Parsing parallel evolution:Ecological divergence and differential gene expression in the adaptive radiations of thick-lipped Midas cichlid fishes from Nicaragua

The study of parallel evolution facilitates the discovery of common rules of diversification. Here, we examine the repeated evolution of thick lips in Midas cichlid fishes (the Amphilophus citrinellus species complex) - from two Great Lakes and two crater lakes in Nicaragua - to assess whether similar changes in ecology, phenotypic trophic traits and gene expression accompany parallel trait evolution. Using next-generation sequencing technology, we characterize transcriptome-wide differential gene expression in the lips of wild-caught sympatric thick- and thin-lipped cichlids from all four instances of repeated thick-lip evolution. Six genes (apolipoprotein D, myelin-associated glycoprotein precursor, four-and-a-half LIM domain protein 2, calpain-9, GTPase IMAP family member 8-like and one hypothetical protein) are significantly underexpressed in the thick-lipped morph across all four lakes. However, other aspects of lips' gene expression in sympatric morphs differ in a lake-specific pattern, including the magnitude of differentially expressed genes (97-510). Generally, fewer genes are differentially expressed among morphs in the younger crater lakes than in those from the older Great Lakes. Body shape, lower pharyngeal jaw size and shape, and stable isotopes (dC and dN) differ between all sympatric morphs, with the greatest differentiation in the Great Lake Nicaragua. Some ecological traits evolve in parallel (those related to foraging ecology; e.g. lip size, body and head shape) but others, somewhat surprisingly, do not (those related to diet and food processing; e.g. jaw size and shape, stable isotopes). Taken together, this case of parallelism among thick- and thin-lipped cichlids shows a mosaic pattern of parallel and nonparallel evolution. © 2012 Blackwell Publishing Ltd.

MTERF1 Binds mtDNA to Prevent Transcriptional Interference at the Light-Strand Promoter but Is Dispensable for rRNA Gene Transcription Regulation

Mitochondrial transcription termination factor 1, MTERF1, has been reported to couple rRNA gene transcription initiation with termination and is therefore thought to be a key regulator of mammalian mitochondrial ribosome biogenesis. The prevailing model is based on a series of observations published over the last two decades, but no in vivo evidence exists to show that MTERF1 regulates transcription of the heavy-strand region of mtDNA containing the rRNA genes. Here, we demonstrate that knockout of Mterf1 in mice has no effect on mitochondrial rRNA levels or mitochondrial translation. Instead, loss of Mterf1 influences transcription initiation at the light-strand promoter, resulting in a decrease of de novo transcription manifested as reduced 7S RNA levels. Based on these observations, we suggest that MTERF1 does not regulate heavy-strand transcription, but rather acts to block transcription on the opposite strand of mtDNA to prevent transcription interference at the light-strand promoter.

Oncofetal gene SALL4 in aggressive hepatocellular carcinoma

Hepatocellular carcinoma is the third leading cause of cancer-related deaths worldwide. In the heterogeneous group of hepatocellular carcinomas, those with characteristics of embryonic stem-cell and progenitor-cell gene expression are associated with the worst prognosis. The oncofetal gene SALL4, a marker of a subtype of hepatocellular carcinoma with progenitor-like features, is associated with a poor prognosis and is a potential target for treatment.

Sequential expression of putative stem cell markers in gastric carcinogenesis

Gastric carcinogenesis has been well documented in the step-wise histopathological model, known as Correa pathway. Several biomarkers including CD44, Musashi-1 and CD133 have been reported as putative stem cell (PSC) markers.

Elevated NRD1 metalloprotease expression plays a role in breast cancer growth and proliferation

Understanding the molecular etiology of cancer and increasing the number of drugs and their targets are critical to cancer management. In our attempt to unravel novel breast-cancer associated proteins, we previously conducted protein expression profiling of the MCF10AT model, which comprises a series of isogenic cell lines that mimic different stages of breast cancer progression. NRD1 expression was found to increase during breast cancer progression. Here, we attempted to confirm the relevance of NRD1 in clinical breast cancer and understand the functional role and mechanism of NRD1 in breast cancer cells. Immunohistochemistry data show that NRD1 expression was elevated in ductal carcinoma in situ and invasive ductal carcinomas compared with normal tissues in 30% of the 26 matched cases studied. Examination of NRD1 expression in tissue microarray comprising >100 carcinomas and subsequent correlation with clinical data revealed that NRD1 expression was significantly associated with tumor size, grade, and nodal status (P <0.05). Silencing of NRD1 reduced MCF10CA1h and MDA-MD-231 breast-cancer-cell proliferation and growth. Probing the oncogenic EGF signaling pathways revealed that NRD1 knock down did not affect overall downstream tyrosine phosphorylation cascades including AKT and MAPK activation. Instead, silencing of NRD1 resulted in a reduction of overall cyclin D1 expression, a reduction of EGF-induced increase in cyclin D1 expression and an increase in apoptotic cell population compared with control cells.

Effects of oxidized and glycated LDL on gene expression in human retinal capillary pericytes

Modified (oxidized and/or glycated) low-density lipoproteins (LDLs) have been implicated in retinal pericyte loss, one of the major pathologic features of early-stage diabetic retinopathy. To delineate underlying molecular mechanisms, the present study was designed to explore the global effects of modified LDL on pericyte gene expression.

TGFβ and CCN2/CTGF mediate actin related gene expression by differential E2F1/CREB activation

CCN2/CTGF is an established effector of TGFß driven responses in diabetic nephropathy. We have identified an interaction between CCN2 and TGFß leading to altered phenotypic differentiation and inhibited cellular migration. Here we determine the gene expression profile associated with this phenotype and define a transcriptional basis for differential actin related gene expression and cytoskeletal function.

Identification of Gene Expression-Based Prognostic Markers in the Hematopoietic Stem Cells of Patients With Myelodysplastic Syndromes

The diagnosis of patients with myelodysplastic syndromes (MDS) is largely dependent on morphologic examination of bone marrow aspirates. Several criteria that form the basis of the classifications and scoring systems most commonly used in clinical practice are affected by operator-dependent variation. To identify standardized molecular markers that would allow prediction of prognosis, we have used gene expression profiling (GEP) data on CD34+ cells from patients with MDS to determine the relationship between gene expression levels and prognosis.

Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

DNA methyltransferase expression in the mouse germ line during periods of de novo methylation

DNA methyltransferase (DNMT) 3A and DNMT3B are both active de novo DNA methyltransferases required for development, whereas DNMT3L, which has no demonstrable methyltransferase activity, is required for methylation of imprinted genes in the oocyte. We show here that different mechanisms are used to restrict access by these proteins to their targets during germ cell development. Transcriptional control of the Dnmt3l promoter guarantees that message is low or absent except during periods of de novo activity. Use of an alternative promoter at the Dnmt3a locus produces the shorter Dnmt3a2 transcript in the germ line and postimplantation embryo only, whereas alternative splicing of the Dnmt3b transcript ensures that Dnmt3b1 is absent in the male prospermatogonia. Control of subcellular protein localization is a common theme for DNMT3A and DNMT3B, as proteins were seen in the nucleus only when methylation was occurring. These mechanisms converge to ensure that the only time that functional products from each locus are present in the germ cell nuclei is around embryonic day 17.5 in males and after birth in the growing oocytes in females.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

Background: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take >2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.

Results: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.

Conclusion: Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.