in vivo survival. However, a comprehensive molecular makeup of the esophageal gland remains unclear. Importantly, which of the esophageal gland factors are responsible for degrading the ingested leukocytes, their mechanism of action, and how such a function relates to parasite survival and immune evasion remains unknown. Here, we identify additional esophageal gland genes by taking a comparative transcriptomics approach to identify transcripts altered in foxA knockdown adult schistosomes. A targeted RNAi screen coupled with biochemistry reveals that specific domains of the micro-exon gene MEG-8.2, can drive host cell lysis in a concentration-dependent manner. Using pull-down assays coupled with mass spectrometry, we discover that MEG-8.2 interacts with several host membrane and extracellular proteins that play important roles in activating innate and/or adaptive immunity. Together, our findings suggest a dual role for MEG-8.2 in effectively lysing the ingested cells in the esophageal lumen and interacting with specific host proteins to neutralize or suppress the host immunity. These findings lay an important foundation for exploiting esophageal gland factors to treat schistosomiasis.

microbiology↗

bioRxiv2024

PRMT5-mediated histone methylation regulates alternative splicing via MECP2-PTBP1 to promote EMT in breast cancer hypoxia

Tumor hypoxia induced alterations in the epigenetic landscape and alternative splicing influence cellular adaptations. PRMT5 is a type II protein arginine methyltransferase that regulates several tumorigenic events in many cancer types. However, the regulation of PRMT5 and its direct implication on aberrant alternative splicing under hypoxia remains unexplored. In this study, we observed hypoxia induced upregulation of PRMT5 via the CCCTC binding factor, CTCF. Further, PRMT5-mediated symmetric arginine dimethylation H4R3me2s and H3R8me2s directly regulated the alternative splicing of Transcription Factor 3 (TCF3). Under hypoxia, PRMT5-mediated histone dimethylation at the intronic conserved region (ICR) present between TCF3 exon 18a and exon 18b recruits DNA methyltransferase 3A (DNMT3A), resulting in DNA methylation. DNA methylation at the TCF3-ICR is recognized and bound by Methyl CpG binding protein (MECP2) resulting in RNA-Pol II pausing, promoting the recruitment of the negative splicing factor PTBP1 to the splicing locus of TCF3 mRNA. PTBP1 promotes the exclusion of exon 18a which results in the production of the pro-invasive TCF3-18B (E47) isoform which promotes EMT and invasion of breast cancer cells under hypoxia. Collectively, our results indicate PRMT5-mediated symmetric arginine dimethylation of histones regulates alternative splicing of TCF3 gene thereby enhancing EMT and invasion in breast cancer hypoxia.

cancer biology↗

bioRxiv2024

Foliar application of nano urea results in higher biomass, chlorophyll, and nitrogen content than equimolar bulk urea through differential gene regulation in Arabidopsis thaliana

Indian Farmers Fertilizer Cooperative (IFFCO)s liquid nano urea formulation (NUF) was applied to one-month-old Arabidopsis thaliana plants grown in vermiculite as a 0.4% foliar spray twice at an interval of 10 days and compared with sprays of equimolar bulk urea. NUF resulted in a 51 {+/-} 14.9% increase in biomass, 29.5 {+/-} 9.1% in chlorophyll, 8.4 {+/-} 3.1% in nitrogen, and 4.5 {+/-} 0.3% in amino acid content of the leaves, compared to bulk urea. NUFs zeta potential of -54.7 mV and particle size of [~=]27.7 nm, measured by dynamic light scattering and transmission electron microscopy, make it suitable for stomatal uptake. We conducted a differential gene expression analysis by mRNA sequencing to understand the molecular basis of the phenotypic gains under NUF rather than urea. NUF resulted in significantly higher expression levels of 211 genes (log2fold-change > 0.5, FDR < 0.05) involved in the biosynthesis of carbohydrates, amino acids, nucleotides, lipids, phytohormones, and secondary metabolites, cell wall biosynthesis and modification, growth and developmental processes, cell cycle, and stress response than bulk urea. On the other hand, 1,286 genes (log2fold-change < -0.5) involved in cell death, abscission, senescence, nitrogen transport and metabolism, and biotic stress response showed lower expression levels upon NUF application than bulk urea. Our results suggest that although NUF foliar spray suppresses nitrogen uptake genes, possibly due to nitrogen excess, it enhances growth by up-regulating the synthesis of essential biomolecules and growth-promoting genes, compared to bulk urea.

plant biology↗

bioRxiv2024

AP2/ERF transcription factors enriched in the drought-response transcriptome of the Thar desert tree Prosopis cineraria show higher copy number and greater DNA-binding affinity than orthologs in drought-sensitive species

We sequenced the drought-response transcriptome of the keystone tree species Prosopis cineraria from the Indian Thar desert to understand the key factors in its drought tolerance mechanism. We identified a network of genes activated in P. cineraria involved in osmotic stress response, phytohormone, calcium, and phosphorelay signal transduction. Of these, up-regulation of 54 APETALA2/Ethylene-Responsive Factor (AP2/ERF) transcription factor genes, validated by real-time PCR, suggests their key role in the drought tolerance of P. cineraria. We conducted a genome-wide study of the AP2/ERF superfamily in P. cineraria, classifying its 232 proteins into 15 clades and analyzing their protein structures, gene structure, and promoter organization. The P. cineraria genome contains more copies of AP2/ERF genes than drought-sensitive plants. Further, we identified sequence polymorphisms in AP2/ERF genes between Arabian and Indian cultivars of P. cineraria. We modeled the DNA-protein complex structures of AP2/ERFs from drought-tolerant and sensitive species using AlphaFold to compare their DNA binding ability. Though the DNA binding domain (DBD) is relatively conserved across species, the unstructured region of these proteins possesses different charge distributions, which might contribute differently to their DNA search and binding. Using all-atom molecular dynamics simulations, we teased out a higher number of specific DBD-DNA hydrogen bonds in P. cineraria, leading to a stronger DNA-binding affinity compared to drought-sensitive Arabidopsis thaliana. These results directly support copy number expansion of AP2/ERF transcription factors and the evolution of their structures for more efficient DNA search and binding as drought adaptation mechanisms in P. cineraria.

plant biology↗

bioRxiv2024

Brain rewiring during developmental transitions: A Comparative Analysis of Larva and Adult Drosophila melanogaster

The brains ability to adapt through structural rewiring during developmental transitions is a fundamental aspect of neuroscience. Our study conducts a detailed comparison of Drosophila melanogaster s brain networks during larval and adult stages, revealing significant changes in neuronal wiring during developmental phases. The degree distribution of the larval brain deviates significantly from power-law behavior and fits well with the Weibull distribution. In contrast, the adult brain exhibits power-law behavior in its degree distribution, with the exponent for the out-degree distribution lying in the scale-free regime and the exponent for the in-degree distribution being close to this regime. This difference reflects a change in the robustness of brain development from larval to adult phases. The core of these networks also changes during development in terms of their cell composition and topological influence. The larval core comprises Mushroom Body neurons, while the adult core mainly has Antennal Lobe neurons. Moreover, all the core neurons in the larval brain are also part of the rich-club neurons, a group of neurons with high in/out degrees that are well connected, whereas the same is not true for the adult brain network. Additionally, the core of the larval brain displays a more heterogeneous connectivity profile in its second-order neighbors compared to adult brain neurons, indicating greater diversity in larval brain connectivity. Our work stands as a step forward in understanding the rewiring of brain networks across the life stages of Drosophila melanogaster.

neuroscience↗

bioRxiv2024

Construction of an Immunoinformatics-Based Multi-Epitope Vaccine Candidate targeting Kyasanur Forest Disease Virus

Kyasanur Forest Disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus was initially considered endemic to the Western Ghats region of Karnataka. Still, over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD expedites the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatic tools. After analyzing 74 complete KFDV genome sequences for genetic recombination and phylogeny, different prioritized B and T cell epitopes were combined using various linkers to construct the vaccine candidate. Docking analysis of the designed vaccine construct revealed a stable interaction with the TLR2-TLR6 receptor complex. After confirming the stability of the vaccine receptor complex, codon optimization was done to ensure the efficient translation of the designed multi-epitope vaccine in the prokaryotic host system, and the subsequent in-silico cloning into the pET30b(+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine in the current study is satisfactory as it can significantly accelerate the initial stages of vaccine development by narrowing down potential vaccine candidates and providing insights into their design. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.

bioinformatics↗

bioRxiv2024

Azadirachta indica: derived, red-emitting, carbon nanoparticles for cellular bioimaging and potential therapeutics

