Avirulent UG10 Entamoeba histolytica mutant derived from HM-1:IMSS strain shows limited genome variability and aberrant 5-methyl cytosine genomic distribution.

Entamoeba histolytica, an intestinal parasite of global significance, poses substantial health risks with its associated high morbidity and mortality rates. Despite the current repertoire of molecular tools for the study of gene function in, the regulatory mechanisms governing its pathogenicity remain largely unexplored. This knowledge gap underscores the need to elucidate key genetic determinants orchestrating cellular functions critical to its virulence. Previously, our group generated an avirulent strain, termed UG10, with the same genetic background as the HM1:IMSS strain. UG10 strain, despite showing normal expression levels of well-known virulence factors, was unable to perform in-vitro and in-vivo activities related to amoebic virulence. In this study, we aimed to uncover the genome-wide modifications that rendered the avirulent phenotype of the UG10 strain through whole-genome sequencing. As a complementary approach, we conducted Methylated DNA Immunoprecipitation coupled with sequencing (MeDIP-seq) analysis on both the highly virulent HM1:IMSS strain and the low-virulence UG10 strain to uncover the genome-wide methylation profile. These dual methodologies revealed two aspects of the UG10 avirulent strain. One is the random integration of fragments from the ribosomal gene cluster and tRNA genes, ranging from 120 to 400 bp; and secondly, a clear, enriched methylation profile in the coding and non-coding strand relative to the start codon sequence in genes encoding small GTPases, which is associated with the previously described avirulent phenotype. This study provides the foundation to explore other genetic and epigenetic regulatory circuitries in E. histolytica and novel targets to understand the pathogenic mechanism of this parasite.

Effect of statin treatment in obese selenium-supplemented mice lacking selenocysteine lyase.

People with obesity are often dyslipidemic and prescribed statins to prevent cardiovascular events. A common side effect of statin use is myopathy. This could potentially be caused by the reduction of selenoproteins that curb oxidative stress, in turn, affecting creatine metabolism. We determined if statins regulate hepatic and muscular selenoprotein expression, oxidative stress and creatine metabolism. Mice lacking selenocysteine lyase (Scly KO), a selenium-provider enzyme for selenoprotein synthesis, were fed a high-fat, Se-supplemented diet and treated with simvastatin. Statin improved creatine metabolism in females and oxidative responses in both sexes. Male Scly KO mice were heavier than females after statin treatment. Hepatic selenoproteins were unaffected by statin and genotype in females. Statin upregulated muscular Gpx1 in females but not males, while Scly loss downregulated muscular Gpx1 in males and Selenon in females. Osgin1 was reduced in statin-treated Scly KO males after AmpliSeq analysis. These results refine our understanding of the sex-dependent role of selenium in statin responses.

Epigenetic Delay in the Neurodevelopmental Trajectory of DNA Methylation States in Autism Spectrum Disorders.

Autism spectrum disorders (ASD) are hypothesized to originate in utero from perturbations in neural stem cell niche regions of the developing brain. Dynamic epigenetic processes including DNA methylation are integral to coordinating typical brain development. However, the extent and consequences of alterations to DNA methylation states in neural stem cell compartments in ASD are unknown. Here, we report significant DNA methylation defects in the subventricular zone of the lateral ventricles from postmortem brain of 17 autism diagnosed compared to 17 age- and gender-matched typically developing individuals. Both array- and sequencing-based genome-wide methylome analyses independently revealed that these alterations were preferentially targeted to intragenic and bivalently modified chromatin domains of genes predominately involved in neurodevelopment, which associated with aberrant precursor messenger RNA splicing events of ASD-relevant genes. Integrative analysis of our ASD and typically developing postmortem brain methylome datasets with that from fetal brain at different neurodevelopmental stages revealed that the methylation states of differentially methylated loci associated with ASD remarkably resemble the methylation states at earlier time points in fetal brain development. This observation was confirmed using additional methylome datasets from three other brain regions. Altogether, these findings implicate an epigenetic delay in the trajectory of normal DNA methylation states during the course of brain development that may consequently lead to deleterious transcriptomic events in ASD and support the hypothesis of an early developmental origin of ASD.

Comparative DNA methylomic analyses reveal potential origins of novel epigenetic biomarkers of insulin resistance in monocytes from virally suppressed HIV-infected adults.

BACKGROUND: Compared to healthy individuals, those with stably repressed HIV experience a higher risk of developing insulin resistance, a hallmark of pre-diabetes and a major determinant for cardiometabolic diseases. Although epigenetic processes, including in particular DNA methylation, appear to be dysregulated in individuals with insulin resistance, little is known about where these occur in the genomes of immune cells and the origins of these alterations in HIV-infected individuals. Here, we examined the genome-wide DNA methylation states of monocytes in HIV-infected individuals (n = 37) with varying levels of insulin sensitivity measured by the homeostatic model assessment of insulin resistance (HOMA-IR). RESULTS: By profiling DNA methylation at single-nucleotide resolution using the Illumina Infinium HumanMethylation450 BeadChip in monocytes from insulin-resistant (IR; HOMA-IR ≥ 2.0; n = 14) and insulin-sensitive (IS; HOMA-IR < 2.0; n = 23) individuals, we identified 123 CpGs with significantly different DNA methylation levels. These CpGs were enriched at genes involved in pathways relating to glucose metabolism, immune activation, and insulin-relevant signaling, with the majority (86.2%) being hypomethylated in IR relative to IS individuals. Using a stepwise multiple logistic regression analysis, we observed 4 CpGs (cg27655935, cg02000426, cg10184328, and cg23085143) whose methylation levels independently predicted the insulin-resistant state at a higher confidence than that of clinical risk factors typically associated with insulin resistance (i.e., fasting glucose, 120-min oral glucose tolerance test, Framingham Risk Score, and Total to HDL cholesterol ratio). Interestingly, 79 of the 123 CpGs (64%) exhibited remarkably similar levels of methylation as that of hematopoietic stem cells (HSC) in monocytes from IR individuals, implicating epigenetic defects in myeloid differentiation as a possible origin for the methylation landscape underlying the insulin resistance phenotype. In support of this, gene ontology analysis of these 79 CpGs revealed overrepresentation of these CpGs at genes relevant to HSC function, including involvement in stem cell pluripotency, differentiation, and Wnt signaling pathways. CONCLUSION: Altogether, our data suggests a possible role for DNA methylation in regulating monocyte activity that may associate with the insulin-resistant phenotype. The methylomic landscape of insulin resistance in monocytes could originate from epigenetic dysregulation during HSC differentiation through the myeloid lineage. Understanding the factors involved with changes in the myeloid trajectory may provide further insight into the development of insulin resistance. Furthermore, regulation of specific genes that were implicated in our analysis reveal possible targets for modulating immune activity to ameliorate insulin resistance.

Genetic Determinants of Drug Resistance in Mycobacterium tuberculosis and Their Diagnostic Value.

RATIONALE: The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance-conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs. OBJECTIVES: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance. METHODS: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool. MEASUREMENTS AND MAIN RESULTS: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci. CONCLUSIONS: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.

Whole-exome sequencing reveals the mutational spectrum of testicular germ cell tumours.

Testicular germ cell tumours (TGCTs) are the most common cancer in young men. Here we perform whole-exome sequencing (WES) of 42 TGCTs to comprehensively study the cancer's mutational profile. The mutation rate is uniformly low in all of the tumours (mean 0.5 mutations per Mb) as compared with common cancers, consistent with the embryological origin of TGCT. In addition to expected copy number gain of chromosome 12p and mutation of KIT, we identify recurrent mutations in the tumour suppressor gene CDC27 (11.9%). Copy number analysis reveals recurring amplification of the spermatocyte development gene FSIP2 (15.3%) and a 0.4 Mb region at Xq28 (15.3%). Two treatment-refractory patients are shown to harbour XRCC2 mutations, a gene strongly implicated in defining cisplatin resistance. Our findings provide further insights into genes involved in the development and progression of TGCT.

