Biology subjects

Das, G.

Publications and source records attributed to Das, G..

17 recordsLinked to original sources

bioRxiv2026

LsGCRPred: lncRNA-SNP regulated gene expression-based breast and ovarian cancer risk prediction model

Long non-coding RNAs (lncRNAs) and single nucleotide polymorphisms (SNPs) within them play crucial role in cancer susceptibility and disease outcomes. Breast and ovarian cancers, characterized by genetic heterogeneity, present significant challenges for precise diagnosis and treatment. Despite recent advancements in personalized medicine, inclusion of lncRNA-SNP (LSNP) markers into cancer risk detection panels remains limited. In this work, we put forward LSNP regulated gene expression based breast and ovarian cancer risk prediction model LsGCRPred (LSNP-Gene Interaction Based Cancer Risk Prediction Model). Notably, our approach accounts for the tissue-specificity of lncRNAs as well the benefit for individuals with predisposing conditions. Additionally, pathway analysis revealed the involvement of the LSNP interacting genes in key cancer regulating pathways. TaqMan genotyping and qPCR were performed to confirm the presence of selected LSNPs in ovarian and breast cancer cell lines along with the significant expression of the lncRNA and associated gene transcripts. These findings highlight previously overlooked genetic variants within lncRNA loci and their regulatory impact on disease outcomes, providing insights into personalized cancer diagnosis and treatment strategies. The tool LsGCRPred can be accessed as a standalone version on GitHub. Github Link: https://github.com/zglabDIB/LsGCRPred

cancer biology↗

bioRxiv2026

Sanjeevani: A manually curated anti-cancerous phytochemical database integrated with downstream analysis tools.

BackgroundCancer continues to pose a massive global health burden. While plant-derived phytochemicals offer promising therapeutic leads, existing natural product databases often lack cancer specificity, dataset downloadability, and integrated screening tools. MethodsWe developed Sanjeevani, an integrative web platform cataloguing 4,823 curated anticancer phytochemicals. Using a balanced dataset of 9,646 molecules, we trained Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbours classifiers using a hybrid feature representation of RDKit descriptors and 2048-bit ECFP4 fingerprints. The platform also integrates AutoDock Vina for web-based molecular docking for binding affinity, poses prediction and ADMET-AI for pharmacokinetics estimation. ResultsThe SVM model demonstrated the strongest predictive capability, achieving a top test accuracy of 0.966 and a ROC-AUC of 0.992. Benchmarking across five docking tools confirmed that AutoDock Vina successfully balanced computational automation with literature-consistent binding affinity replication. The final architecture provides rapid interactive 2D/3D visualizations integrated with downstream analysis tools. ConclusionSanjeevani provides an open-access, one-stop pipeline that bridges the gap between raw natural product data and actionable computational screening, accelerating natural product-based oncology drug discovery. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/732344v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@77183borg.highwire.dtl.DTLVardef@d7e465org.highwire.dtl.DTLVardef@1d3dfd7org.highwire.dtl.DTLVardef@10cc94d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

E-InfertilityTest: An Explainable AI Framework for Male Infertility Assessment

Male infertility has emerged as a significant concern in modern society, with genetic defects as one of the major underlying cause behind it. This impairment negatively impacts sperm motility and morphology, leading to conditions such as Asthenozoospermia (reduced sperm motility), Teratozoospermia (abnormal sperm morphology) and sometimes Asthenoteratozoospermia (both motility and morphology defects). Assisted reproductive technologies (ART), such as in-vitro fertilization (IVF), offer a potential solution for such cases but with a low success rate. Classical semen analysis provides only a phenotypic snapshot without revealing the fertilizing potential of the sperms. Hence, in order to screen the functional sperm population as well as to get a deeper insight into the reasons underlying the aberrant sperm population, it is important to study their genetic profile. In this work, we have performed a meta analysis of the transcriptomic data of infertile sperms from Asthenozoospermia and Teratozoospermia patients with that from fertile sperms of normal individuals. Thereafter we have screened a signature gene set which has been used to develop a prediction model named Explainable Infertility Test (E-InfertilityTest) to classify between fertile versus infertile sperm at the preliminary level. For each prediction, it will also provide the set of genes which are playing a dominant role towards such prediction. Thus, it will provide patient specific dominant gene expression profile responsible for the aberration. This work warrants validation experiments in future to substantiate the models performance in a clinical setting. User can access the tool named E-InfertilityTest as a standalone version on GitHub. Github Linkhttps://github.com/zglabDIB/einfertility.git

bioinformatics↗

bioRxiv2025

Traumatic Conditioning Induces a Combination of Anesthesia Resistant Memory and Protein Synthesis Dependent Long-Term Memory in Adult Drosophila

