scholarly journals Comparative brain transcriptomic analyses of scouting across distinct behavioural and ecological contexts in honeybees

2014 ◽  
Vol 281 (1797) ◽  
pp. 20141868 ◽  
Author(s):  
Zhengzheng S. Liang ◽  
Heather R. Mattila ◽  
Sandra L. Rodriguez-Zas ◽  
Bruce R. Southey ◽  
Thomas D. Seeley ◽  
...  

Individual differences in behaviour are often consistent across time and contexts, but it is not clear whether such consistency is reflected at the molecular level. We explored this issue by studying scouting in honeybees in two different behavioural and ecological contexts: finding new sources of floral food resources and finding a new nest site. Brain gene expression profiles in food-source and nest-site scouts showed a significant overlap, despite large expression differences associated with the two different contexts. Class prediction and ‘leave-one-out’ cross-validation analyses revealed that a bee's role as a scout in either context could be predicted with 92.5% success using 89 genes at minimum. We also found that genes related to four neurotransmitter systems were part of a shared brain molecular signature in both types of scouts, and the two types of scouts were more similar for genes related to glutamate and GABA than catecholamine or acetylcholine signalling. These results indicate that consistent behavioural tendencies across different ecological contexts involve a mixture of similarities and differences in brain gene expression.

2004 ◽  
Vol 3 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Minhui Paik ◽  
Yuhong Yang

Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when there is considerable uncertainty in choosing the best candidate classifier. As an alternative to selecting a single “winner," we propose a weighting method to combine the multiple NN rules. Four gene expression data sets are used to compare its performance with CV methods. The results show that when the CV selection is unstable, the combined classifier performs much better.


2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Carl Grant Mangleburg ◽  
Timothy Wu ◽  
Hari K. Yalamanchili ◽  
Caiwei Guo ◽  
Yi-Chen Hsieh ◽  
...  

Abstract Background Tau neurofibrillary tangle pathology characterizes Alzheimer’s disease and other neurodegenerative tauopathies. Brain gene expression profiles can reveal mechanisms; however, few studies have systematically examined both the transcriptome and proteome or differentiated Tau- versus age-dependent changes. Methods Paired, longitudinal RNA-sequencing and mass-spectrometry were performed in a Drosophila model of tauopathy, based on pan-neuronal expression of human wildtype Tau (TauWT) or a mutant form causing frontotemporal dementia (TauR406W). Tau-induced, differentially expressed transcripts and proteins were examined cross-sectionally or using linear regression and adjusting for age. Hierarchical clustering was performed to highlight network perturbations, and we examined overlaps with human brain gene expression profiles in tauopathy. Results TauWT induced 1514 and 213 differentially expressed transcripts and proteins, respectively. TauR406W had a substantially greater impact, causing changes in 5494 transcripts and 697 proteins. There was a ~ 70% overlap between age- and Tau-induced changes and our analyses reveal pervasive bi-directional interactions. Strikingly, 42% of Tau-induced transcripts were discordant in the proteome, showing opposite direction of change. Tau-responsive gene expression networks strongly implicate innate immune activation. Cross-species analyses pinpoint human brain gene perturbations specifically triggered by Tau pathology and/or aging, and further differentiate between disease amplifying and protective changes. Conclusions Our results comprise a powerful, cross-species functional genomics resource for tauopathy, revealing Tau-mediated disruption of gene expression, including dynamic, age-dependent interactions between the brain transcriptome and proteome.


Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2996-2996
Author(s):  
Sanggyu Lee ◽  
Jianjun Chen ◽  
Goulin Zhou ◽  
Run Shi ◽  
Masha Kocherginsky ◽  
...  

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. The abnormally expressed genes from each translocation can be used to identify specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow(BM) samples from 22 patients with four types of AML, [de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11)].We made SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patient’s samples and normal CD15+ BM; they were also statistically significantly different at the 5% level. Using these strict criteria, we identified 2,381 unique tags, of which 2,053 were known genes and ESTs, and 328 were novel transcripts that were either specific for each translocation or were common(55) SAGE tags for all 4 translocations. The major change in all translocations was a decrease in expression in leukemia cells compared with normal cells; the decrease was least in the t(8;21) cells. Changes in expression of these known genes, which fall into different gene ontology functional categories, varied by translocation. Those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial microarray contained 349 probes including 212 known genes, 61 ESTs, 28 novel sequences based on our data and 48 genes reported by others. We have now included 65 additional probes that appeared to be correlated with survival. Using 63 samples with the four translocations [16 inv(16), 4 t(9;11), 20 t(15;17), 4 t(8;21) and 19 other translocations], we are validating which genes provide a robust, reproducible “fingerprint” for each translocation, for all translocations, and which ones provide reliable information related to prognosis and survival. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype as well as which genes appear to provide valid prognostic information.


2012 ◽  
Vol 11 ◽  
pp. CIN.S10375 ◽  
Author(s):  
Mark Burton ◽  
Mads Thomassen ◽  
Qihua Tan ◽  
Torben A. Kruse

Background The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features. Methods In this study we compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach. Results MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms. Conclusion Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.


2018 ◽  
Author(s):  
Daniel Berg ◽  
Katherine Kartheiser ◽  
Megan Leyrer ◽  
Alexandra Saali ◽  
David Berson

AbstractIntrinsically photosensitive retinal ganglion cells (ipRGCs) are rare mammalian photoreceptors essential for non-image-forming vision functions, such as circadian photoentrainment and the pupillary light reflex. They comprise multiple subtypes distinguishable by morphology, physiology, projections, and levels of expression of melanopsin (Opn4), their photopigment. The molecular programs that differentiate ipRGCs from other ganglion cells and ipRGC subtypes from one another remain elusive. Here, we present comprehensive gene expression profiles of early postnatal and adult mouse ipRGCs purified from two lines of reporter mice marking different sets of ipRGC subtypes. We find dozens of novel genes highly enriched in ipRGCs. We reveal that Rasgrp1 and Tbx20 are selectively expressed in subsets of ipRGCs, though these molecularly defined groups imperfectly match established ipRGC subtypes. We demonstrate that the ipRGCs regulating circadian photoentrainment are unexpectedly diverse at the molecular level. Our findings reveal unexpected complexity in gene expression patterns across mammalian ipRGC subtypes.


2018 ◽  
Vol 27 (20) ◽  
pp. 4136-4151 ◽  
Author(s):  
Claudia Kasper ◽  
Francois Olivier Hebert ◽  
Nadia Aubin-Horth ◽  
Barbara Taborsky

Sign in / Sign up

Export Citation Format

Share Document