Region-Based Analysis of Rare Genomic Variants in Whole-Genome Sequencing Datasets Reveal Two Novel Alzheimer’s Disease-Associated Genes: \u3cem\u3eDTNB\u3c/em\u3e and \u3cem\u3eDLG2\u3c/em\u3e

Alzheimer’s disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency \u3c0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values \u3c10−6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (pmeta= 4.74 × 10−8 ). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (pmeta = 1 × 10−6 ). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants

Ten Simple Rules for Organizing a Virtual Conference—Anywhere

Etienne P. de Villiers and Sheila C. Ommeh are ILRI author

Comparison of Illumina and 454 Deep Sequencing in Participants Failing Raltegravir-Based Antiretroviral Therapy

Background: The impact of raltegravir-resistant HIV-1 minority variants (MVs) on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs. Methods: A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naïve patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser. Results: Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P<0.001). Illumina sequencing detected 2.4-fold greater nucleotide MVs and 2.9-fold greater amino acid MVs compared to 454. The only raltegravir-resistant MV detected was an E138K mutation in one participant by Illumina sequencing, but not by 454. Conclusions: In participants of A5262 with raltegravir resistance at virologic failure, baseline raltegravir-resistant MVs were rarely detected. At comparable costs to 454 sequencing, Illumina demonstrated greater depth of coverage, increased sensitivity for detecting HIV MVs, and fewer false positive variant calls

Novel Developmental Analyses Identify Longitudinal Patterns of Early Gut Microbiota that Affect Infant Growth

It is acknowledged that some obesity trajectories are set early in life, and that rapid weight gain in infancy is a risk factor for later development of obesity. Identifying modifiable factors associated with early rapid weight gain is a prerequisite for curtailing the growing worldwide obesity epidemic. Recently, much attention has been given to findings indicating that gut microbiota may play a role in obesity development. We aim at identifying how the development of early gut microbiota is associated with expected infant growth. We developed a novel procedure that allows for the identification of longitudinal gut microbiota patterns (corresponding to the gut ecosystem developing), which are associated with an outcome of interest, while appropriately controlling for the false discovery rate. Our method identified developmental pathways of Staphylococcus species and Escherichia coli that were associated with expected growth, and traditional methods indicated that the detection of Bacteroides species at day 30 was associated with growth. Our method should have wide future applicability for studying gut microbiota, and is particularly important for translational considerations, as it is critical to understand the timing of microbiome transitions prior to attempting to manipulate gut microbiota in early life

A Novel and Critical Role for Oct4 as a Regulator of the Maternal-Embryonic Transition

Compared to the emerging embryonic stem cell (ESC) gene network, little is known about the dynamic gene network that directs reprogramming in the early embryo. We hypothesized that Oct4, an ESC pluripotency regulator that is also highly expressed at the 1- to 2-cell stages in embryos, may be a critical regulator of the earliest gene network in the embryo.Using antisense morpholino oligonucleotide (MO)-mediated gene knockdown, we show that Oct4 is required for development prior to the blastocyst stage. Specifically, Oct4 has a novel and critical role in regulating genes that encode transcriptional and post-transcriptional regulators as early as the 2-cell stage. Our data suggest that the key function of Oct4 may be to switch the developmental program from one that is predominantly regulated by post-transcriptional control to one that depends on the transcriptional network. Further, we propose to rank candidate genes quantitatively based on the inter-embryo variation in their differential expression in response to Oct4 knockdown. Of over 30 genes analyzed according to this proposed paradigm, Rest and Mta2, both of which have established pluripotency functions in ESCs, were found to be the most tightly regulated by Oct4 at the 2-cell stage.We show that the Oct4-regulated gene set at the 1- to 2-cell stages of early embryo development is large and distinct from its established network in ESCs. Further, our experimental approach can be applied to dissect the gene regulatory network of Oct4 and other pluripotency regulators to deconstruct the dynamic developmental program in the early embryo

Population Differences in Transcript-Regulator Expression Quantitative Trait Loci

