Biology subjects

Brown, M.

Publications and source records attributed to Brown, M..

11 recordsLinked to original sources

bioRxiv2018

Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

Public health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

epidemiology↗

bioRxiv2018

Nucleic acid extraction and sequencing from low-biomass synthetic Mars analog soils for in situ life detection

Recent studies regarding the origin of life and Mars-Earth meteorite transfer simulations suggest that biological informational polymers, such as nucleic acids (DNA and RNA), have the potential to provide unambiguous evidence of life on Mars. To this end, we are developing a metagenomics-based life-detection instrument which integrates nucleic acid extraction and nanopore sequencing: The Search for Extra-Terrestrial Genomes (SETG). Our goal is to isolate and sequence nucleic acids from extant or preserved life on Mars in order to determine if a particular genetic sequence (1) is distantly-related to life on Earth indicating a shared-ancestry due to lithological exchange, or (2) is unrelated to life on Earth suggesting a convergent origin of life on Mars. In this study, we validate prior work on nucleic acid extraction from cells deposited in Mars analog soils down to microbial concentrations observed in the driest and coldest regions on Earth. In addition, we report low-input nanopore sequencing results equivalent to 1 ppb life-detection sensitivity achieved by employing carrier sequencing, a method of sequencing sub-nanogram DNA in the background of a genomic carrier.

molecular biology↗

bioRxiv2018

MicroRNA-196a is regulated by ER and is a prognostic biomarker in ER+ Breast Cancer

MicroRNAs are potent post-transcriptional regulators involved in all hallmarks of cancer. Mir-196a is transcribed from two loci and has been implicated in a wide range of developmental and pathogenic processes, with targets including Hox, Fox, Cdk inhibitors and annexins. Genetic variants and altered expression of miR196a are associated with risk and progression of multiple cancers including breast cancer, however little is known about the regulation of the genes encoding this miRNA, nor the impact of variants therein. Here we demonstrate that MIR196A displays complex and dynamic expression patterns, in part controlled by long range transcriptional regulation between promoter and enhancer elements bound by ER. Expression of this miRNA is significantly increased in models of hormone receptor positive disease resistance. The expression of MIR196A also proves to be a robust prognostic factor for patients with advanced and post-menopausal ER+ disease. This work sheds light on the normal and abnormal regulation of MIR196A and provides a novel stratification method for therapeutically resistant breast cancer.

cancer biology↗

bioRxiv2018

MiR-29b-1-5p is altered in BRCA1 mutant tumours and is a biomarker in basal- like breast cancer

Depletion of BRCA1 protein in mouse mammary glands results in defects in lactational development and increased susceptibility to mammary cancer. Extensive work has focussed on the role of BRCA1 in the normal breast and in the development of breast cancer, the cell of origin for BRCA1 tumours and the protein-coding genes altered in BRCA1 deficient cells. However, the role of non-coding RNAs in BRCA1-deficient cells is poorly understood. To evaluate miRNA expression in BRCA1 deficient mammary cells, RNA sequencing was performed on the mammary glands of Brca1 knockout mice. We identified 140 differentially expressed miRNAs, 9 of which were also differentially expressed in human BRCA1 breast tumours or familial non-BRCA1 patients and during normal gland development. We show that BRCA1 binds to putative cis-elements in promoter regions of the miRNAs and regulates their expression, and that four miRNAs (miR-29b-1-5p, miR-664, miR-16-2 and miR-744) significantly stratified the overall survival of basal-like tumours. Importantly the prognostic value of miR-29b-1-5p was higher in significance than several commonly used clinical biomarkers. These results emphasize the role of Brca1 in modulating expression of miRNAs and highlights the potential for BRCA1 regulated miRNAs to be informative biomarkers associated with BRCA1 loss and survival in breast cancer.

cancer biology↗

bioRxiv2018

The SERM/SERD Bazedoxifene Disrupts ESR1 Helix 12 to Overcome Acquired Hormone Resistance in Breast Cancer Cells

Acquired resistance to endocrine therapy remains a significant clinical burden for breast cancer patients. Somatic mutations in the ESR1 (estrogen receptor alpha (ER) gene ligand-binding domain (LBD) represent a recognized mechanism of acquired resistance. Antiestrogens with improved efficacy versus tamoxifen might overcome the resistant phenotype in ER+ breast cancers. Bazedoxifene (BZA) is a potent antiestrogen that is clinically approved for use in hormone replacement therapies. We find BZA possesses improved inhibitory potency against the Y537S and D538G ER mutants compared to tamoxifen and has additional inhibitory activity in combination with the CDK4/6 inhibitor palbociclib. In addition, comprehensive biophysical and structural biology studies show that BZAs selective estrogen receptor degrading (SERD) properties that override the stabilizing effects of the Y537S and D538G ER mutations.\n\nSignificanceBazedoxifene (BZA) is a potent orally available antiestrogen that is clinically approved for use in hormone replacement therapy (DUAVEE). We explore the efficacy of BZA to inhibit activating somatic mutants of ER that can arise in metastatic breast cancers after prolonged exposure to aromatase inhibitors or tamoxifen therapy. Breast cancer cell line, biophysical, and structural data show that BZA disrupts helix 12 of the ER ligand binding domain to achieve improved potency against Y537S and D538G somatic mutants compared to 4-hydroxytamoxifen.

cancer biology↗

bioRxiv2018

EukRef: phylogenetic curation of ribosomal RNA to enhance understanding of eukaryotic diversity and distribution

Environmental sequencing has greatly expanded our knowledge of micro-eukaryotic diversity and ecology by revealing previously unknown lineages and their distribution. However, the value of these data is critically dependent on the quality of the reference databases used to assign an identity to environmental sequences. Existing databases contain errors, and struggle to keep pace with rapidly changing eukaryotic taxonomy, the influx of novel diversity, and computational challenges related to assembling the high-quality alignments and trees needed for accurate characterization of lineage diversity. EukRef (eukref.org) is a community driven initiative that addresses these challenges by bringing together taxonomists with expertise spanning the complete eukaryotic tree of life and microbial ecologists that actively use environmental sequencing data for the purpose of developing reliable reference databases across the diversity of microbial eukaryotes. EukRef organizes and facilitates rigorous sequence data mining and annotation by providing protocols, guidelines and tools to do so.

microbiology↗

bioRxiv2018

Resistance to pentamidine is mediated by AdeAB, regulated by AdeRS, and influenced by growth conditions in Acinetobacter baumannii ATCC 17978

In recent years, effective treatment of infections caused by Acinetobacter baumannii has become challenging due to the ability of the bacterium to acquire or up-regulate antimicrobial resistance determinants. Two component signal transduction systems are known to regulate expression of virulence factors including multidrug efflux pumps. Here, we investigated the role of the AdeRS two component signal transduction system in regulating the AdeAB efflux system, determined whether AdeA and/or AdeB can individually confer antimicrobial resistance, and explored the interplay between pentamidine resistance and growth conditions in A. baumannii ATCC 17978. Results identified that deletion of adeRS affected resistance towards chlorhexidine and 4,6-diamidino-2-phenylindole dihydrochloride, two previously defined AdeABC substrates, and also identified an 8-fold decrease in resistance to pentamidine. Examination of {Delta}adeA, {Delta}adeB and {Delta}adeAB cells augmented results seen for {Delta}adeRS and identified a set of dicationic AdeAB substrates. RNA-sequencing of {Delta}adeRS revealed transcription of 290 genes were [≥]2-fold altered compared to the wildtype. Pentamidine shock significantly increased adeA expression in the wildtype, but decreased it in {Delta}adeRS, implying that AdeRS activates adeAB transcription in ATCC 17978. Investigation under multiple growth conditions, including the use of Biolog phenotypic microarrays, revealed resistance to pentamidine in ATCC 17978 and mutants could be altered by bioavailability of iron or utilization of different carbon sources. In conclusion, the results of this study provide evidence that AdeAB in ATCC 17978 can confer intrinsic resistance to a subset of dicationic compounds and in particular, resistance to pentamidine can be significantly altered depending on the growth conditions.

microbiology↗

bioRxiv2017

An evidence-based approach to globally assess the covariate-dependent effect of MTHFR SNP rs1801133 on plasma homocysteine: a systematic review and meta-analysis

