Biology subjects

Dandekar, T.

Publications and source records attributed to Dandekar, T..

4 recordsLinked to original sources

bioRxiv2018

Functional genomics identifies AMPD2 as a new prognostic marker for undifferentiated pleomorphic sarcoma

Soft-tissue sarcomas are rare, heterogeneous and often aggressive mesenchymal cancers. Many of them are associated with poor outcome, in part because biomarkers that can reliably identify high-risk patients are lacking. Studies on sarcomas often are limited by small sample sizes rendering the identification of novel biomarkers difficult when focusing only on individual cohorts. However, the increasing number of publicly available omics data opens inroads to overcome this obstacle.\n\nHere, we combine high-throughput transcriptome analyses, immunohistochemistry, and functional assays to show that high adenosine monophosphate deaminase 2 (AMPD2) is a robust prognostic biomarker for worse patient outcome in undifferentiated pleomorphic sarcoma (UPS). Publicly available gene expression and survival data for UPS from two independent studies, The Cancer Genome Atlas (TCGA) and the CINSARC reference dataset, were subjected to survival association testing. Genes, whose high expression was significantly correlated with worse outcome in both cohorts (overall and metastasis-free survival), were considered as prognostic marker candidates. The best candidate, AMPD2, was validated on protein level in an independent tissue microarray. Analysis of DNA copy-number and matched gene expression data indicated that high AMPD2 expression is significantly correlated with copy-number gains at the AMPD2 locus. Gene-set enrichment analyses of AMPD2 co-expressed genes in both UPS gene expression datasets suggested that highly AMPD2 expressing tumors are enriched in gene signatures involved in tumorigenesis. Consistent with this prediction in primary tumors, knockdown of AMPD2 by RNA interference with pooled siRNAs or a doxycycline-inducible shRNA construct in the UPS cell line FPS-1 markedly inhibited proliferation in vitro and tumorigenicity in vivo.\n\nCollectively, these results provide evidence that AMPD2 may serve as a novel biomarker for outcome prediction in UPS. Our study exemplifies how the integration of available omics data, immunohistochemical analyses, and functional experiments can identify novel biomarkers even in a rare sarcoma, which may serve as a blueprint for biomarker identification for other rare cancers.

pathology↗

bioRxiv2018

Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning

1. AbstractSynaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical \"clear core\" vesicles (CCV) and the typically larger \"dense core\" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].\n\nTo better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.\n\n2. Author summaryVesicles are important components of the cell, and synaptic vesicles are central for neuronal signaling. Two types of synaptic vesicles can be distinguished by electron microscopy: the classical \"clear core\" vesicles (CCVs) and the typically larger \"dense core\" vesicles (DCVs). The distinct appearance of vesicles is caused by their different cargos. To rapidly distinguish between both vesicle types, we present here a new automated approach to classify vesicles in electron tomograms. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, an ImageJ macro, to reliably distinguish CCVs and DCVs using specific image-based features. The approach was trained and validated using data-sets that were hand curated by microscopy experts. Our technique can be transferred to more extensive comparisons in both stages as well as to other neurobiology questions regarding synaptic vesicles.

neuroscience↗

bioRxiv2017

IntelliEppi: Intelligent reaction monitoring and holistic data management system for the molecular biology lab

Daily alterations of routines and protocols create high, yet so far unmet demands for intelligent reaction monitoring, quality control and data management in molecular biology laboratories. To meet such needs, the \"internet of things\" is implemented here. We propose an approach which combines direct tracking of lab tubes, reactions and racks with a comprehensive data management system. Reagent tubes in this system are tagged with 2D data matrices or imprinted RFID-chips using a unique identification number. For each tube, individual content and all relevant information based on conducted experimental procedures are stored in an experimental data management system. This information is managed automatically but allow scientists to engage and interfere via user-friendly graphical interface. Tagged tubes are used in connection with a detectable RFID-tagged rack. We show that reaction protocols, HTS storage and complex reactions are easily planned and controlled.

bioinformatics↗

bioRxiv2017

Draft genome of the Heterotardigrade Milnesium tardigradum sheds light on ecdysozoan evolution

Tardigrades are among the most stress tolerant animals and survived even unassisted exposure to space in low earth orbit. Still, the adaptations leading to these unusual physiological features remain unclear. Even the phylogenetic position of this phylum within the Ecdysozoa is unclear. Complete genome sequences might help to address these questions as genomic adaptations can be revealed and phylogenetic reconstructions can be based on new markers. Here, we present a first draft genome of a species from the family Milnesiidae, namely Milnesium tardigradum. We consistently place M. tardigradum and the two previously sequenced Hypsibiidae species, Hypsibius dujardini and Ramazzottius varieornatus, as sister group of the nematodes with the arthropods as outgroup. Based on this placement, we identify a massive gene loss thus far attributed to the nematodes which predates their split from the tardigrades. We provide a comprehensive catalog of protein domain expansions linked to stress response and show that previously identified tardigrade-unique proteins are erratically distributed across the genome of M. tardigradum. We further suggest alternative pathways to cope with high stress levels that are yet unexplored in tardigrades and further promote the phylum Tardigrada as a rich source of stress protection genes and mechanisms.

evolutionary biology↗