Gene expression quantitative trait loci (eQTL) are useful for identifying single nucleotide polymorphisms (SNPs) associated with diseases. At times, a genetic variant may be associated with a master regulator involved in the manifestation of a disease. The downstream target genes of the master regulator are typically co-expressed and share biological function. Therefore, it is practical to screen for eQTLs by identifying SNPs associated with the targets of a transcript-regulator (TR). We used a multivariate regression with the gene expression of known targets of TRs and SNPs to identify TReQTLs in European (CEU) and African (YRI) HapMap populations. A nominal p-value of <1×10−6 revealed 234 SNPs in CEU and 154 in YRI as TReQTLs. These represent 36 independent (tag) SNPs in CEU and 39 in YRI affecting the downstream targets of 25 and 36 TRs respectively. At a false discovery rate (FDR) = 45%, one cis-acting tag SNP (within 1 kb of a gene) in each population was identified as a TReQTL. In CEU, the SNP (rs16858621) in Pcnxl2 was found to be associated with the genes regulated by CREM whereas in YRI, the SNP (rs16909324) was linked to the targets of miRNA hsa-miR-125a. To infer the pathways that regulate expression, we ranked TReQTLs by connectivity within the structure of biological process subtrees. One TReQTL SNP (rs3790904) in CEU maps to Lphn2 and is associated (nominal p-value = 8.1×10−7) with the targets of the X-linked breast cancer suppressor Foxp3. The structure of the biological process subtree and a gene interaction network of the TReQTL revealed that tumor necrosis factor, NF-kappaB and variants in G-protein coupled receptors signaling may play a central role as communicators in Foxp3 functional regulation. The potential pleiotropic effect of the Foxp3 TReQTLs was gleaned from integrating mRNA-Seq data and SNP-set enrichment into the analysis

Clues to Neuro-Degeneration in Niemann-Pick Type C Disease from Global Gene Expression Profiling

BACKGROUND: Niemann-Pick Type C (NPC) disease is a neurodegenerative disease that is characterized by the accumulation of cholesterol and glycosphingolipids in the late endocytic pathway. The majority of NPC cases are due to mutations in the NPC1 gene. The precise function of this gene is not yet known. METHODOLOGY/PRINCIPAL FINDINGS: Using cDNA microarrays, we analyzed the genome-wide expression patterns of human fibroblasts homozygous for the I1061T NPC1 mutation that is characterized by a severe defect in the intracellular processing of low density lipoprotein-derived cholesterol. A distinct gene expression profile was identified in NPC fibroblasts from different individuals when compared with fibroblasts isolated from normal subjects. As expected, NPC1 mutant cells displayed an inappropriate homeostatic response to accumulated intracellular cholesterol. In addition, a number of striking parallels were observed between NPC disease and Alzheimer's disease. CONCLUSIONS/SIGNIFICANCE: Many genes involved in the trafficking and processing of amyloid precursor protein and the microtubule binding protein, tau, were more highly expressed. Numerous genes important for membrane traffic and the cellular regulation of calcium, metals and other ions were upregulated. Finally, NPC fibroblasts exhibited a gene expression profile indicative of oxidative stress. These changes are likely contributors to the pathophysiology of Niemann-Pick Type C disease

Iterative sorting reveals CD133+ and CD133- melanoma cells as phenotypically distinct populations

Background: The heterogeneity and tumourigenicity of metastatic melanoma is attributed to a cancer stem cell model, with CD133 considered to be a cancer stem cell marker in melanoma as well as other tumours, but its role has remained controversial. Methods: We iteratively sorted CD133+ and CD133- cells from 3 metastatic melanoma cell lines, and observed tumourigenicity and phenotypic characteristics over 7 generations of serial xeno-transplantation in NOD/SCID mice. Results: We demonstrate that iterative sorting is required to make highly pure populations of CD133+ and CD133- cells from metastatic melanoma, and that these two populations have distinct characteristics not related to the cancer stem cell phenotype. In vitro, gene set enrichment analysis indicated CD133+ cells were related to a proliferative phenotype, whereas CD133- cells were of an invasive phenotype. However, in vivo, serial transplantation of CD133+ and CD133- tumours over 7 generations showed that both populations were equally able to initiate and propagate tumours. Despite this, both populations remained phenotypically distinct, with CD133- cells only able to express CD133 in vivo and not in vitro. Loss of CD133 from the surface of a CD133+ cell was observed in vitro and in vivo, however CD133- cells derived from CD133+ retained the CD133+ phenotype, even in the presence of signals from the tumour microenvironment. Conclusion: We show for the first time the necessity of iterative sorting to isolate pure marker-positive and marker-negative populations for comparative studies, and present evidence that despite CD133+ and CD133- cells being equally tumourigenic, they display distinct phenotypic differences, suggesting CD133 may define a distinct lineage in melanoma

Meta-Analysis of the Alzheimer\u27s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer\u27s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington\u27s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies