Biology subjects

Gupta, V.

Publications and source records attributed to Gupta, V..

10 recordsLinked to original sources

bioRxiv2017

White matter differences in Parkinson’s disease mapped using tractometry

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinsons disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

neuroscience↗

bioRxiv2017

FiberNet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

The brains white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimers disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brains neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

neuroscience↗

bioRxiv2017

Isolation and characterization of BpL1, a broad acting lytic bacteriophage against Brucella

We have isolated a new broad acting lytic brucellaphage (BpL1) from the sewage of a dairy farm. The phage lysed all the 12 Brucella abortus field isolates, B. abortus strain 99 and Brucella melitensis but did not lyse any of the heterologous species tested viz. Staphylococcus aureus, Pasteurella multocida, Escherichia coli, and Salmonella species. Streaking the plaques on Brucella lawn gave clear zones along the streak lines. The plaques were circular with a diameter of 0.5- 3.0 mm. At a concentration of 10-4 the phage count was 4.5 x 106 plaques per ml. It was a tailed phage with icosahedral head (62.2 nm in diameter and 73.71 nm in length), and the head to tail length was 229.21 nm. The phage belonged to the order Caudovirales and family Siphoviridae. It was inactivated within one hour at 55{degrees}C and within 4 hours at -20{degrees}C. Treatment at pH 2 for 4 hours and at pH 4 for 12 hours inactivated it. It was inactivated after 4 hours exposure to sunlight, and within 4 minutes by UV light. Chloroform and Sodium Dodecyl Sulfate inactivated it within 15 minutes. Lysozyme inactivated it within 1 hour whereas RNase treatment did not affect its activity.

microbiology↗

bioRxiv2017

Pathogen Dependent Phenotypic But No Genetic Correlation Between Sexual Activity And Immunity In Male Drosophila melanogaster Subjected To Differential Sexual Selection

The theory of trade-off suggest that limited resources should lead to trade-off in resource intensive traits such as immunity related and sexually selected traits in males. Alternatively, sexual exaggerations can also act as an honest indicator of underlying immunocompetence, leading to positive correlations between these traits. Several studies have addressed this question using experimental evolution. However, they have rarely used ecologically relevant pathogens and fitness measurement (e.g., measuring post-infection survivorship) to find correlations between sexual selection and immunity. Here we attempt to address this caveat by evolving populations of Drosophila melanogaster under differential sexual selection. After more than hundred generations, we infected virgin and mated males from each population with three pathogenic bacteria: Pseudomonas entomophila (Pe), Staphylococcus succinus (Ss) and Providentia rettgeri (Pr). Fitness was measured as either post-infection survivorship (Pe and Ss) or bacterial clearance ability (Pr). Contrary to expectations, sexual selection had no evolutionary effect on male fitness against any of the pathogens. Moreover, mating had a beneficial effect against Pe and Pr, but no effect against Ss, suggesting pathogen specific phenotypic correlations between mating and immunity. Following these results, we discuss the significance of using ecologically relevant pathogens and quantifying host fitness while studying sexual selection-immunity correlations.

evolutionary biology↗

bioRxiv2017

FiberNET: An ensemble deep learning framework for clustering white matter fibers

White matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

bioinformatics↗

bioRxiv2017

Organ-Specific NLR Resistance Gene Expression Varies With Plant Symbiotic Status

Nucleotide-binding site leucine-rich repeat resistance genes (NLRs) allow plants to detect microbial effectors. We hypothesized that NLR expression patterns would reflect organ-specific differences in effector challenge and tested this by carrying out a meta-analysis of expression data for 1,235 NLRs from 9 plant species. We found stable NLR root/shoot expression ratios within species, suggesting organ-specific hardwiring of NLR expression patterns in anticipation of distinct challenges. Most monocot and dicot plant species preferentially expressed NLRs in roots. In contrast, Brassicaceae species, including oilseed rape and the model plant Arabidopsis thaliana, were unique in showing NLR expression skewed towards the shoot across multiple phylogenetically distinct groups of NLRs. The Brassicaceae NLR expression shift coincides with loss of the endomycorrhization pathway, which enables intracellular root infection by symbionts. We propose that its loss offer two likely explanations for the unusual Brassicaceae NLR expression pattern: loss of NLR-guarded symbiotic components and elimination of constraints on general root defences associated with exempting symbionts from targeting. This hypothesis is consistent with the existence of Brassicaceae-specific receptors for conserved microbial molecules and suggests that Brassicaceae species are rich sources of unique antimicrobial root defences.

plant biology↗

bioRxiv2017

RNA helicase, DDX27 regulates proliferation and myogenic commitment of muscle stem cells

Developmental processes depend on the combined efforts of epigenetic, transcriptional and post-transcriptional processes that lead to the production of specific proteins that are important determinants of cellular identity and developmental processes. Ribosomes are a central component of the protein biosynthesis machinery in cells; however, their regulatory roles in the translational control of gene expression in an organ specific context during development remain to be defined. In a genetic screen to identify critical regulators of myogenesis, we identified a DEAD-Box RNA helicase, DDX27, that is required for the proliferation and myogenic commitment of skeletal muscle stem cells. DDX27 deficient skeletal muscle exhibits hypotrophy and impaired regeneration potential. We demonstrate that DDX27 regulates ribosomal RNA (rRNA) maturation, and thereby the ribosome biogenesis and the translation of specific transcripts that are required to maintain pluripotency and myogenic differentiation of satellite cells. These findings provide insight into the translational regulation of gene expression in myogenesis and suggest novel functions for ribosomes in regulating gene expression during skeletal muscle development.\n\nAUTHOR SUMMARYInherited skeletal muscle diseases are the most common form of genetic disorders with primary abnormalities in the structure and function of skeletal muscle resulting in the impaired locomotion in affected patients. A major hindrance to the development of effective therapies is a lack of understanding of biological processes that promote skeletal muscle growth. By performing a forward genetic screen in zebrafish we have identified mutation in a RNA helicase that leads to perturbations of ribosomal biogenesis pathway and impairs skeletal muscle growth and regeneration. Therefore, our studies have identified novel ribosome-based disease processes that may be therapeutic modulated to restore muscle function in skeletal muscle diseases.

genetics↗

bioRxiv2017

Non-linear disease tolerance curves reveal distinct components of host responses to viral infection

The ability to tolerate infection is a key component of host defence and offers potential novel therapeutic approaches for infectious diseases. To yield successful targets for therapeutic intervention, it is important that the analytical tools employed to measure disease tolerance are able to capture distinct host responses to infection. Here, we show that commonly used methods that estimate tolerance as a linear relationship may be inadequate, and that more flexible, non-linear estimates of this relationship may reveal variation in distinct components of host defence. To illustrate this, we measured the survival of Drosophila melanogaster carrying either a functional or non-functional regulator of the JAK-STAT immune pathway (G9a) when challenged with a range of concentrations of Drosophila C Virus (DCV). While classical linear model analyses indicated that G9a affected tolerance only in females, a more powerful non-linear logistic model showed that G9a mediates viral tolerance to different extents in both sexes. This analysis also revealed that G9a acts by changing the sensitivity to increasing pathogen burdens, but does not reduce the ultimate severity of infection. These results indicate that fitting non-linear models to host health-pathogen burden relationships may offer better and more detailed estimates of disease tolerance.

microbiology↗

bioRxiv2017

Costs and benefits of sub-lethal Drosophila C virus infection

Viruses are major evolutionary drivers of insect immune systems. Much of our knowledge of insect immune responses derives from experimental infections using the fruit fly Drosophila melanogaster. Most experiments, however, employ lethal pathogen doses through septic injury, frequently overwhelming host physiology. While this approach has revealed a number of immune mechanisms, it is less informative about the fitness costs hosts may experience during infection in the wild. Using both systemic and oral infection routes we find that even apparently benign, sub-lethal infections with the horizontally transmitted Drosophila C Virus (DCV) can cause significant physiological and behavioral morbidity that is relevant for host fitness. We describe DCV-induced effects on fly reproductive output, digestive health, and locomotor activity, and we find that viral morbidity varies according to the concentration of pathogen inoculum, host genetic background and sex. Notably, sub-lethal DCV infection resulted in a significant increase in fly reproduction, but this effect depended on host genotype. We discuss the relevance of sub-lethal morbidity for Drosophila ecology and evolution, and more broadly, we remark on the implications of deleterious and beneficial infections for the evolution of insect immunity.

evolutionary biology↗

bioRxiv2016

BioBlocks: Programming protocols in biology made easier

The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping and improved reproducibility of results. However, learning to automate and codify experiments is a difficult task as it requires programming expertise. Here, we present a web-based visual development environment called BioBlocks for describing experimental protocols in biology. It is based on Googles Blockly and Scratch, and requires little or no experience in computer programming to automate the execution of experiments. The experiments can be specified, saved, modified and shared between multiple users in an easy manner. BioBlocks is open-source and can be customized to execute protocols on local robotic platforms or remotely i.e. in the cloud. It aims to serve as a de facto open standard for programming protocols in Biology.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC=\"FIGDIR/small/081075_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (51K):\norg.highwire.dtl.DTLVardef@108403corg.highwire.dtl.DTLVardef@b15c17org.highwire.dtl.DTLVardef@14ed6forg.highwire.dtl.DTLVardef@1f046c4_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