Red-emitting carbon nanoparticles (CNPs) were synthesized by the refluxed green synthesis method using ethanolic extract of neem leaves (Azadirachta indica). These nanoparticles were called as nQDs (neem quantum dots). The nQDs exhibited excellent photoluminescence properties with a maximum emission at 672nm, and the average size of nQDs was around 47nm. In the in-vitro study, Retinal Pigment epithelial (RPE1) cells and SUM159A cells showed enhanced cellular uptake. In RPE1 cells, the cellular uptake was higher than in SUM159A cells. In the biocompatibility assay, SUM159A cell viability declined with the increasing nQDs concentration. The results show that red-emissive CNPs can be synthesized from Azadirachta indica (neem) leaves using a simple method with a possible application in bioimaging and therapeutics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/582094v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@30da5aorg.highwire.dtl.DTLVardef@14f6fbcorg.highwire.dtl.DTLVardef@30e0f0org.highwire.dtl.DTLVardef@ffc962_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

In-silico evaluation of Mirror repeats in some selected genes ofCandida albicans

All cellular processes in a living cell are controlled by its genetic material. DNA in majority of the domains acts as a regulatory molecule by controlling various vital functions. The genetic makeup of DNA or RNA (in viruses) is unique in all the domains. Their uniqueness is determined by the presence of various types of repetitive patterns of bases. These includes inverted repeats, tandem repeats, VNTRs, palindromes etc. Among many repetitive pattern types, Mirror repeats (MR) found to be dispersed throughout the genes or genomes. These sequences are associated with various functional features like their involvement in H-DNA formation, in replication & transcription, nervous system related diseases development etc. The major focus of this investigation is to identify MR sequences from some selected genes of Candida albicans using a bioinformatics based pipeline. The approach refers to as FPCB which utilized some manual steps to extract out MR sequence from any targeted gene or genome. The current study find out that the identified Mirror repeats found to be dispersed throughout the selected genes along with variable length. Among them the maximum & minimum MR sequences were reported in the gene FAS2 (108) & HIS1 (15) respectively. The present study will be helpful to provide a new insight for molecular as well as computational based studies of Candida albicans as well as other related fungal groups.

bioinformatics↗

bioRxiv2023

Machine learning analysis of lung adenocarcinoma and squamous cell carcinoma microbiome datasets reveals biomarkers for early diagnosis

IntroductionLung cancer (LC) is the second most frequent cancer worldwide with high incidences and mortality rates and non-small-cell-lung cancer (NSCLC) accounts for 80-85% cases of LC. It is further majorly sub-classified into adenocarcinoma (AC), and squamous-cell carcinoma (SCC). A late diagnosis at an advanced stage, a high rate of metastasis, and the development of therapy resistance are responsible for approximately 95% of mortality. Owing to high heterogeneity and variances in subtypes, it is important to precisely classify them for treatment. However, it poses a challenge in clinical practices, as it requires accurate quantification of each proportion subtype which is time-consuming and sometimes erroneous. The lower airways are home to a dynamic bacterial population sustained by the immigration, elimination, and migration of microbes from the gastrointestinal tract and upper airway tracts. The disruption in the homeostasis of microbiome compositions was found to be correlated with the increased risk of LC. Artificial intelligence (AI) techniques are used extensively in the early screening, and treatment of NSCLC have made significant strides in recent years. Recently, the use of CT/MRI scan image data in prediction models also results in false-positive rates and requires subsequent tests for further exploration which delays the prognostication. Therefore, early diagnosis, prevention, and treatment are critical to enhance survival and reduce death. Here, we aim for the classification of AC and SCC using the lung microbiome of lung tissue samples, implementing AI-based algorithms. MethodsWe have obtained raw sequencing data from the NCBI online database, and 149 AC samples and 145 SCC samples in patients were analyzed for their lung microbiome present in the lung tissue samples. The metadata such as patient age, sex, smoking history, and environmental material (malignant or not) were also analyzed. Using these data, machine learning algorithms were applied to select the best microbiome features for the classification of subtypes. ResultsA supervised ML and DL based model was developed that can discriminate NSCLC subtypes based on their microbial information, exploring the microbiome as predictive information for early screening. Consequently, 17 features were identified as a biomarker, and they showed good performance in distinguishing AC from SCC with an accuracy of 81% in KNN and 71% in DNN when demonstrated on the validation dataset. ConclusionThis study proposed a supervised machine learning framework where we can rely on taxonomic features and AI techniques to classify overlapped AC and SCC metagenomic data providing lung microbiome as a predictive and diagnostic biomarker in LC. Moreover, our framework will also be very helpful to other researchers to obtain further biomarkers and perform analysis in overlapped subtypes in different diseases.

bioinformatics↗

bioRxiv2023

Deep learning-based method to identify disease-resistance proteins in Oryza sativa and relative species