Multi-stage genome-wide association study identifies new susceptibility locus for testicular germ cell tumour on chromosome 3q25.

Recent genome-wide association studies (GWAS) and subsequent meta-analyses have identified over 25 SNPs at 18 loci, together accounting for >15% of the genetic susceptibility to testicular germ cell tumour (TGCT). To identify further common SNPs associated with TGCT, here we report a three-stage experiment, involving 4098 cases and 18 972 controls. Stage 1 comprised previously published GWAS analysis of 307 291 SNPs in 986 cases and 4946 controls. In Stage 2, we used previously published customised Illumina iSelect genotyping array (iCOGs) data across 694 SNPs in 1064 cases and 10 082 controls. Here, we report new genotyping of eight SNPs showing some evidence of association in combined analysis of Stage 1 and Stage 2 in an additional 2048 cases of TGCT and 3944 controls (Stage 3). Through fixed-effects meta-analysis across three stages, we identified a novel locus at 3q25.31 (rs1510272) demonstrating association with TGCT [per-allele odds ratio (OR) = 1.16, 95% confidence interval (CI) = 1.06-1.27; P = 1.2 × 10(-9)].

Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis.

M. tuberculosis is evolving antibiotic resistance, threatening attempts at tuberculosis epidemic control. Mechanisms of resistance, including genetic changes favored by selection in resistant isolates, are incompletely understood. Using 116 newly sequenced and 7 previously sequenced M. tuberculosis whole genomes, we identified genome-wide signatures of positive selection specific to the 47 drug-resistant strains. By searching for convergent evolution--the independent fixation of mutations in the same nucleotide position or gene--we recovered 100% of a set of known resistance markers. We also found evidence of positive selection in an additional 39 genomic regions in resistant isolates. These regions encode components in cell wall biosynthesis, transcriptional regulation and DNA repair pathways. Mutations in these regions could directly confer resistance or compensate for fitness costs associated with resistance. Functional genetic analysis of mutations in one gene, ponA1, demonstrated an in vitro growth advantage in the presence of the drug rifampicin.

Aminoglycoside cross-resistance in Mycobacterium tuberculosis due to mutations in the 5' untranslated region of whiB7.

Since the discovery of streptomycin's bactericidal activity against Mycobacterium tuberculosis, aminoglycosides have been utilized to treat tuberculosis (TB). Today, the aminoglycosides kanamycin and amikacin are used to treat multidrug-resistant (MDR) TB, and resistance to any of the second-line injectable antibiotics, including kanamycin, amikacin, or capreomycin, is a defining characteristic of extensively drug-resistant (XDR) TB. Resistance to kanamycin and streptomycin is thought to be due to the acquisition of unlinked chromosomal mutations. However, we identified eight independent mutations in the 5' untranslated region of the transcriptional activator whiB7 that confer low-level resistance to both aminoglycosides. The mutations lead to 23- to 145-fold increases in whiB7 transcripts and subsequent increased expression of both eis (Rv2416c) and tap (Rv1258c). Increased expression of eis confers kanamycin resistance in these mutants, while increased expression of tap, which encodes an efflux pump, is a previously uncharacterized mechanism of low-level streptomycin resistance. Additionally, high-level resistance to streptomycin arose at a much higher frequency in whiB7 mutants than in a wild-type (WT) strain. Although whiB7 is typically associated with intrinsic antibiotic resistance in M. tuberculosis, these data suggest that mutations in an uncharacterized regulatory region of whiB7 contribute to cross-resistance against clinically used second-line antibiotics. As drug resistance continues to develop and spread, understanding the mechanisms and molecular basis of antibiotic resistance is critical for the development of rapid molecular tests to diagnose drug-resistant TB strains and ultimately for designing regimens to treat drug-resistant cases of TB.

