Biology subjects

Paul, D.

Publications and source records attributed to Paul, D..

8 recordsLinked to original sources

bioRxiv2018

Cell and tissue type independent age-associated DNA methylation changes are not rare but common

Age-associated DNA methylation changes have been widely reported across many different tissue and cell types. Epigenetic clocks that can predict chronological age with a surprisingly high degree of accuracy appear to do so independently of tissue and cell-type, suggesting that a component of epigenetic drift is cell-type independent. However, the relative amount of age-associated DNAm changes that are specific to a cell or tissue type versus the amount that occurs independently of cell or tissue type is unclear and a matter of debate, with a recent study concluding that most epigenetic drift is tissue-specific. Here, we perform a novel comprehensive statistical analysis, including matched multi cell-type and multi-tissue DNA methylation profiles from the same individuals and adjusting for cell-type heterogeneity, demonstrating that a substantial amount of epigenetic drift, possibly over 70%, is shared between significant numbers of different tissue/cell types. We further show that ELOVL2 is not unique and that many other CpG sites, some mapping to genes in the Wnt and glutamate receptor signaling pathways, are altered with age across at least 10 different cell/tissue types. We propose that while most age-associated DNAm changes are shared between cell-types that the putative functional effect is likely to be tissue-specific.

bioinformatics↗

bioRxiv2018

Emergence of Multidrug-Resistant Uropathogens harboring ESBL, Carbapenem, Aminoglycosides and AmpC resistant genes from Northern India

Extended-spectrum {beta}-lactamase (ESBL) producing bacteria acts as a serious threat, and its co-existence with other antibiotic resistant gene makes the clinical scenario worse nowadays. Therefore in this study, we investigated the occurrence of ESBL genes coexisting with carbapenem, AmpC and aminoglycoside resistance gene in uropathogens. Out of 1516 urine samples, 454 showed significant bacteriuria with a prevalence rate of 29.94 %. Escherichia coli (n=340) were found to be the most predominant uropathogen followed by Klebsiella pneumoniae (n=92), Pseudomonas aeruginosa (n=10) and Proteus mirabilis (n=9). Among the total uropathogens, sixty-three ESBL-producers were identified which included blaCTX-M-15 (n=32), followed by blaCTX-M-15 + blaOXA-2 (n=15), blaCTX-M-15 + blaOXA-2 + blaTEM (n=6), blaOXA-2 (n=5), bla OXA-2 + bla SHV-76 (n=1), bla TEM+SHV-76 (n= 1) and bla TEM (n=1). All ESBL genes were found on plasmid incompatibility types: HI1, I1, FIA+FIB, FIA and Y and were horizontally transferable. Among 63 ESBL-producers, 59 isolates harboured carbapenem-resistant genes which included blaNDM-5 (n=48), blaNDM-5 + blaOXA-48 (n=5), blaNDM-5 + blaIMP (n=5) and blaNDM-5 + blaIMP + blaVIM (n=1). The ESBL producing uropathogens also harbored 16S rRNA methylase genes which included rmtB (n=9), rmtA (n=4), rmtC (n=1) and ArmA (n=1) followed by AmpC genes which includes CIT (n=8) and DHA-1 (n=1) genes. Imipenem and gentamicin were found to be more effective. We speculating, this is the first report showing the prevalence of multidrug-resistant uropathogens in this area demanding regular surveillance for such resistance mechanisms which will be useful for health personnel to treat ESBL infection and its co-existence with another antibiotic resistance gene.

microbiology↗

bioRxiv2018

Tensorial Blind Source Separation for Improved Analysis of Multi-Omic Data

There is an increased need for integrative analyses of multi-omic data. Although several algorithms for analysing multi-omic data exist, no study has yet performed a detailed comparison of these methods in biologically relevant contexts. Here we benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. Using simulated and real multi-omic data, we find that tICA outperforms established methods in identifying biological sources of data variation at a significantly reduced computational cost. Using two independent multi cell-type EWAS, we further demonstrate how tICA can identify, in the absence of genotype information, mQTLs at a higher sensitivity than competing multi-way algorithms. We validate mQTLs found with tICA in an independent set, and demonstrate that approximately 75% of mQTLs are independent of blood cell subtype. In an application to multi-omic cancer data, tICA identifies many gene modules whose expression variation across tumors is driven by copy number or DNA methylation changes, but whose deregulation relative to the normal state is independent such alterations, an important finding that we confirm by direct analysis of individual data types. In summary, tICA is a powerful novel algorithm for decomposing multi-omic data, which will be of great value to the research community.

