Biology subjects

Kumar, T.

Publications and source records attributed to Kumar, T..

7 recordsLinked to original sources

bioRxiv2021

A conserved Guided Entry of Tail-anchored pathway is involved in the trafficking of tail-anchored membrane proteins in Plasmodium falciparum.

Tail-anchored (TA) proteins are defined by the absence of N-terminus signal sequence and the presence of a single transmembrane domain (TMD) proximal to their extreme C-terminus. They play fundamental roles in cellular processes including vesicular trafficking, protein translocation and quality control. Accordingly, TA proteins are post-translationally integrated by the Guided Entry of TA (GET) pathway to the cellular membranes; with their N-terminus oriented towards the cytosol and C-terminus facing the organellar lumen. The TA repertoire and the GET machinery have been extensively characterized in the yeast and mammalian systems, however, they remain elusive in the human malaria parasite Plasmodium falciparum. In this study, we bioinformatically predicted a total of 63 TA proteins in the P. falciparum proteome and revealed the association of their subset with the P. falciparum homolog of Get3 (PfGet3). In addition, our proximity labelling studies either definitively identified or shortlisted the other eligible GET constituents, and our in vitro association studies validated associations between PfGet3 and the corresponding homologs of Get4 and Get2 in P. falciparum. Collectively, this study reveals the presence of proteins with hallmark TA signatures and the involvement of evolutionary conserved GET trafficking pathway for their targeted delivery within the parasite. SynopsisTail-anchored (TA) proteins, characterized by an absence of N-terminal signal sequence and the presence of a transmembrane domain near the C-terminus, are post-translationally inserted at organellar membranes by the conserved multi-component Guided Entry of TA (GET) pathway. Here, we identified the putative homologs of GET machinery in the human malaria parasite Plasmodium falciparum and revealed their association with a subset of bioinformatically predicted 63 putative TA proteins, thereby validating the functional existence of this trafficking pathway within the apicomplexan parasite. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/442402v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1c0239org.highwire.dtl.DTLVardef@d5ffbaorg.highwire.dtl.DTLVardef@1884a50org.highwire.dtl.DTLVardef@c6df7d_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

Optofluidic Fiber Component for Separation and counting of Micron-Sized Particles

An all-fiber separation component capable of sorting and counting micron-sized particles based on size is presented. A sequence of silica fiber capillaries with various diameters and longitudinal cavities were used to fabricate the component for separation and detection in an uninterrupted flow. Fluorescence microparticles of 1 m and 10 m sizes are mixed in a visco-elastic fluid and infused into the all-fiber separation component. Elasto-inertial forces focus the larger particle to the center of the silica capillary, while the smaller microparticles exit from a side capillary. Analysis of the separated particles at the output showed a separation efficiency of 100% for the 10 m and 97% for the 1 m particles. In addition, the counting of the larger particles is demonstrated in the same flow. The separated 10 m particles are further routed through another all-fiber component for counting. A counting speed of ~1400 particles/min and with the variation in amplitude of 10% is achived. A combination of separation and counting can be powerful tool may find several applications in biology and medicine, such as separation and analysis of exosomes, bacteria, and blood cell sub-populations.

bioengineering↗

bioRxiv2021

Predicting cross-tissue hormone-gene relations usingbalanced word embeddings

MotivationLarge volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing. ResultsWe present here a first study to identify from literature the genes involved in inter-tissue signaling via a hormone in the human body. Our models BioEmbedS and BioEmbedS-TS respectively predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormones production or response. Our models are classifiers trained on word embeddings that we had carefully balanced across different strata of the training data such as across production vs. response genes of a hormone (or) well-studied vs. poorly-represented hormones in the literature. Model training and evaluation are enabled by a unified dataset called HGv1 of ground-truth associations between genes and known endocrine hormones that we had compiled. Our models not only recapitulate known gene mediators of tissue-tissue signaling (e.g., at average 70.4% accuracy for BioEmbedS), but also predicts novel genes involved in inter-tissue communication in humans. Furthermore, the species-agnostic nature of our ground-truth HGv1 data and our predictive modeling approach, demonstrated concretely using human data and generalized to mouse, hold much promise for future work on elucidating inter-tissue signaling in other multi-cellular organisms. AvailabilityProposed HGv1 dataset along with our models predictions, and the associated code to reproduce this work are available respectively at https://cross-tissue-signaling.herokuapp.com/, and https://github.com/BIRDSgroup/BioEmbedS. Contactnmanik@cse.iitm.ac.in

bioinformatics↗

bioRxiv2020

High resolution and high throughput bacteria separation from blood using elasto-inertial microfluidics