Estimation of sequencing error rates in short reads.

BACKGROUND: Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments. RESULTS: We developed a fast, scalable and accurate approach to estimating error rates in short reads, which has the added advantage of not requiring a reference genome. We build on the fundamental observation that there is a linear relationship between the copy number for a given read and the number of erroneous reads that differ from the read of interest by one or two bases. The slope of this relationship can be transformed to give an estimate of the error rate, both by read and by position. We present simulation studies as well as analyses of real data sets illustrating the precision and accuracy of this method, and we show that it is more accurate than alternatives that count the difference between the sample of interest and a reference genome. We show how this methodology led to the detection of mutations in the genome of the PhiX strain used for calibration of Illumina data. The proposed method is implemented in an R package, which can be downloaded from http://bcb.dfci.harvard.edu/∼vwang/shadowRegression.html. CONCLUSIONS: The proposed method can be used to monitor the quality of sequencing pipelines at the level of individual experiments without the use of reference genomes. Furthermore, having an estimate of the error rates gives one the opportunity to improve analyses and inferences in many applications of next-generation sequencing data.

nEASE: a method for gene ontology subclassification of high-throughput gene expression data.

UNLABELLED: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. AVAILABILITY: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures.

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a 'basket' feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.

Using the DFCI gene index databases for biological discovery.

The DFCI Gene Index Web pages provide access to analyses of ESTs and gene sequences for nearly 114 species, as well as a number of resources derived from these. Each species-specific database is presented using a common format with a home page. A variety of methods exist that allow users to search each species-specific database. Methods implemented currently include nucleotide or protein sequence queries using WU-BLAST, text-based searches using various sequence identifiers, searches by gene, tissue and library name, and searches using functional classes through Gene Ontology assignments. This protocol provides guidance for using the Gene Index Databases to extract information.

Therapeutic implications of GIPC1 silencing in cancer.

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.

GeneSigDB--a curated database of gene expression signatures.

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.

Functional classification analysis of somatically mutated genes in human breast and colorectal cancers.

A recent study published by Sjoblom and colleagues [T. Sjoblom, S. Jones, L.D. Wood, D.W. Parsons, J. Lin, T.D. Barber, D. Mandelker, R.J. Leary, J. Ptak, N. Silliman, S. Szabo, P. Buckhaults, C. Farrell, P. Meeh, S.D. Markowitz, J. Willis, D. Dawson, J.K. Willson, A.F. Gazdar, J. Hartigan, L. Wu, C. Liu, G. Parmigiani, B.H. Park, K.E. Bachman, N. Papadopoulos, B. Vogelstein, K.W. Kinzler, V.E. Velculescu, The consensus coding sequences of human breast and colorectal cancers. Science 314 (2006) 268-274.] performed comprehensive sequencing of 13,023 human genes and identified mutations in genes specific to breast and colorectal tumors, providing insight into organ-specific tumor biology. Here we present a systematic analysis of the functional classifications of Sjoblom's "CAN" genes, a subset of these validated mutant genes, that identifies novel organ-specific biological themes and molecular pathways associated with disease-specific etiology. This analysis links four somatically mutated genes associated with diverse oncological types to colorectal and breast cancers through established TGF-beta1-regulated interactions, revealing mechanistic differences in these cancers and providing potential diagnostic and therapeutic targets.

Physiogenomic resources for rat models of heart, lung and blood disorders.

