Biology subjects

Devarajan, K.

Publications and source records attributed to Devarajan, K..

15 recordsLinked to original sources

bioRxiv2026

A large-scale crowd-sourced annotated acoustic dataset of Indian fauna

Global rates of biodiversity loss warrant conservation action and monitoring at large geographic scales. Conservation technologies such as acoustic monitoring in conjunction with deep learning now enable us to monitor wildlife simultaneously across space and time. However, for a significant proportion of biodiversity in tropical regions, we cannot yet rely on automated recognition approaches because we lack acoustic templates to robustly train deep learning algorithms. In this paper, we relied on a novel participatory approach, enlisting researchers, conservation practitioners, and nature enthusiasts to create a unique crowd-sourced, open-access dataset of acoustic annotations across taxonomic groups for biodiversity in India. Our dataset comprises 3311 minutes of strongly labelled data (bounding boxes or annotations for a species vocalization) and 2504 minutes of weakly labelled data (indicating the presence of a species within an audio file but lacking bounding boxes) for 518 species across India, spanning 25 of 36 states and union territories. We present metadata and code for data processing and highlight the strengths of a participatory approach to biodiversity monitoring.

ecology↗

bioRxiv2024

Design and Validation of a Wearable Imaging System for Automated Wound Monitoring in Porcine Model

Effective wound monitoring has the potential to guide treatment regiments and improve healing outcomes, yet current clinical assessment methods remain largely subjective and labor-intensive. To address this challenge, we present a high-resolution wearable imaging system designed for continuous wound monitoring. The system integrates a 64 MP camera with a plano-convex lens housed in an enclosure measuring 73 mm in diameter and 36.1 mm in height, and features a custom printed circuit board (PCB) for programmable LED illumination. The 3D-printed device enclosure is designed to accommodate a silicone bioelectronic device and can be securely attached using a commercially available ostomy skin barrier. In porcine model validation studies, the system successfully captured daily wound progression over periods up to 7 days. The captured images were wirelessly transmitted to a processing unit where DeepMapper, a machine learning algorithm, processed z-stacked images and performed multi-level feature extraction to predict wound healing stages and indicate potential complications such as infection. This imaging system enables automated analysis of wound progression and supports the development of smart wound care platforms for personalized treatment strategies. The integrated design approach demonstrates the feasibility of creating compact, high-resolution imaging systems suitable for clinical wound monitoring applications.

bioengineering↗

bioRxiv2024

Remote-Controlled Wireless Bioelectronics for Fluoxetine Therapy to Promote Wound Healing in a Porcine Model

Wound healing presents a significant challenge in biomedical science, requiring precise therapeutic delivery and real-time monitoring. Bioelectronic systems offer a promising solution but remain largely unexplored for wound care, particularly in large animal models that reflect human healing dynamics. This study introduces a remote controlled wireless bioelectronic platform equipped with an iontophoretic pump to deliver fluoxetine, a selective serotonin reuptake inhibitor that promotes wound repair. In vitro and ex-vivo testing validated efficient on demand fluoxetine delivery. In vivo experiments in a porcine wound model demonstrated clear therapeutic efficacy over 3-day and 7-day periods. The system enhanced healing outcomes, increasing re-epithelialization by 37% (H&E staining), reducing the M1/M2 macrophage ratio by 33%, and stimulating neuronal growth at the wound site. This bioelectronic platform delivers fluoxetine in a controlled, remotely-controlled manner while allowing for wound direct wound imaging that can be used to monitor wound healing progress. Additionally, it allows precise dose and temporal delivery of treatment to enhance the outcome of future large animal wound healing studies.