Improved sample preparation has the potential to address a huge unmet need for fast turnaround sepsis tests that enable early administration of appropriate antimicrobial therapy. In recent years, inertial and elasto-inertial microfluidics-based sample preparation has gained substantial interest for bioparticle separation applications. However, for applications in blood stream infections the throughput and bacteria separation efficiency has thus far been limited. In this work, for the first time we report elasto-inertial microfluidics-based bacteria isolation from blood at throughputs and efficiencies unparalleled with current microfluidics-based state of the art. In the method, bacteria-spiked blood sample is prepositioned close to the outer wall of a spiral microchannel using a viscoelastic sheath buffer. The blood cells will remain fully focused throughout the length of the channel while bacteria migrate to the inner wall for effective separation. Initially, particles of different sizes were used to investigate particle focusing and the separation performance of the spiral device. A separation efficiency of 96% for the 1 {micro}m particles was achieved, while 100% of 3 {micro}m particles were recovered at the desired outlet at a high throughput of 1 mL/min. Following, processing blood samples revealed a minimum of 1:2 dilution was necessary to keep the blood cells fully focus at the outer wall. In experiments involving bacteria spiked in diluted blood, viable E.coli were continuously separated at a total flow rate of 1 mL/min, with an efficiency between 82 to 90% depending on the blood dilution. Using a single spiral, it takes 40 minutes to process 1 mL of blood at a separation efficiency of 82% and 3 hours at 90% efficiency. To the best of our knowledge, this is the highest blood sample throughput per single microfluidic chip reported for the corresponding separation efficiency. As such, the label-free, passive and high throughput bacteria isolation method has a great potential for speeding up downstream phenotypic and molecular analysis of bacteria.

biophysics↗

bioRxiv2020

Fluorescence Microscopy Datasets for Training Deep Neural Networks

BackgroundFluorescence microscopy is an important technique in many areas of biological research. Two factors which limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging, and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal to noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample. FindingsTo employ deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high quality data sets which can be used to train and evaluate deep learning methods under development. ConclusionThe availability of high quality data is vital for training convolutional neural networks which are used in current machine learning approaches.

bioinformatics↗

bioRxiv2020

CD56 regulates human NK cell cytotoxicity through Pyk2

Natural killer (NK) cells are innate immune cells that control viral infection and tumorigenic cell growth through targeted cell lysis and cytokine secretion. Human NK cells are classically defined as CD56+CD3- in peripheral blood. CD56 is neural cell adhesion molecule (NCAM1), and despite its ubiquitous expression on human NK cells, the role of CD56 in human NK cell cytotoxic function has not been fully explored. In non-immune cells, NCAM can induce signaling, mediate adhesion, and promote exocytosis, in part through interactions with focal adhesion kinase (FAK). Here we describe the generation and use of CD56-deficient human NK cell lines to define a novel requirement for CD56 in target cell lysis. Namely, we demonstrate that deletion of CD56 on the NK92 cell line led to impaired cytotoxic function against multiple susceptible target cell lines. Deletion of CD56 in a second NK cell line, YTS cells, led to a less severe cytotoxicity defect but impairment in cytokine secretion. Confocal microscopy of wild-type and CD56-KO NK92 cells conjugated to susceptible targets revealed that CD56-KO cells failed to polarize during immunological synapse (IS) formation and had severely impaired exocytosis of lytic granules at the IS. Phosphorylation of the FAK family member Pyk2 at tyrosine 402 was decreased in NK92 CD56-KO cells, demonstrating a functional link between CD56 and IS formation and signaling in human NK cells. Cytotoxicity, lytic granule exocytosis, and the phosphorylation of Pyk2 were rescued by the reintroduction of NCAM140 (CD56), into NK92 CD56-KO cells. These data highlight a novel functional role for CD56 in stimulating exocytosis and promoting cytotoxicity in human NK cells.

immunology↗

bioRxiv2019

Topology-driven analysis of protein-protein interaction networks detects functional genetic modules regulating reproductive capacity

Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

developmental biology↗