Cardiovascular disorders are influenced by genetic and environmental factors. The TIGR rodent expression web-based resource (TREX) contains over 2,200 microarray hybridizations, involving over 800 animals from 18 different rat strains. These strains comprise genetically diverse parental animals and a panel of chromosomal substitution strains derived by introgressing individual chromosomes from normotensive Brown Norway (BN/NHsdMcwi) rats into the background of Dahl salt sensitive (SS/JrHsdMcwi) rats. The profiles document gene-expression changes in both genders, four tissues (heart, lung, liver, kidney) and two environmental conditions (normoxia, hypoxia). This translates into almost 400 high-quality direct comparisons (not including replicates) and over 100,000 pairwise comparisons. As each individual chromosomal substitution strain represents on average less than a 5% change from the parental genome, consomic strains provide a useful mechanism to dissect complex traits and identify causative genes. We performed a variety of data-mining manipulations on the profiles and used complementary physiological data from the PhysGen resource to demonstrate how TREX can be used by the cardiovascular community for hypothesis generation.

Transcriptional profiling with a blood pressure QTL interval-specific oligonucleotide array.

Although the evidence for a genetic predisposition to human essential hypertension is compelling, the genetic control of blood pressure (BP) is poorly understood. The Dahl salt-sensitive (S) rat is a model for studying the genetic component of BP. Using this model, we previously reported the identification of 16 different genomic regions that contain one or more BP quantitative trait loci (QTLs). The proximal region of rat chromosome 1 contains multiple BP QTLs. Of these, we have localized the BP QTL1b region to a 13.5-cM (20.92 Mb) region. Interestingly, five additional independent studies in rats and four independent studies in humans have reported genetic linkage for BP control by regions homologous to QTL1b. To view the overall renal transcriptional topography of the positional candidate genes for this QTL, we sought a comparative gene expression profiling between a congenic strain containing QTL1b and control S rats by employing 1) a saturated QTL1b interval-specific oligonucleotide array and 2) a whole genome cDNA microarray representing 20,465 unique genes that are positioned outside the QTL. Results indicated that 17 of the 231 positional candidate genes for this QTL are differentially expressed between the two strains tested. Surprisingly, >1,500 genes outside of QTL1b were differentially expressed between the two rat strains. Integrating the results from the two approaches revealed at least one complex network of transcriptional control initiated by the positional candidate Nr2f2. This network appears to account for the majority of gene expression differences occurring outside of the QTL interval. Further substitution mapping is currently underway to test the validity of each of these differentially expressed positional candidate genes. These results demonstrate the importance of using a saturated oligonucleotide array for identifying and prioritizing differentially expressed positional candidate genes of a BP QTL.

The institute for genomic research Osa1 rice genome annotation database.

We have developed a rice (Oryza sativa) genome annotation database (Osa1) that provides structural and functional annotation for this emerging model species. Using the sequence of O. sativa subsp. japonica cv Nipponbare from the International Rice Genome Sequencing Project, pseudomolecules, or virtual contigs, of the 12 rice chromosomes were constructed. Our most recent release, version 3, represents our third build of the pseudomolecules and is composed of 98% finished sequence. Genes were identified using a series of computational methods developed for Arabidopsis (Arabidopsis thaliana) that were modified for use with the rice genome. In release 3 of our annotation, we identified 57,915 genes, of which 14,196 are related to transposable elements. Of these 43,719 non-transposable element-related genes, 18,545 (42.4%) were annotated with a putative function, 5,777 (13.2%) were annotated as encoding an expressed protein with no known function, and the remaining 19,397 (44.4%) were annotated as encoding a hypothetical protein. Multiple splice forms (5,873) were detected for 2,538 genes, resulting in a total of 61,250 gene models in the rice genome. We incorporated experimental evidence into 18,252 gene models to improve the quality of the structural annotation. A series of functional data types has been annotated for the rice genome that includes alignment with genetic markers, assignment of gene ontologies, identification of flanking sequence tags, alignment with homologs from related species, and syntenic mapping with other cereal species. All structural and functional annotation data are available through interactive search and display windows as well as through download of flat files. To integrate the data with other genome projects, the annotation data are available through a Distributed Annotation System and a Genome Browser. All data can be obtained through the project Web pages at http://rice.tigr.org.