bioengineering↗

bioRxiv2024

Wireless bioelectronic device for wound healing

Wireless bioelectronic actuators have been developed to deliver targeted treatments over multiple days while continuously monitoring delivery, thereby improving wound healing. Specifically, these devices can deliver charged biomolecules such as fluoxetine cations (Flx+) and electric field (EF) in freely moving pigs. Treatments can be controlled and monitored in real time via WiFi, with options for both user-specified delivery rates and durations, as well as automated closed-loop (CL) control. The devices are engineered to handle various failure scenarios that may arise in dynamic, real-world experiments--such as communication or power interruptions--ensuring that valuable experimental data is collected with minimal disruption. The ion pump features eight drug reservoirs and channels arranged around a 20 mm diameter-wound, with a central ground electrode (0 V). When voltages above 0 V are applied to the outer channels, currents flow from the reservoirs and channels into the wound, delivering Flx+ and/or EF depending on the reservoir solution. The device records applied voltages and currents locally to a microSD card at a high sampling rate, while simultaneously transmitting real-time measurements via a local WiFi network to a wound healing algorithm running on a nearby laptop. CL control of current/delivery rate is performed by an onboard microcontroller unit (MCU) and current-source microchips, based on instructions received from the wound healing algorithm. A graphical user interface (GUI) provides intuitive user control and real-time data visualization, with support for multiple devices. In vivo studies over seven days showed that Flx+-treated wounds had a 20% lower M1/M2 macrophage ratio and 41.67% greater re-epithelialization compared to controls (standard-of-care), demonstrating the actuators potential to enhance wound healing.

biochemistry↗

bioRxiv2023

A model-based hypothesis framework to define and estimate the diel niche via the 'Diel.Niche' R package

O_LIHow animals use the diel period (24-hour light-dark cycle) is of fundamental importance to understand their niche. While ecological and evolutionary literature abound with discussion of diel phenotypes (e.g., diurnal, nocturnal, crepuscular, cathemeral), they lack clear and explicit quantitative definitions. As such, inference can be confounded when evaluating hypotheses of animal diel niche switching or plasticity across studies because researchers may be operating under different definitions of diel phenotypes. C_LIO_LIWe propose quantitative definitions of diel phenotypes using four alternative hypotheses sets (Maximizing, Traditional, General, and Selection) aimed at achieving different objectives. Each hypothesis set is composed of mutually exclusive hypotheses defined based on the activity probabilities in the three fundamental periods of light availability (twilight, daytime, and nighttime). C_LIO_LIWe develop a Bayesian modeling framework that compares diel phenotype hypotheses using Bayes factors and estimates model parameters using a multinomial model with linear inequality constraints. Model comparison, parameter estimation, and visualizing results can be done in the Diel.Niche R package. A simplified R Shiny web application is also available. C_LIO_LIWe provide extensive simulation results to guide researchers on the power to discriminate among hypotheses for a range of sample sizes (10 to 1280). We also work through several examples of using data to make inferences on diel activity, and include online vignettes on how to use the Diel.Niche package. We demonstrate how our modeling framework complements analyses that are commonly used to investigate diel activity, such as circular kernel density estimators. C_LIO_LIOur aim is to encourage standardization of the language of diel activity and bridge conceptual frameworks and hypotheses in diel research with data and models. Lastly, we hope more research focuses on the ecological and conservation importance of understanding how animals use diel time. C_LI

animal behavior and cognition↗

bioRxiv2021

Identification and validation of putative target genes regulated by miR-34 in cervical cancer

PurposeWe aimed to identify the epithelial-mesenchymal transition and associated putative targets of the miR-34 family in transcriptomic data of cervical epithelial squamous cell carcinoma (CESC) to find new therapeutic targets for better disease management. MethodsA combined computational analysis of the miR-34 family; gene expression in heterogeneous primary CESCs derived from TCGA; and the integration of miR-34b and EMT-regulated genes was performed. Four EMT-associated miR-34b gene targets were analysed in primary human CESC and non-cancerous cervical tissues by qRT-PCR. Effects of endogenous miR-34b expression and its associated gene modulations in cervical cancer cells (C33A and HeLa) were analysed using qRT-PCR, western blotting and immunofluorescence, transwell migration and invasion assays. ResultsThe results showed that the miR-34 family might regulate the mTOR pathway, cell cycle (CCND2) and cell adhesion functions (FZD4). Further, we showed that a low negative correlation (r2= -0.07) between miR-34b/EM[I] score and four EMT signature genes (E>MP7, CAV1, ID2, FN1) were significantly regulated by miR-34b. Also, FN1 was indicated as its putative target with the highest negative EMT score and high binding energy between MRE/3UTR. Further, these transcriptomic signatures in CESC revealed a significant inverse correlation across the stages of primary human CESC. These genes were repressed at transcriptional and translational levels in miR-34b-3p expressing C33A and HeLa cells, contributing to their reduced cell migration and invasive properties. ConclusionsOur studies revealed the potential targets of the miR-34 family, especially miR-34b, that can emerge as potential biomarkers and promising therapeutic targets in CESC disease management.

