Generative rules of Drosophila locomotor behavior as a candidate homology across phyla

The discovery of shared behavioral processes across phyla is a significant step in the establishment of a comparative study of behavior. We use immobility as an origin and reference for the measurement of fly locomotor behavior; speed, walking direction and trunk orientation as the degrees of freedom shaping this behavior; and cocaine as the parameter inducing progressive transitions in and out of immobility. We characterize and quantify the generative rules that shape Drosophila locomotor behavior, bringing about a gradual buildup of kinematic degrees of freedom during the transition from immobility to normal behavior, and the opposite narrowing down into immobility. Transitions into immobility unfold via sequential enhancement and then elimination of translation, curvature and finally rotation. Transitions out of immobility unfold by progressive addition of these degrees of freedom in the opposite order. The same generative rules have been found in vertebrate locomotor behavior in several contexts (pharmacological manipulations, ontogeny, social interactions) involving transitions in-and-out of immobility. Recent claims for deep homology between arthropod central complex and vertebrate basal ganglia provide an opportunity to examine whether the rules we report also share common descent. Our approach prompts the discovery of behavioral homologies, contributing to the elusive problem of behavioral evolution

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

Identifying compositionally homogeneous and nonhomogeneous domains within the human genome using a novel segmentation algorithm

It has been suggested that the mammalian genome is composed mainly of long compositionally homogeneous domains. Such domains are frequently identified using recursive segmentation algorithms based on the Jensen–Shannon divergence. However, a common difficulty with such methods is deciding when to halt the recursive partitioning and what criteria to use in deciding whether a detected boundary between two segments is real or not. We demonstrate that commonly used halting criteria are intrinsically biased, and propose IsoPlotter, a parameter-free segmentation algorithm that overcomes such biases by using a simple dynamic halting criterion and tests the homogeneity of the inferred domains. IsoPlotter was compared with an alternative segmentation algorithm, DJS, using two sets of simulated genomic sequences. Our results show that IsoPlotter was able to infer both long and short compositionally homogeneous domains with low GC content dispersion, whereas DJS failed to identify short compositionally homogeneous domains and sequences with low compositional dispersion. By segmenting the human genome with IsoPlotter, we found that one-third of the genome is composed of compositionally nonhomogeneous domains and the remaining is a mixture of many short compositionally homogeneous domains and relatively few long ones

A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species

RNA-Seq has emerged as a revolutionary technology for transcriptome analysis. In this article, we report a systematic comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. On a panel of human/chimpanzee/rhesus cerebellum RNA samples previously examined by the high-density human exon junction array (HJAY) and real-time qPCR, we generated 48.68 million RNA-Seq reads. Our results indicate that RNA-Seq has significantly improved gene coverage and increased sensitivity for differentially expressed genes compared with the high-density HJAY array. Meanwhile, we observed a systematic increase in the RNA-Seq error rate for lowly expressed genes. Specifically, between-species DEGs detected by array/qPCR but missed by RNA-Seq were characterized by relatively low expression levels, as indicated by lower RNA-Seq read counts, lower HJAY array expression indices and higher qPCR raw cycle threshold values. Furthermore, this issue was not unique to between-species comparisons of gene expression. In the RNA-Seq analysis of MicroArray Quality Control human reference RNA samples with extensive qPCR data, we also observed an increase in both the false-negative rate and the false-positive rate for lowly expressed genes. These findings have important implications for the design and data interpretation of RNA-Seq studies on gene expression differences between and within species

A set of miRNAs that involve in the pathways of drug resistance and leukemic stem-cell differentiation is associated with the risk of relapse and glucocorticoid response in childhood ALL

