Biology subjects

Patel, A.

Publications and source records attributed to Patel, A..

8 recordsLinked to original sources

bioRxiv2018

C/VDdb: a multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD)

The cardiovascular disease (C/VD) database is an integrated and clustered information resource that covers multi-omic studies (microRNA, genomics, proteomics and metabolomics) of cardiovascular-related traits with special emphasis on coronary artery disease (CAD). This resource was built by mining existing literature and public databases and thereafter manual biocuration was performed. To enable integration of omic data from distinct platforms and species, a specific ontology was applied to tie together and harmonise multi-level omic studies based on gene and protein clusters (CluSO) and mapping of orthologous genes (OMAP) across species.\n\nCAD continues to be a leading cause of death in the population worldwide, and it is generally thought to be an age-related disease. However, CAD incidence rates are now known to be highly influenced by environmental factors and interactions, in addition to genetic determinants. With the complexity of CAD aetiology, there is a difficulty in research studies to elucidate general elements compared to other cardiovascular diseases.\n\nData from 92 studies, covering 13945 molecular entries (4353 unique molecules) is described, including data descriptors for experimental setup, study design, discovery-validation sample size and associated fold-changes of the differentially expressed molecular features (p-value<0.05). A dedicated interactive web interface, equipped with a multi-parametric search engine, data export and indexing menus are provided for a user-accessible browsing experience.\n\nThe main aim of this work was the development of a data repository linking clinical information and molecular differential expression in several CVD-related traits from multi-omics studies (genomics, transcriptomics, proteomics and metabolomics). As an example case of how to query and identify data sets within the database framework and concomitantly demonstrate the database utility, we queried CAD-associated studies and performed a systems-level integrative analysis.\n\nURL: www.padb.org/cvd

systems biology↗

bioRxiv2018

TEsmall identifies small RNAs associated with targeted inhibitor resistance in melanoma

MicroRNAs (miRNAs) are small 21-22nt RNAs that act to regulate the expression of mRNA target genes through direct binding to mRNA targets. While miRNAs typically dominate small RNA transcriptomes, many other classes are present including tRNAs, snoRNAs, snRNAs, Y-RNAs, piRNAs, and siRNAs. Interactions between processing machinery and targeting networks of these various small RNA classes remains unclear, largely because these small RNAs are typically analyzed separately. Here we present TEsmall, a tool that allows for the simultaneous processing and analysis of small RNAs from each annotated class in a single integrated workflow. The pipeline begins with raw fastq reads and proceeds all the way to producing count tables formatted for differential expression. Several interactive charts are also produced to look at overall distributions in length and annotation classes. We next applied the TEsmall pipeline to small RNA libraries generated from melanoma cells responding to targeted inhibitors of the MAPK pathway. Targeted oncogene inhibitors have emerged as way to tailor cancer therapies to the particular mutations present in a given tumor. While these targeted strategies are typically effective for short intervals, the emergence of resistance is extremely common, limiting the effectiveness of single-agent therapeutics and driving the need for a better understanding of resistance mechanisms. Using TEsmall, we identified several microRNAs and other small RNA classes that are enriched in inhibitor resistant melanoma cells in multiple melanoma cell lines and may be able to serve as markers of resistant populations more generally.

genomics↗

bioRxiv2018

Azithromycin reduces systemic inflammation and provides survival benefit in murine model of polymicrobial sepsis

Sepsis is a life threatening systemic inflammatory condition triggered as a result of excessive host immune response to infection. In the past, drugs modulating immune reactions have demonstrated protective effect in sepsis. Azithromycin (macrolide antibiotic) with immunomodulatory activity was therefore evaluated in combination with ceftriaxone in a more clinically relevant murine model of sepsis induced by caecal ligation and puncture (CLP). First, mice underwent CLP and 3 h later were administered with vehicle, sub-effective dose of ceftriaxone (100 mg/kg, subcutaneous) alone or in combination with immunomodulatory dose of azithromycin (100 mg/kg, intraperitoneal). Survival was then monitored for 5 days. Parameters like body temperature, blood glucose, total white blood cell count, plasma glutathione (GSH), plasma and lung myeloperoxidase (MPO) as well as cytokine (interleukin IL-6, IL-1{beta}, tumor necrosis factor-) levels along with bacterial load in blood, peritoneal fluid and lung homogenate were measured 18 h after CLP challenge. Combination group significantly improved the survival of CLP mice. It attenuated the elevated levels of inflammatory cytokines and MPO in plasma and lung tissue and increased the body temperature, blood glucose and GSH which were otherwise markedly decreased in CLP mice. Ceftriaxone exhibited significant reduction of bacterial count in blood, peritoneal fluid and lung homogenate, while co-administration of azithromycin did not further reduce it. This confirms that survival benefit by azithromycin was due to immunomodulation and not by its antibacterial action. Findings of this study indicate that azithromycin in combination with ceftriaxone could exhibit clinical benefit in sepsis.