cancer biology↗

bioRxiv2021

Combined Inhibition of AKT and KIT Restores Expression of Programmed Cell Death 4 (PDCD4) in Gastrointestinal Stromal Tumor

The majority of gastrointestinal stromal tumor (GIST) patients develop resistance to the first-line KIT inhibitor, imatinib mesylate (IM), through acquisition of secondary mutations in KIT or bypass signaling pathway activation. AKT is a relevant target for inhibition, in addition to KIT, since the PI3K/AKT pathway is crucial for IM-resistant GIST survival. We evaluated the activity of a novel pan-AKT inhibitor, MK-4440 (formerly ARQ 751), as monotherapy and in combination with IM in GIST cell lines and preclinical models with varying IM sensitivities. Dual inhibition of KIT and AKT demonstrated significant synergistic effects in IM-sensitive and -resistant GIST cell lines. Proteomic analyses revealed upregulation of the tumor suppressor, PDCD4, in combination treated cells. Enhanced PDCD4 expression correlated to cell cycle arrest and cell death. In vivo studies revealed superior efficacy of MK-4440/IM combination in an IM-sensitive preclinical model of GIST compared with either single agent. The combination demonstrated limited efficacy in two IM-resistant models, including a GIST patient-derived xenograft model possessing an exon 9 KIT mutation. These studies provide strong rationale for further use of AKT inhibition in combination with IM in primary GIST; however, alternative agents will need to be tested in combination with AKT inhibition in the resistant setting.

cancer biology↗

bioRxiv2020

The Company Canids Confront: Spatiotemporal Partitioning at Local Scales Facilitates Carnivore Coexistence at the Landscape Level

Canids are the most widely distributed carnivores in the world. The increasing impacts of commensal carnivores such as free-ranging dogs on wildlife communities has resulted in an urgent need to understand putative interactions within carnivore guilds. It is thus imperative to understand the processes driving canid assemblages in different landscapes and at multiple spatial and temporal scales, in order to conserve and manage wildlife communities. Here, we demonstrate the complex interactions and spatiotemporal dynamics underlying the coexistence of co-occurring carnivores in a landscape modified by the invasive, Prosopis juliflora. We investigated spatial, temporal, and habitat partitioning within a guild of four co-occurring canids in the arid northwest of India. The results indicate complex associations between the study species, where co-occurrence at the local spatial scale between species corresponds with temporal partitioning. Our study offers evidence that avoidance at the local scale and coexistence at the landscape scale are maintained in co-occurring intra-guild carnivores that are of similar body size through both facultative and behavioral character displacement such as temporal partitioning. It has also resulted in essential baseline information on the occurrence and distribution patterns of multiple canids in a human-dominated and understudied landscape threatened by global change. Understanding these biotic and abiotic drivers that impact carnivore guilds is crucial for the conservation and management of communities at the landscape scale.

ecology↗

bioRxiv2020

Probability-based methods for outlier detection in replicated high-throughput biological data

A problem that arises frequently in high-throughput biological studies is the assessment of technical reproducibility of data obtained under homogeneous experimental conditions. This is an important problem considering the significant growth in the number of high-throughput technologies that have become available to the researcher in the past decade. Although certain ad hoc and graphical methods for determining data quality have been in existence, these methods lack statistical rigor and are not broadly applicable across different technologies. There is an inherent need for the quantitative evaluation of the reproducibility of technical replicates from high-throughput compound and siRNA screening, next-generation sequencing and other modern "omics" studies. To this end, we have developed an approach that accounts for technical variability and potential asymmetry that arise naturally in the distribution of replicate data, and aids in the identification of outliers. Our methods for outlier detection rely on flexible statistical models, employ maximum likelihood methods for estimation and are broadly applicable to a variety of high-throughput biological studies. We discuss an adaptation of these methods when there are multiple replicates as well as current limitations to these techniques. We illustrate our methods using experimental data from high-throughput compound screening and protein expression studies as well as simulated data. Our methods are implemented in the R package replicateOutliers and are currently available at github.com/matthew-seth-smith/replicateOutliers.

bioinformatics↗

bioRxiv2020

Kinome Profiling of Gastrointestinal Stromal Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies Wee1 as Candidate Therapeutic Target