Relapse is a major challenge in the successful treatment of childhood acute lymphoblastic leukemia (ALL). Despite intensive research efforts, the mechanisms of ALL relapse are still not fully understood. An understanding of the molecular mechanisms underlying treatment outcome, therapy response and the biology of relapse is required. In this study, we carried out a genome-wide microRNA (miRNA) microarray analysis to determine the miRNA expression profiles and relapse-associated miRNA patterns in a panel of matched diagnosis–relapse or diagnosis–complete remission (CR) childhood ALL samples. A set of miRNAs differentially expressed either in relapsed patients or at diagnosis compared with CR was further validated by quantitative real-time polymerase chain reaction in an independent sample set. Analysis of the predicted functions of target genes based on gene ontology ‘biological process’ categories revealed that the abnormally expressed miRNAs are associated with oncogenesis, classical multidrug resistance pathways and leukemic stem cell self-renewal and differentiation pathways. Several targets of the miRNAs associated with ALL relapse were experimentally validated, including FOXO3, BMI1 and E2F1. We further investigated the association of these dysregulated miRNAs with clinical outcome and confirmed significant associations for miR-708, miR-223 and miR-27a with individual relapse-free survival. Notably, miR-708 was also found to be associated with the in vivo glucocorticoid therapy response and with disease risk stratification. These miRNAs and their targets might be used to optimize anti-leukemic therapy, and serve as novel targets for development of new countermeasures of leukemia. This fundamental study may also contribute to establish the mechanisms of relapse in other cancers

The influence of tumor size and environment on gene expression in commonly used human tumor lines

BACKGROUND: The expression profiles of solid tumor models in rodents have been only minimally studied despite their extensive use to develop anticancer agents. We have applied RNA expression profiling using Affymetrix U95A GeneChips to address fundamental biological questions about human tumor lines. METHODS: To determine whether gene expression changed significantly as a tumor increased in size, we analyzed samples from two human colon carcinoma lines (Colo205 and HCT-116) at three different sizes (200 mg, 500 mg and 1000 mg). To investigate whether gene expression was influenced by the strain of mouse, tumor samples isolated from C.B-17 SCID and Nu/Nu mice were also compared. Finally, the gene expression differences between tissue culture and in vivo samples were investigated by comparing profiles from lines grown in both environments. RESULTS: Multidimensional scaling and analysis of variance demonstrated that the tumor lines were dramatically different from each other and that gene expression remained constant as the tumors increased in size. Statistical analysis revealed that 63 genes were differentially expressed due to the strain of mouse the tumor was grown in but the function of the encoded proteins did not link to any distinct biological pathways. Hierarchical clustering of tissue culture and xenograft samples demonstrated that for each individual tumor line, the in vivo and in vitro profiles were more similar to each other than any other profile. We identified 36 genes with a pattern of high expression in xenograft samples that encoded proteins involved in extracellular matrix, cell surface receptors and transcription factors. An additional 17 genes were identified with a pattern of high expression in tissue culture samples and encoded proteins involved in cell division, cell cycle and RNA production. CONCLUSIONS: The environment a tumor line is grown in can have a significant effect on gene expression but tumor size has little or no effect for subcutaneously grown solid tumors. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments. This could be used as a quality control tool for preclinical oncology studies

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways

<p>Abstract</p> <p>Background</p> <p>An estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies.</p> <p>Methods</p> <p>Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.</p> <p>Results</p> <p>We have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patient's prognosis.</p> <p>Conclusions</p> <p>This study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.</p

Gene Expression Profiles in Stage I Uterine Serous Carcinoma in Comparison to Grade 3 and Grade 1 Stage I Endometrioid Adenocarcinoma

Endometrial cancer is the most common gynecologic malignancy in the developed countries. Clinical studies have shown that early stage uterine serous carcinoma (USC) has outcomes similar to early stage high grade endometrioid adenocarcinoma (EAC-G3) than to early stage low grade endometrioid adenocarcinoma (EAC-G1). However, little is known about the origin of these different clinical outcomes. This study applied the whole genome expression profiling to explore the expression difference of stage I USC (n = 11) relative to stage I EAC-G3 (n = 11) and stage I EAC-G1 (n = 11), respectively.We found that the expression difference between USC and EAC-G3, as measured by the number of differentially expressed genes (DEGs), is consistently less than that found between USC and EAC-G1. Pathway enrichment analyses suggested that DEGs specific to USC vs. EAC-G3 are enriched for genes involved in signaling transduction, while DEGs specific to USC vs. EAC-G1 are enriched for genes involved in cell cycle. Gene expression differences for selected DEGs are confirmed by quantitative RT-PCR with a high validation rate.This data, although preliminary, indicates that stage I USC is genetically similar to stage I EAC-G3 compared to stage I EAC-G1. DEGs identified from this study might provide an insight in to the potential mechanisms that influence the clinical outcome differences between endometrial cancer subtypes. They might also have potential prognostic and therapeutic impacts on patients diagnosed with uterine cancer