Stressful experiences elicit long-lasting memory trace that often persists for life-time. Though storage of experience dependent memory is key to survival, post-traumatic memories are detrimental to the physical and emotional wellbeing. The generation of any form of long-term memory culminates from synaptic plasticity and potentiation through persistent change in protein synthesis. Drosophila has been a robust model for the study of memory mechanisms; olfactory memory paradigms have been enormously successful in elucidating neuroanatomical, molecular, physiological and signalling pathways underlying the process. Long-term memory (LTM) has been induced by protein synthesis dependent mechanisms followed by a post-learning consolidation process whereas a medium-term anaesthesia resistant memory (ARM) is mediated by the Radish protein in flies. Here we present a novel and simple memory paradigm in adult flies for inducing sustained stress-induced memory paradigm, where a single attractive odorant is associated with an aversive stimulus-copper sulfate, which provides bitter taste as well as sustained malaise due to toxicity. We find that the elicited memory is a combination of ARM and LTM which is dependent on the number of training cycles. The eight-cycle training paradigm leads to robust, CREB and protein synthesis dependent memory persisting for about 7 days. The mushroom body (MB) Kenyon cells neurons as well as the dopaminergic input neurons (PPL1 subset) are involved in the formation of memory mediated by enhanced calcium as well as synaptic elaboration in the {gamma} subset of the MB neurons.

neuroscience↗

bioRxiv2025

A gut-brain axis for aversive interoception drives innate and anticipatory emesis in Drosophila.

Signals from the gut are increasingly recognized as modulators of brain function and behavior. However, the pathways through which the gut conveys adverse or unpleasant information to the brain are still not well understood. In this study, we identify an aversive gut-brain axis in Drosophila melanogaster that detects toxin-induced gut damage and triggers both innate and learned anticipatory emesis (vomiting). After toxin ingestion, reactive oxygen species are produced by midgut enterocytes and detected by the transient receptor potential channel TrpA1 on nearby enteroendocrine cells. This sensing stimulates the release of neuropeptides from enteroendocrine cells, likely representing the gastric malaise flies experience after eating. We show that these neuropeptides act on specific serotonergic and dopaminergic neurons in the brain. These neurons interact with each other and signal to the downstream memory-related mushroom bodies to promote emesis. This circuit not only drives an immediate emetic response but also represents a malaise-driven aversive signal. The signal manifests as the persistent activity of dopaminergic neurons, which reinforces aversive valence to odor cues in the mushroom bodies. Thus, the flies learn that a specific odor predicts the presence of a toxin in food and exhibit anticipatory emesis upon re-exposure to the same odor. Taken together, we have identified an interoceptive signaling pathway that may be conserved for detecting harmful gut conditions and for remembering how to avoid them. Our work offers a mechanistic framework for studying aversive gut-brain communication involved in feeding, metabolism, depression, brain injury, and neurodegenerative diseases.

neuroscience↗

bioRxiv2025

Site-Specific Immune and Stromal Architecture Drive Resistance to Trastuzumab Deruxtecan in HER2+ Metastatic Breast Cancer

Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate (ADC) that has demonstrated remarkable efficacy in HER2+ and HER2-low metastatic breast cancer (mBC). Yet, 40-50% of patients fail to respond, and mechanisms underlying resistance, particularly those involving the immune microenvironment, remain poorly understood. To address this gap, we conducted spatial proteomic profiling of metastatic lesions from 25 HER2+ mBC patients treated with T-DXd, using the Bruker GeoMX Digital Spatial Profiler (DSP) platform. Regions of interest (ROIs) were segmented by CD45 expression to distinguish immune-rich versus immune-poor compartments, and spatial marker expression was compared between responders (R) and non-responders (NR). Spearman correlation analysis was used to assess spatial coordination among HER2, fibronectin, and immune markers. Non-responders exhibited greater HER2 heterogeneity, lower CD45+ immune infiltration, and consistent upregulation of fibronectin and granzyme B. In both bone and brain metastases, NR tissues showed strong correlations between fibronectin and T cell markers, indicating spatial immune exclusion. In contrast, NR lymph node metastases displayed negative correlations between fibronectin and immune markers and uncoordinated B-T cell clustering, suggesting an alternative resistance mechanism rooted in immune disorganization rather than exclusion. Collectively, these results implicate HER2 heterogeneity, fibrotic immune exclusion, and immune suppression as key drivers of T-DXd resistance across metastatic sites. By integrating spatial proteomics with clinical outcomes, this study provides novel insights into the spatial immune landscape of ADC resistance and identifies actionable biomarkers for patient stratification and combination therapy design, laying a critical foundation for developing biomarker-guided therapies and personalized treatment strategies to overcome ADC resistance in metastatic breast cancer. Translational SignificanceT-DXd represents a major advance in antibody-drug conjugate (ADC) therapies to address HER2 heterogeneity in tumors through bystander killing. However, treatment options following resistance to T-DXd remain limited, leaving a critical unmet need. As new ADCs continue to emerge, resistance will likely remain a persistent challenge across tumor types. This work extends beyond T-DXd resistance--it provides a framework to address resistance mechanisms common to other ADCs. By investigating immune-mediated mechanisms of ADC resistance, this research identifies therapeutic targets that can be leveraged to develop new treatment strategies. By integrating spatial biology with clinical data, this study offers unprecedented insights into immune-tumor interactions within the resistant tumor microenvironment. This approach deepens mechanistic understanding and enables personalized treatment strategies guided by spatially resolved biomarkers. This novel combination of spatial mechanistic research with clinical data represents advancement toward overcoming ADC resistance and improving outcomes for patients with few therapeutic options.

cancer biology↗

bioRxiv2025

LncPTPred: Predicting lncRNA-Protein Interaction based on Crosslinking and Immunoprecipitation (CLIP-Seq) Data

Long non-coding RNA (lncRNA)-Protein Interaction (LPI) across diverse biological systems directly and indirectly regulates various cellular processes. Experimental assays to recognize the protein binding partners of lncRNAs is highly time consuming and expensive. In-silico predictive approaches involving pattern recognition technique provides a promising alternative to it by reducing the search space. Our work identifies such hidden pattern from within the Cross-linking immunoprecipitation sequencing (CLIP-Seq) data which aids to overcome the problem of obtaining real negative dataset and thus offers a state-of-the-art machine learning (ML) based prediction algorithm to predict LPI. Initial phase of this work involves preparation of the training dataset and the next phase is devoted towards developing the ML based model to perform prediction operation. In order to show the efficacy of our model, its performance has been compared against that of the contemporary prediction tools, where the result clearly shows the outperformance of our model. Moreover it also provides the segments of interaction within the lncRNA loci which acts as a roadmap for precise designing of the validation experiment. The LncPTPred tool has been provided in terms of web server as well as standalone version in github. Web server Link: http://bicresources.jcbose.ac.in/zhumur/lncptpred/ Github Link: https://github.com/zglabDIB/lncptpred.git

bioinformatics↗

bioRxiv2024

Targeting prostate cancer by new bispecific monocyte engager directed to prostate-specific membrane antigen

Prostate cancer (PCa) ranks as the second leading cause of cancer-related deaths among men in the United States. Prostate-specific membrane antigen (PSMA) represents a well-established biomarker of PCa and its levels correlate positively with the disease progression, culminating at the stage of metastatic castration-resistant prostate cancer. Due to its tissue-specific expression and cell surface localization, PSMA shows superior potential for precise imaging and therapy of PCa. Antibody-based immunotherapy targeting PSMA offers the promise of selectively engaging the host immune system with minimal off-target effects. Here we report on the design, expression, purification, and characterization of a bispecific engager, termed 5D3-CP33, that efficiently recruits macrophages to the vicinity of PSMA-positive cancer cells mediating PCa death. The engager was engineered by fusing the anti-PSMA 5D3 antibody fragment to a cyclic peptide 33 (CP33) selectively binding the Fc gamma receptor I (Fc{gamma}RI/CD64) on the surface of phagocytes. Functional parts of 5D3-CP33 engager revealed nanomolar affinity for PSMA and Fc{gamma}RI/CD64 with dissociation constants of KD = 3 nM and KD = 140 nM, respectively. At a concentration as low as 0.3 nM, the engager was found to trigger production of reactive oxygen species by U937 monocytic cells in the presence of PSMA-positive cells. Moreover, flow cytometry analysis demonstrated antibody-dependent cell-mediated phagocytosis of PSMA-positive cancer cells by U937 monocytes when exposed to 0.1 nM 5D3-CP33. Our findings illustrate that 5D3-CP33 effectively and specifically activates monocytes upon PSMA-positive target engagement, resulting in the elimination of tumor cells. The 5D3-CP33 engager can thus serve as a promising lead for the development of new immunotherapy tools for the efficient treatment of PCa.