Management of gastrointestinal stromal tumor (GIST) has been revolutionized by the identification of activating mutations in KIT and PDGFRA, and the clinical application of receptor tyrosine kinase (RTK) inhibitors in the advanced disease setting. Stratification of GIST into molecularly defined subsets provides insight into clinical behavior and response to approved targeted therapies. Although these RTK inhibitors are effective in the majority of GIST, resistance to these agents remains a significant clinical problem. Development of effective treatment strategies for refractory GIST subtypes requires identification of novel targets to provide additional therapeutic options. Global kinome profiling has the potential to identify critical signaling networks and reveal protein kinases that are essential in GIST. Using Multiplexed Inhibitor Beads and Mass Spectrometry, we explored the majority of the kinome in GIST specimens from the three most common molecular subtypes to identify novel kinase targets. Kinome profiling revealed distinct signatures in GIST subtypes and identified kinases that are universally activated in all GIST, as well as kinases that are unique to each subtype. Kinome profiling in combination with loss-of-function assays identified a significant role for the G2-M tyrosine kinase, Wee1, in GIST cell survival. In vitro and in vivo studies revealed significant efficacy of MK-1775 (Wee1 inhibitor) in combination with avapritinib in KIT and PDGFRA-mutant GIST cell lines, and notable efficacy of MK-1775 as a single agent in the PDGFRA-mutant line. These studies provide strong preclinical justification for the use of MK-1775 in GIST.

cancer biology↗

bioRxiv2020

Unified methods for feature selection in large-scale genomics data with censored survival outcomes

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the diseases process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each feature. When applied to genomic features exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. We propose a broad array of marginal screening techniques that aid in feature ranking and selection by accommodating various forms of NPH. First, we develop an approach based on Kullback-Leibler information divergence and the Yang-Prentice model that includes methods for the PH and proportional odds (PO) models as special cases. Next, we propose R2 indices for the PH and PO models that can be interpreted in terms of explained randomness. Lastly, we propose a generalized pseudo-R2 measure that includes PH, PO, crossing hazards and crossing odds models as special cases and can be interpreted as the percentage of separability between subjects experiencing the event and not experiencing the event according to feature expression. We evaluate the performance of our measures using extensive simulation studies and publicly available data sets in cancer genomics. We demonstrate that the proposed methods successfully address the issue of NPH in genomic feature selection and outperform existing methods. The proposed information divergence, R2 and pseudo-R2 measures were implemented in R (www.R-project.org) and code is available upon request.

bioinformatics↗

bioRxiv2019

TET1 and TDG suppress intestinal tumorigenesis by down regulating the inflammatory and immune response pathways

IntroductionAberrant DNA methylation is frequently observed in colorectal cancer (CRC), but the underlying mechanisms are poorly understood. Ten-Eleven Translocation (TET) dioxygenases and DNA repair enzyme Thymine DNA Glycosylase (TDG) are involved in active DNA demethylation by generating and removing, respectively, novel oxidized cytosine species. Mutations of TET1 and TDG, and alterations of the levels of oxidized cytosine species have been identified in human CRC cases, but the biological significance of the TET-TDG demethylation axis in intestinal tumorigenesis is unclear.\n\nMaterial and MethodsWe generated ApcMin mice with additional inactivation of Tet1 and/or Tdg, and characterized the methylome and transcriptome of intestinal adenomas by DREAM and RNA sequencing, respectively.\n\nResultsTet1- and/or Tdg-deficient ApcMin mice show enhanced intestinal tumorigenesis in comparison to wild type Tet1 and Tdg ApcMin mice. Specifically, Tet1 and/or Tdg-deficient ApcMin adenomas manifested increased size or features of erosion and stroma activation. Methylome analysis revealed progressive loss of global DNA hypomethylation in colonic adenomas from Tet1- and Tdg-deficient ApcMin mice, and hypermethylation of CpG islands in Tet1-deficient ApcMin mice. In addition, RNA sequencing showed upregulation of genes in inflammatory and immune response pathways in Tet1-and Tdg-mutant colonic adenomas compared to control ApcMin adenomas.\n\nConclusionsTaken together, these findings demonstrate the important role of active DNA demethylation mediated by TET-TDG in reducing intestinal tumor formation, by modulating the epigenome and inflammatory/immune responses. This study highlights a novel mechanism of epigenetic deregulation during intestinal tumorigenesis with diagnostic, therapeutic and prognostic implications.

cancer biology↗

bioRxiv2019

Supervised dimension reduction for large-scale \"omics\" data with censored survival outcomes under possible non-proportional hazards

The past two decades have witnessed significant advances in high-throughput \"omics\" technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive \"omics\" profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR) - a framework that includes a variety of regression methods - in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data.

bioinformatics↗

bioRxiv2018

Old fibroblasts secrete inflammatory cytokines that drive variability in reprogramming efficiency and may affect wound healing between old individuals

Age-associated chronic inflammation (inflammaging) has emerged as a central hallmark of aging1-3, but its impact on specific cells is still largely unknown. Fibroblasts are present in all tissues and contribute to wound healing4-6. They are also the cell type that is mostly used for induced pluripotent stem cell (iPSC) reprogramming7 - a process that has implications for regenerative medicine and rejuvenation strategies8-17. Here we show that primary fibroblasts from old mice secrete inflammatory cytokines and that there is an increased variability in reprogramming efficiency between fibroblast cultures from old individuals. Individual-to-individual variability is emerging as a key feature of old age18-21, which could reflect distinct aging trajectories, but the underlying causes remain unknown. To identify drivers of this variability, we perform a multi-omic assessment of young and old fibroblast cultures with different reprogramming efficiency. This approach, coupled with single cell transcriptomics, reveals that old fibroblast cultures are heterogeneous and show a greater proportion of activated fibroblasts that secrete inflammatory cytokines, which correlates with reprogramming efficiency. We experimentally validate that activated fibroblasts express inflammatory cytokines in vivo and that their presence is linked to enhanced reprogramming efficiency in culture. Conditioned-media swapping experiments show that extrinsic factors secreted by activated fibroblasts are more critical than intrinsic factors for the individual-to-individual variability in reprogramming efficiency, and we identify TNF as a key inflammatory cytokine underlying this variability. Interestingly, old mice also exhibit variability in wound healing efficiency in vivo and old wounds show an increased subpopulation of activated fibroblasts with a unique TNF signature. Our study shows that a switch in fibroblast composition, and the ratio of inflammatory cytokines they secrete, drives variability in reprogramming in vitro and may influence wound healing in vivo. These findings could help identify personalized strategies to improve iPSC generation and wound healing in older individuals.

cell biology↗

bioRxiv2018

NetrinG1/NGL-1 Axis promotes pancreatictumorigenesis through cancer associated fibroblastderived nutritional supply and immunosuppression

Pancreatic ductal adenocarcinoma (PDAC) has a poor 5-year survival rate and lacks effective therapeutics. Therefore, it is of paramount importance to identify new targets. Using multi-plex data from patient tissue, three-dimensional co-culturing in vitro assays, and orthotopic murine models, we identified Netrin G1 (NetG1) as a promoter of PDAC tumorigenesis. NetG1+ cancer-associated fibroblasts (CAFs) supported PDAC survival, through a NetG1 mediated effect on glutamate/glutamine metabolism. NetG1+ CAFs were intrinsically immunosuppressive and inhibited NK cell mediated killing of tumor cells. These pro-tumor functions were controlled by a signaling circuit downstream to NetG1, which was comprised of AKT/4E-BP1, p38/FRA1, vesicular glutamate transporter 1, and glutamine synthetase. Finally blocking NetG1 with a neutralizing antibody stunted in vivo tumorigenesis, suggesting NetG1 as potential target in PDAC. SignificancePDAC is a devastating disease lacking effective therapies. A major hallmark of PDAC is desmoplasia, characterized by the expansion of CAFs and their extracellular matrix, creating a unique microenvironment that limits blood-supplied nutrition and is highly immunosuppressive. A better understanding of the role of CAFs in PDAC may lead to the identification of new targets for therapeutic intervention. Here, we uncovered roles for NetG1 in CAFs to promote tumorigenesis. NetG1 was important for two major CAF functions: the metabolic support of PDAC cells and the intrinsic immunosuppressive capacity of CAFs. Our results helped clarify the role that CAFs play in PDAC, by defining CAF phenotypes through NetG1 expression. Moreover, we established a link between CAF driven metabolism and their intrinsic immunosuppressive capacity, and identified a signaling circuit that governs NetG1 functions. Finally, we demonstrated the therapeutic potential of inhibiting NetG1 in vivo by limiting tumorigenesis in mice with a neutralizing antibody, illustrating that targeting stromal NetG1 could be an attractive therapeutic approach.

cancer biology↗