bioinformatics↗

bioRxiv2018

The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs

The relation between design principles of signaling network motifs and their robustness against intrinsic noise still remains illusive. In this work we investigate the role of cascading for coping with intrinsic noise due to stochasticity in molecular reactions. We use stochastic approaches to quantify fluctuations in the terminal kinase of phosphorylation-dephosphorylation cascade motifs and demonstrate that cascading highly affects these fluctuations. We show that this purely stochastic effect can be explained by time-varying sequestration of upstream kinase molecules. In particular, we discuss conditions on time scales and parameter regimes which lead to a reduction of output fluctuations. Our results are put into biological context by adapting rate parameters of our modeling approach to biologically feasible ranges for general binding-unbinding and phosphorylation-dephosphorylation mechanisms. Overall, this study reveals a novel role of stochastic sequestration for dynamic noise filtering in signaling cascade motifs.

systems biology↗

bioRxiv2018

DNA methylation oscillation defines classes of enhancers

Understanding the regulatory landscape of human cells requires the integration of genomic and epigenomic maps, capturing combinatorial levels of cell type-specific and invariant activity states.\n\nHere, we segmented whole-genome bisulfite sequencing-derived methylomes into consecutive blocks of co-methylation (COMETs) to obtain spatial variation patterns of DNA methylation (DNAm oscillations) integrated with histone modifications and promoter-enhancer interactions derived from promoter capture Hi-C (PCHi-C) sequencing of the same purified blood cells.\n\nMapping DNAm oscillations onto regulatory genome annotation revealed that enhancers are enriched for DNAm hyper-oscillations (>30-fold), where multiple machine learning models support DNAm as predictive of enhancer location. Based on this analysis, we report overall predictive power of 99% for DNAm oscillations, 77.3% for DNaseI, 41% for CGIs, 20% for UMRs and 0% for LMRs, demonstrating the power of DNAm oscillations over other methods for enhancer prediction. Methylomes of activated and non-activated CD4+ T cells indicate that DNAm oscillations exist in both states irrespective of activation; hence they can be used to determine the location of latent enhancers.\n\nOur approach advances the identification of tissue-specific regulatory elements and outperforms previous approaches defining enhancer classes based on DNA methylation.

genomics↗

bioRxiv2017

ProGeM: A framework for the prioritisation of candidate causal genes at molecular quantitative trait loci

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids, and proteins through genome-wide association studies (GWAS) represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here we present a framework for the \"Prioritisation of candidate causal Genes at Molecular QTLs\" (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritised by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of \"true positive\" causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

bioinformatics↗

bioRxiv2017

Platelet enrichment from whole blood in a clog-free microfluidic radial pillar device (RAPID)

Pillar-based passive microfluidic devices combine the advantages of simple designs, low device footprint, and high selectivity for size-based separation of blood cells. Most of these device designs have been validated with dilute blood samples. Handling whole blood in pillar-based devices is extremely challenging due to clogging. The high proportion of cells (particularly red blood cells) in blood, the varying sizes and stiffness of the different blood cells, and the tendency of the cells to aggregate lead to clogging of the pillars within a short period. We recently reported a radial pillar device (RAPID) design for contin-uous and high throughput separation of multi-sized rigid polystyrene particles in a single experiment. In this manuscript, we have given detailed guidelines to modify the design of RAPID for any application with deformable objects (e.g. cells). We have adapted RAPID to work with blood samples directly without any pre-processing steps. We were successful in operating the device with whole blood for almost 6 hours, which is difficult to achieve with most pillar-based devices. Finally, we demonstrated up to ~ 60-fold enrichment of platelets as an illustration of the improved device design. Whole blood pillar-based platelet clog-free RAPID

physiology↗

bioRxiv2017

A radial pillar device (RAPID) for continuous and high-throughput separation of multi-sized particles

Pillar-based microfluidic sorting devices are preferred for isolation of rare cells due to their simple designs and passive operation. Dead-end pillar filters can efficiently capture large rare cells, such as, circulating tumor cells (CTCs), nucleated red blood cells (NRBCs), etc., but they get clogged easily. Cross flow filters are preferred for smaller rare particles (e.g. separating bacteria from blood), but they need additional buffer inlets and a large device footprint for efficient operation. We have designed a new microparticle separation device i.e. Radial Pillar Device (RAPID) that combines the advantages of dead-end and cross flow filters. RAPID can simultaneously isolate both large and small rare particles from a mixed population, while functioning for several hours without clogging. We have achieved simultaneous separation of 10{micro}m and 2{micro}m polystyrene particles from a mixture of 2 {micro}m, 7 {micro}m and 10 {micro}m beads. RAPID achieved average separation purity and recovery in excess of 90%. The throughput of our device ( 3ml/min) is 10 and 100 times higher compared to cross flow and dead-end filters respectively, thereby justifying the name RAPID.

bioengineering↗