bioengineering↗

bioRxiv2024

Crude extract of Ruellia tuberosa L. flower induces intracellular ROS, promotes DNA damage and apoptosis in Triple Negative Breast Cancer Cells

Ethnophamacological relevanceIn the traditional folklore medicine system, the primary uses of Ruellia tuberosa L. include as a diuretic, anti-hypertensive, antipyretic, anti-diabetic, analgesic, and gastroprotective agent. Some reports also demonstrated that it has been used to treat gonorrhea-like diseases. PurposeExploring the anti-cancer potential of the methanolic extract of Ruellia tuberosa L. flower (RTME) with special emphasis on human triple-negative breast cancer (TNBC) and investigating the possible signaling networks and regulatory pathways underlying it. MethodsPreparation of RTME and identifying the possible phytochemicals through GC-MS analysis. The anti-cancer potential of RTME was executed through in-vitro cytotoxicity assay, clonogenic assay, wound healing assay, ROS generation assay, cell cycle arrest, apoptotic nuclear morphology study, cellular apoptosis study, mitochondrial membrane potential (MMP) alteration study, protein and gene expressions alteration study. Apart from this, toxicological status and in-silico molecular docking studies were also conducted. ResultsFrom this study, it was obvious that several phytochemicals within RTME have the potential to act as anti-cancerous agents. RTME was found to exhibit significant in-vitro cytotoxicity along with a reduction in colony formation and inhibition of cell migratory potential in MDA-MB-231 cells. RTME also induced intracellular ROS, promoted G0/G1 cell cycle arrest, caused mitochondrial membrane potential (MMP) alteration, and promoted cell death. From the pro- and anti-apoptotic marker study through the western blot and the qRT-PCR analysis, it was revealed that RTME promoted the intrinsic pathway of apoptosis. Furthermore, blood parameters and histological analysis revealed that RTME doesnt exhibit any toxic effect on female Balb/C mice. Finally, an in-silico molecular docking study revealed that the three identified lead phytochemicals in RTME show strong receptor-ligand interactions with the anti-apoptotic Bcl-2 and give a clue to the possible molecular mechanism of the RTME extract. ConclusionFrom the findings, it was concluded that RTME has a significant therapeutic potential against TNBC which could be an alternative option for anti-cancer drug development. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/586749v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@72780corg.highwire.dtl.DTLVardef@1276029org.highwire.dtl.DTLVardef@1a2d91org.highwire.dtl.DTLVardef@a5cb44_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPreparation of methanolic extract of Ruellia tuberosa L. flower, C_LIO_LIIdentification of phytochemicals from the methanolic extract of Ruellia tuberosa L. flower, C_LIO_LIMethanolic extract of Ruellia tuberosa L. (RTME) flower exhibited significant anti-cancer potential in triple-negative breast cancer (TNBC) cells, MDA-MB-231 through induction of intracellular ROS, G0/G1 cell cycle arrest, and apoptosis, C_LIO_LIToxicological assessments of RTME on female Balb/C mice, C_LIO_LIIn-silico assessments of lead phytochemicals with the target anti-apoptotic protein, bcl-2 C_LI

cancer biology↗

bioRxiv2023

Direct Intake Estimation and Longitudinal Tracking of Solid-food Consumption (DIETS) in Drosophila

The fruit fly Drosophila melanogaster offers a powerful model to study how diet affects the body and brain. However, existing methods for measuring their food intake often rely on dyes or tags mixed with food, which can be inaccurate due to how the flies absorb and eliminate them. Capillary-based assays like CAFE directly measure consumption, but only work with liquids and shorten fly lifespan. Additionally, capillary assays are incompatible with delivering viscous foods like high-fat diets. Even solidified high-fat diets tend to be sticky death traps for flies. Another longstanding challenge for fly researchers is that dietary restriction in flies involves diluting food, leading to compensatory feeding. To address these shortcomings, we have developed DIETS, a sensitive feeding assay that can be implemented even in low-resource settings. DIETS eliminates the need for labels and directly weighs the solid food consumed by small groups of flies over extended periods of hours to weeks. It allows us to deliver precise amounts of food to flies and implement accurate dietary restrictions. Importantly, DIETS is compatible with studying energy-dense high-fat diets. Using DIETS, we observed that, unlike a high-sugar diet, an isocaloric high-fat diet did not improve the flies ability to withstand starvation, even though they consumed more calories and had higher fat deposition.

animal behavior and cognition↗

bioRxiv2022

A machine learning based method for the identification of functionally important genes

Deciphering complex mechanisms underlying communicable and non-communicable diseases require comprehensive analysis of genetic factors and interactions between them. Experimental identification of genes related to pathogens virulence and human diseases is effective but laborious, time consuming and costly. Consequently, many genetic factors associated with pathogenesis or disease still remain to be unknown. In present work, a normalized point wise mutual information (nPMI) driven computational method has been developed to identify the association between biological entities (genes) and physiological responses utilizing published literatures in PubMed. Association prediction models are then developed using machine learning approach for four different datasets including virulent genes from two bacterial species (E. coli and S. enterica) and stress responsive genes from two plant species (A. thaliana and O. sativa). This approach provides a generic platform for identifying association of genes in diverse biological systems (host and pathogen) and provide up-to-date estimate of association measures of the genes with stress, virulence. In future, the causal relation between them may be of research importance.

bioinformatics↗

bioRxiv2022

Exploring repeats in rice genomes: Identification, Characterization and its Applications

Biodiversity is a fundamental property of all natural systems existing in the field biology. It refers to the underlying heterogeneity at different levels of ecology, genetics and evolution. In case of plant systems, dramatic variability has been observed during the Anthropocene at different spatial scales. Environmental stress is one of the major influencing factors behind this plant biodiversity. Huge genetic diversity has been also demonstrated across varieties of important crop species like rice. Repetitive sequences which are a major contributor of genomic diversity in polyploidy plants have been found to occur ubiquitously in their genomes. To date diverse repeat types have been characterized in the plant genomes performing various functions starting from qualitative trait markers to genome evolution and stress management. With an objective to identify of plant stress associated genes using DNA repeat probes, a robust method has been developed. The method has been modularized into three distinct sections. First part is dedicated for identification of different types of repeats. Earlier review has suggested building a pipeline of multiples tools for capturing different types of repeats from the genome sequences. Specialized tools like TROLL, Tandem repeat finder (TRF), PHOBOS and database like REPBASE have been selected for performing this job. Second module is intended for screening of stress related genes from the published articles and databases and the last module has been designed for the association mining between genomic repetitive patterns with stress phenotypes. The method has been used to explore stress associated repeats from 9 Oryza species from different continents and other plants like Arabidopsis and Brachypodium. In case of Oryza species distribution repeats has been found to be significantly different between stress associated and housekeeping genes. More than 55% of the repeats are found to be in positive association (nPMI > 0) whereas 26% of the repeats are false-positives. These repetitive probes have been utilized in several applications. Firstly, using as molecular markers to identify stress related genes in different Oryza species where availability is limited. Secondly, using as a probe to reanalyzing the evolutionary lineage of Oryza species etc.

bioinformatics↗

bioRxiv2021

LitRev: A data driven method for quick literature review from PubMed

SummaryLitRev is a novel robust data driven approach, developed for quick literature review on a particular topic of interest. This method identifies common biological phrases that follow a power law distribution and important phrases which have the normalized point wise mutual information score greater than zero. Contactgourabdas0727@gmail.com

bioinformatics↗

bioRxiv2021

Potential Key Genes Associated with Stroke types and its subtypes: A Computational Approach

To investigate prospective key genes and pathways associated with the pathogenesis and prognosis of stroke types along with subtypes. Human genes using genome assembly build 38 patch release 13 with known gene symbols through NCBI gene database (https://www.ncbi.nlm.nih.gov/gene) were fetched. PubMed advanced queries were constructed using stroke-related keywords and associations were calculated using Normalized pointwise mutual information (nPMI) between each gene symbol and queries. Genes related with stroke risk within their types and subtypes were investigated in order to discover genetic markers to predict individuals who are at the risk of developing stroke with their subtypes. A total of 2,785 (9.4%) genes were found to be linked to the risk of stroke. Based on stroke types, 1,287 (46.2%) and 376 (13.5%) genes were found to be related with IS and HS respectively. Further stratification of IS based on TOAST classification, 86 (6.6%) genes were confined to Large artery atherosclerosis; 131 (10.1%) and 130 (10%) genes were related with the risk of small vessel disease and Cardioembolism subtypes of IS. Besides, a prognostic panel of 9 genes signature consisting of CYP4A11, ALOX5P, NOTCH, NINJ2, FGB, MTHFR, PDE4D, HDAC9, and ZHFX3 can be treated as a diagnostic marker to predict individuals who are at the risk of developing stroke with their subtypes.