pharmacology and toxicology↗

bioRxiv2017

SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations

Single-cell RNA-Seqs (scRNA-Seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-Seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation): a robust method for scRNA-Seq analysis that provides quantitative statistical metrics at each step of the scRNA-Seq analysis pipeline including metrics for: the determination of the number of clusters present, the likelihood that each cell belongs to a given cluster, and the association of each gene marker in determining cluster membership. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-Seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. In addition, we compare integrated genomic and transcriptomic markers to show that resistance can arise stochastically within multiple distinct clonal populations.

genomics↗

bioRxiv2017

Theta and alpha oscillations are traveling waves in the human neocortex

Human cognition requires the coordination of neural activity across widespread brain networks. Here we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found that contiguous clusters of cortex in individual patients showed oscillations at specific frequencies in the range of 2 to 15 Hz. These clusters displayed spatial phase gradients, indicating that individual oscillation cycles moved across the cortex at ~0.25-0.75 m/s. We found that traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent during good performance. Traveling waves showed a correlation between propagation speed and temporal frequency, which suggests that they propagate across the cortex following principles of phase-coupled oscillatory networks. By demonstrating that theta and alpha traveling waves are widespread and behaviorally relevant, our results suggest a broad role for brain oscillations in supporting cortical connectivity by organizing neural activity across space and time.

neuroscience↗

bioRxiv2017

Ultra-sensitive mutation detection and genome-wide DNA copy number reconstruction by error corrected circulating tumour DNA sequencing

Minimally invasive circulating free DNA (cfDNA) analysis can portray cancer genome landscapes but highly sensitive and specific genetic approaches are necessary to accurately detect mutations with often low variant frequencies. We developed a targeted cfDNA sequencing technology using novel off-the-shelf molecular barcodes for error correction, in combination with custom solution hybrid capture enrichment. Modelling based on cfDNA yields from 58 patients shows that our assay, which requires 25ng of cfDNA input, should be applicable to >95% of patients with metastatic colorectal cancer. Sequencing of a 163.3 kb target region including 32 genes detected 100% of single nucleotide variants with 0.15% variant frequency in cfDNA spike-in experiments. Molecular barcode error correction reduced false positive mutation calls by 98.6%. In a series of 28 patients with metastatic colorectal cancers, 80 out of 91 (88%) mutations previously detected by tumour tissue sequencing were called in the cfDNA. Call rates were similar for single nucleotide variants and small insertions/deletions. Mutations only called in cfDNA but not detectable in matched tumour tissue included, among others, a subclonal resistance driver mutation to anti-EGFR antibodies in the KRAS gene, multiple activating PIK3CA mutations in each of two patients (indicative of parallel evolution), and TP53 mutations originating from clonal haematopoiesis. Furthermore, we demonstrate that cfDNA off-target read analysis allows the reconstruction of genome wide copy number aberration profiles from 71% of these 28 cases. This error-corrected ultra-deep cfDNA sequencing assay with a target region that can be readily customized enables broad insights into cancer genomes and evolution.

genomics↗

bioRxiv2017

The small molecule KHS101 induces bioenergetic dysfunction in glioblastoma cells through inhibition of mitochondrial HSPD1

Pharmacological inhibition of uncontrolled cell growth with small molecule inhibitors is a potential strategy against glioblastoma multiforme (GBM), the most malignant primary brain cancer. Phenotypic profiling of the neurogenic small molecule KHS101 revealed the chemical induction of lethal cellular degradation in molecularly-diverse GBM cells, independent of their tumor subtype, whereas non-cancerous brain cells remained viable. Mechanism-of-action (MOA) studies showed that KHS101 specifically bound and inhibited the mitochondrial chaperone HSPD1. In GBM but not non-cancerous brain cells, KHS101 elicited the aggregation of an enzymatic network that regulates energy metabolism. Compromised glycolysis and oxidative phosphorylation (OXPHOS) resulted in the metabolic energy depletion in KHS101-treated GBM cells. Consistently, KHS101 induced key mitochondrial unfolded protein response factor DDIT3 in vitro and in vivo, and significantly reduced intracranial GBM xenograft tumor growth upon systemic administration, without discernible side effects. These findings suggest targeting of HSPD1-dependent oncometabolic pathways as an anti-GBM therapy.

cancer biology↗

bioRxiv2017

Telomere elongation and Telomerase activity in Normal and Cancer cell lines: HEK-293, HeLa and A549

Telomerase, an eukaryotic ribonucleoprotein (RNP) complex, consists of an essential RNA template and a reverse transcriptase termed as human telomerase reverse transcriptase (hTERT). By using this reverse transcriptase enzyme, telomerase RNP maintains its telomeric length in all cancer cells and in few stem cells. As cells age, telomerase level decreases and telomere get shorten, leading to senescence. Presence of telomerase helps delay the senescence due to active production of telomere, leading to cell turning malignant and eventually cancerous. In this experiment, we would investigate the telomerase activity tested using q PCR and telomerase level using western blot in three different cell lines, HEK-293, HeLa and A549.\n\nHypothesisWhere HEK-293 cells have longer telomeres and higher telomerase activity, HeLa and A549 cell lines will have varied length of telomere but higher telomerase activity.

molecular biology↗