BackgroundThe single nucleotide polymorphism (SNP) of the gene Methylenetetrahydrofolate Reductase (MTHFR) C677T (or rs1801133) is the most established genetic factor that increases plasma total homocysteine (tHcy) and consequently results in hyperhomocysteinemia. Yet given the limited penetrance of this genetic variant, it is necessary to individually predict the risk of hyperhomocysteinemia for a rs1801133 carrier.\n\nObjectiveWe hypothesized that variability of this genetic risk is largely due to the presence of factors (covariates) that serve as effect modifiers and/or confounders, such as folic acid (FA) intake, and aimed to assess this risk in the complex context of these covariates.\n\nDesignWe systematically extracted from published studies the data of tHcy, rs1801133, and any previously reported rs1801133 covariates. The resulting meta-dataset was first used to analyze the covariates modifying effect by meta regression and other statistical means. Subsequently, we stratified tHcy data by the rs1801133 genotypes and analyzed under each genotype the variability of the risk resulted from the covariates confounding.\n\nResultsThe dataset contains data of 36 rs1801133 covariates that were collected from 114,448 subjects and 249 qualified studies, among which 6 covariates (sex, age, race, FA intake, smoking, and alcohol consumption) are the most frequently informed and therefore included for statistical analysis. The effect of rs1801133 on tHcy exhibits significant variability that can be attributed to effect modification and, to a larger degree, confounding by these covariates. Via statistical modeling, we predicted the covariate-dependent risk of tHcy elevation and hyperhomocysteinemia in a systematic manner.\n\nConclusionswe demonstrated an evidence-based approach that globally assesses the covariate-dependent effect of rs1801133 on tHcy. The results should assist clinicians in interpreting the rs1801133 data from genetic testing for their patients. Such information is also important for the public that increasingly receives genetic data from commercial services without interpretation of its clinical relevance.

genetics↗

bioRxiv2017

An Algorithm for Cellular Reprogramming

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newtons laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.\n\nSignificance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

bioinformatics↗

bioRxiv2017

Integrative analysis and refined design of CRISPR knockout screens

Genome-wide CRISPR-Cas9 screen has been widely used to interrogate gene functions. However, the analysis remains challenging and rules to design better libraries beg further refinement. Here we present MAGeCK-NEST, which integrates protein-protein interaction (PPI), improves the inference accuracy when fewer guide-RNAs (sgRNAs) are available, and assesses screen qualities using information on PPI. MAGeCK-NEST also adopts a maximum-likelihood approach to remove sgRNA outliers, which are characterized with higher G-nucleotide counts, especially in regions distal from the PAM motif. Using MAGeCK-NEST, we found that choosing non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by sgRNAs targeting the \"safe harbor\" regions. Custom-designed screens confirmed our findings, and further revealed that 19nt sgRNAs consistently gave the best signal-to-noise separation. Collectively, our method enabled robust calling of CRISPR screen hits and motivated the design of an improved genome-wide CRISPR screen library.

bioinformatics↗

bioRxiv2016

High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization

Atlases provide a framework for information from diverse sources to be spatially mapped and integrated into a common reference space. In particular, brain atlases allow regional annotation of gene expression, cell morphology, connectivity and activity. In larval zebrafish, advances in genetics, imaging and computational methods have enabled the collection of large datasets providing such information on a whole-brain scale. However, datasets from different sources may not be aligned to the same spatial coordinate system, because technical considerations may necessitate use of different reference templates. Two recent brain atlases for larval zebrafish exemplify this problem. The Z-Brain atlas contains information on gene expression, neural activity and neuroanatomical segmentation acquired using immunohistochemical staining of fixed tissue. In contrast, the Zebrafish Brain Browser (ZBB) atlas was constructed from live scans of fluorescent reporter genes in transgenic larvae. Although different reference brains were used, the two atlases included several transgene patterns in common that provided potential 'bridges' for transforming each into the others coordinate space. We therefore tested multiple bridging channels and registration algorithms. The symmetric diffeomorphic normalization (SyN) algorithm in ANTs improved the precision of live brain registration while better preserving cell morphology than the previously used B-spline elastic registration algorithm. SyN could also be calibrated to correct for tissue distortion introduced during fixation and permeabilization. Finally, multi-reference channel optimization provided a transformation matrix that enabled Z-Brain and ZBB to be co-aligned with acceptable precision and minimal perturbation of cell and tissue morphology. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate acquisition protocols and reference templates, when sufficient information is present for bridging.\n\nAnatomical abbreviations

neuroscience↗