bioinformatics↗

bioRxiv2021

Benchmarking tools for DNA repeat identification in diverse genomes

Continuous progression in genomics shows that repeats are important elements of genomes that perform many regulatory and other functions. Eventually, to date, many computational tools have been developed and frequently used for the identification and analysis of genomic repeats. A single tool cannot detect all different types of repeats in diverse species rather pipeline of tools is more effective. But, the choice of such rigorous and robust tools is highly challenging. A method has been implemented to select a set of optimal tools for finding all available classes of perfect and imperfect tandem repeats including microsatellites, minisatellites, and interspersed CRISPRs in genomes. A total of 11 tools have been shortlisted using rule-based selection and then ranked by analyzing rigorousness in searching in diverse species and execution time. Tool comparison shows consistency in perfect microsatellite detection performance but significantly differ for long and imperfect repeats. A web-server has been built which provides a generic platform for various classes of repeat identification from the diverse genome using multiple tools and comparison.

bioinformatics↗

bioRxiv2021

Targeting the artemisinin resistant malaria by repositioning of the anti-Hepatitis C Virus drug Alisporivir

The rapid emergence of P. falciparum-resistant strains raises an urgent need to find new antimalarial drug candidates. This study reports the rational repositioning of the anti-Hepatitis C Virus drug, Alisporivir, a non-immunosuppressive analog of cyclosporin A (CsA) against multiple, drug-resistant strains of P. falciparum. Alisporivir being non-hemolytic has been proven to be a better drug than CsA. Indeed, our study also demonstrated the same. Alisporivir inhibited chloroquine-sensitive parasite growth with an IC50 of 196.6nM. Alisporivir also inhibited the growth of chloroquine-resistant parasites with an IC50 of 422.1nM. Alisporivir exhibited, anti-malarial activity in in vivo. Further, we exploited the Cyclophilins targeting potential of Alisporivir against artemisinin-resistant malaria parasite owing to the fact that PfCyP-19B is one of the genes that is overexpressed in artemisinin-resistant parasite revealed by a population transcriptomic study. Our semiquantitative real-time transcript and immunofluorescence analysis confirmed the overexpression of PfCyP-19B in Artemisinin-resistant P. falciparum (PfKelch13R539T). Artemisinin resistance is attributed to slow clearance of ring stage parasites. Ring survival assay (RSA) is designed to access the potency of compounds on these dormant slow clearing parasites leading to drug resistance. Thus, the potency of Alisporivir against PfKelch13R539T was evaluated by RSA. A 2.5-fold decrease in parasite survival was detected with Alisporivir. Further, combination of Alisporivir with DHA found to potentiate the efficacy of DHA by 4.55-fold. These results support the hypothesis that targeting of resistance mechanism is a potential approach to deal with resistant parasite. Overall, this study demonstrates the rational reposition of Alisporivir against resistant malaria resistance.

microbiology↗

bioRxiv2020

Enhanced odour-associated memory performance with a Y-maze assembly in Drosophila

The neural basis of behaviour is identified by systematically disrupting the activity of specific neurons and screening for loss in phenotype. Robust, high-scoring behavioural assays are thus necessary for identifying the neural circuits of novel behaviours. Here, we report the design and use of a Y-maze based classical olfactory learning and memory assay in Drosophila. Appetitive memory scores in our Y-mazes are considerably better and longer-lasting than that from a commonly used T-maze design. We found that the mechanism that traps flies in their choice of an odour is mainly responsible for the improving scores in the Y-mazes. Using Y-mazes, we could assay significant 24 h gustatory aversive memories in flies. These aversive memories are susceptible to protein synthesis inhibitor cycloheximide (CXM) and therefore embodies long-term memory (LTM). When anaesthesia resistant memory (ARM) deficient radish mutant flies are trained with dry sucrose, 24 h memory is severely disrupted. However, when we trained with 2 M sucrose-agar and tested in Y-mazes, radish mutants exhibited a residual 24 appetitive memory. This memory is not ARM, and we show that it is not CXM sensitive LTM either. It could be a third form of appetitive consolidated memory in flies. The Y-maze assembly described here is particularly sensitive and will thus enable the study of new memory phenotypes in Drosophila.

neuroscience↗