Rice (Oryza sativa) is a significant agricultural crop consumed by more than half of the global population. Its demand is expected to increase due to rising consumption and a growing global population. Moreover, the rice plant is frequently exposed to disease-causing pathogens, such as bacteria, fungi, viruses, and nematodes. Thus, cultivating disease-resistant varieties is an efficient way of disease control compared to pesticide applications. However, the rice plant has a well-defined defense system to prevent the onset of disease, including Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). The defense system is controlled by various disease-resistance proteins, such as resistance (R) proteins and pathogen recognition receptors (PRRs). Therefore, the identification of disease-resistance proteins not only reduces the amount of pesticides used in rice fields but also increases their yield. Though some resistant proteins have been characterized, their rapid identification, precise diagnosis, and appropriate management are still lacking. However, few methods based on sequence-similarity and de novo prediction, such as Machine Learning (ML), usually have low prediction power. In this study, we built a state-of-the-art classifier based on Deep Learning (DL) for the early detection of disease-resistance proteins in rice and related species. We compared the DL-based Multi-layer Perceptron (MLP) model with the five well-established ML-based methods using a protein dataset of rice and its related species. The DL-based MLP model outperformed all of the five classifiers on 10-fold cross-validation. The accuracy, Area Under Receiving Operating Characteristic (ROC) curve (AUC), F1-score, precision, and recall were superior in the DL-based MLP model. In conclusion, the MLP model is an effective DL model for predicting disease-resistance proteins with high scores in performance metrics. This study will provide insight to the breeders in developing disease-resistant rice varieties and assist in transforming traditional rice farming practices into a new age of smart rice farming.

bioinformatics↗

bioRxiv2023

A novel highly divergent enteric calicivirus in a bovine calf, India

In 2015, a novel highly divergent bovine calicivirus was detected in an Indian calf with enteritis. Phylogenetic analysis linked it to the Nebovirus with only 38.5% sequence identities, emphasizing the need for separate taxonomic classification. Furthermore, PCR screening detected these unique caliciviruses widely in Indias northern states.

microbiology↗

bioRxiv2023

Reciprocal inflammatory signals establish profibrotic cross-feeding metabolism

Idiopathic pulmonary fibrosis (IPF) is a disease of progressive lung remodeling and collagen deposition that leads to respiratory failure. Myeloid cells are abundant in IPF lung and in murine lung fibrosis, but their functional effects are incompletely understood. Using mouse and human lung models, we show that ornithine produced by myeloid cells expressing Arg1 serves as a substrate for proline and collagen synthesis by lung fibroblasts. The predominant Arg1-expressing myeloid cells in mouse lung were macrophages, but in IPF lung, high-dimensional imaging revealed ARG1 to be expressed mainly in neutrophils. Arg1 inhibition suppressed both ornithine levels and collagen expression in cultured, precision-cut IPF lung slices and in murine lung fibrosis. These results were confirmed in macrophage-specific Arg1 KO mice. Furthermore, we find that this pathway is regulated by cell-to-cell crosstalk, starting with purinergic signaling: Fibroblast eATP receptor P2rx4 was necessary for fibroblast IL-6 expression, which in turn was necessary for Arg1 expression by myeloid cells. Taken together, our findings define an immune-mesenchymal circuit that governs profibrotic metabolism in lung fibrosis.

immunology↗

bioRxiv2023

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

Spatial transcriptomics has advanced our understanding of tissue biology by enabling sequencing while preserving spatial coordinates. In sequencing-based spatial technologies, each measured spot typically consists of multiple cells. Deconvolution algorithms are required to decipher the cell-type distribution at each spot. Existing spot deconvolution algorithms for spatial transcriptomics often neglect spatial coordinates and lack scalability as datasets get larger. We introduce SpatialPrompt, a spatially aware and scalable method for spot deconvolution as well as domain identification for spatial transcriptomics. Our method integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) reference data to infer cell-type proportions of spatial spots accurately. At the core, SpatialPrompt uses non-negative ridge regression and an iterative approach inspired by graph neural network (GNN) to capture the local microenvironment information in the spatial data. Quantitative assessments on the human prefrontal cortex dataset demonstrated the superior performance of our tool for spot deconvolution and domain identification. Additionally, SpatialPrompt accurately decipher the spatial niches of the mouse cortex and the hippocampus regions that are generated from different protocols. Furthermore, consistent spot deconvolution prediction from multiple references on the mouse kidney spatial dataset showed the impressive robustness of the tool. In response to this, SpatialPromptDB database is developed to provide compatible scRNA-seq references with cell-type annotations for seamless integration. In terms of scalability, SpatialPrompt is the only method performing spot deconvolution and clustering in less than 2 minutes for large spatial datasets with 50,000 spots. SpatialPrompt tool along with the SpatialPromptDB database are publicly available as open source software for large-scale spatial transcriptomics analysis.

bioinformatics↗

bioRxiv2023

Unravelling Genomic Origin of Lumpy Skin Disease Virus in Recent Outbreaks

Lumpy skin disease virus (LSDV) belongs to the genus Capripox virus and family Poxviridae. LSDV was endemic in most of Africa, Middle east and Turkey, but since 2015, several outbreaks have been reported in Asian countries. In this study we used Whole Genome Sequence (WGS) approach to investigate the origin of the outbreak and understand the genomic landscape of the virus. Our study showed that the LSDV strain of 2022 outbreak exhibited many genetic variations, compared to the Reference Neethling strain sequence and the previous field strains from India. A total of 1819 variations were found in 22 genome sequences, which includes 399 extragenic mutations, 153 insertion frameshift mutations, 234 deletion frameshift mutations, 271 Single nucleotide polymorphisms (SNPs) and 762 silent SNPs. 38 genes have more than 2 variations per gene and these genes belong to viral-core protein, viral binding proteins, replication and RNA polymerase proteins. We highlight the importance of several SNPs in various genes which may play an essential role in pathogenesis of LSDV. Phylogenetic analysis performed on all whole genome sequences of LSDV showed two types of variants in India. One group of the variant with fewer mutations was found to lie closer to the LSDV 2019 strain from Ranchi while the other group clustered with previous Russian outbreaks from 2015. Our study highlights the importance of genomic characterization of viral outbreaks to not only monitor the frequency of mutations but also address its role in pathogenesis of LSDV as the outbreak continues.

microbiology↗

bioRxiv2023

In-Silico analysis of Human Insulin Gene for Mirror repeat sequences

The genetic material contains all the instructions necessary for the development, functioning & regulation of biological processes. This is also considering as a regulatory molecule. The structure constitute by its unique pattern of bases make it unique for every living being consider from bacteria to human. The varied pattern of bases in genomes or genes represents different types of repeats. These repeats types constitute the major fraction of the genomes. Among varied types Mirror repeats (MRs) play crucial roles in the genome especially in formation of H-DNA structure by Non Watson pairing. Some studies suggest that mirror sequences responsible for neurological diseases also. Now days in modern era of technology in silico techniques were develop to extract any type of repeat sequences from any gene or genome. The major focus of current work is on identification of mirror repeats (MRs) from human insulin gene sequence using an in silico approach. The approach is refer to as FPCB (FASTA Parallel Complement Analysis) were utilized to extract MR sequences. From the current study a total no of 210 MR sequenced were identified in Human insulin gene. The identified repeats vary in their length and majority of them are imperfect in nature. The maximum no of MR were reported in the region 3 & minimum is in the region 8 (8). In the remaining regions the no of MR sequences were lies in between maximum & minimum values. These sequences may be helpful in many molecular level studies.

bioinformatics↗

bioRxiv2023

Dopamine functionalized, red carbon quantum dots for in vivo bioimaging, cancer therapeutics, and neuronal differentiation

One of the crucial requirements of quantum dots for biological applications is their surface modifications for very specific and enhanced biological recognition and uptake. Toward this, we present the green synthesis of bright, red-emitting carbon quantum dots derived from mango leaf extract (mQDs). These mQDs are conjugated electrostatically with dopamine to form mQDs-dopamine (mQDs: DOPA) bioconjugates. Bright red fluorescence of mQDs was used for bioimaging and uptake in multiple cell lines, tissues, and in vivo models like zebrafish. mQDs exhibited the highest uptake in brain tissue as compared to others. mQD:DOPA conjugate induced cellular toxicity only in cancer cells while showing increased uptake in epithelial cells and zebrafish. Additionally, the mQDs: DOPA promoted neuronal differentiation of SH-SY5Y cells to complete neurons. Both mQDs and mQDs: DOPA exhibited potential for higher collective cell migrations implicating their future potential as next-generation tools for advanced biological and biomedical applications. TOCmQDs were electrostatically conjugated with dopamine (DOPA) to form the mQDs: DOPA bioconjugate. mQDs are used to image cells, tissues, and zebrafish embryos. mQDs: DOPA kills cancer cells, differentiates neuronal cells, and increases the uptake of mQDs in zebrafish embryos. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/545347v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@f521a4org.highwire.dtl.DTLVardef@173a8f0org.highwire.dtl.DTLVardef@585271org.highwire.dtl.DTLVardef@8c959d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