Gene Regulatory Network Reconstruction and Pathway Inference from High Throughput Gene Expression Data.

Two basic motivating questions in biomedical research are: What genes regulate what other genes? What genes or groups of genes regulate a specific phenotype? Gene regulatory network (GRN) reconstruction and pathway inference are the two computational strategies addressing these two questions respectively. GRN reconstruction is to infer the components and topology of an unknown pathway, while pathway inference is to infer association between known pathways and a phenotype. This thesis focuses on gene regulatory network reconstruction and pathway inference from high throughput biological data. In the first part of this work, I developed a novel method, MI3, for de novo GRN reconstruction using continuous three-way mutual information. MI3 addresses three major issues in previous probabilistic methods simultaneously: (1) to handle continuous variables, (2) to detect high order relationships, (3) to differentiate causal vs. confounding relationships. MI3 consistently and significantly outperformed frequently used control methods and faithfully capture mechanistic relationships from gene expression data. In the second part of this work, I proposed another novel method, GAGE, Generally Applicable Gene Set Enrichment for pathway inference. I successfully apply GAGE to multiple microarray data sets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better performance when compared to two other commonly used GSA methods of GSEA and PAGE. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. In the third part of this work, we conducted a microarray study on transcriptional programs during BMP6 induced osteoblast differentiation and mineralization, and applied GAGE to recover the regulatory pathways and transcriptional signaling networks in the process. I not only showed which pathways or gene sets are significant, but also when and how they are involved in the osteoblast differentiation and mineralization. Different from common pathway analyses, our work further captures the interconnections among individual pathways or functional groups and integrate them into a whole system.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61768/1/luow_1.pd

Systematic reconstruction of autism biology from massive genetic mutation profiles

Autism spectrum disorder (ASD) affects 1% of world population and has become a pressing medical and social problem worldwide. As a paradigmatic complex genetic disease, ASD has been intensively studied and thousands of gene mutations have been reported. Because these mutations rarely recur, it is difficult to (i) pinpoint the fewer disease-causing versus majority random events and (ii) replicate or verify independent studies. A coherent and systematic understanding of autism biology has not been achieved. We analyzed 3392 and 4792 autism-related mutations from two large-scale whole-exome studies across multiple resolution levels, that is, variants (single-nucleotide), genes (protein-coding unit), and pathways (molecular module). These mutations do not recur or replicate at the variant level, but significantly and increasingly do so at gene and pathway levels. Genetic association reveals a novel gene + pathway dual-hit model, where the mutation burden becomes less relevant. In multiple independent analyses, hundreds of variants or genes repeatedly converge to several canonical pathways, either novel or literature-supported. These pathways define recurrent and systematic ASD biology, distinct from previously reported gene groups or networks. They also present a catalog of novel ASD risk factors including 118 variants and 72 genes. At a subpathway level, most variants disrupt the pathway-related gene functions, and in the same gene, they tend to hit residues extremely close to each other and in the same domain. Multiple interacting variants spotlight key modules, including the cAMP (adenosine 3′,5′-monophosphate) second-messenger system and mGluR (metabotropic glutamate receptor) signaling regulation by GRKs (G protein–coupled receptor kinases). At a superpathway level, distinct pathways further interconnect and converge to three biology themes: synaptic function, morphology, and plasticity

The stability of exfoliated FeSe nanosheets during in-air device fabrication process

We studied the stability and superconductivity of FeSe nanosheets during an in-air device fabrication process. Methods were developed to improve the exfoliation yield and to maintain the superconductivity of FeSe. Raman spectroscopy, atomic force microscopy, optical microscopy and time-of-flight-secondary-ion-mass-spectroscopy measurements show that FeSe nanosheets decayed in air. Precipitation of Se particles and iron oxidation likely occurred during the decay process. Transport measurements revealed that the superconductivity of FeSe disappeared during a conventional electron beam lithography process. Shadow mask evaporation and transfer onto pre-defined electrodes methods were shown to be effective in maintaining the superconductivity after the in-air device fabrication process. These methods developed provide a way of making high quality FeSe nano-devices

Time series gene expression profiling and temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization

Background: BMP6 mediated osteoblast differentiation plays a key role in skeletal development and bone disease. Unfortunately, the signaling pathways regulated by BMP6 are largely uncharacterized due to both a lack of data and the complexity of the response. Results: To better characterize the signaling pathways responsive to BMP6, we conducted a time series microarray study to track BMP6 induced osteoblast differentiation and mineralization. These temporal data were analyzed using a customized gene set analysis approach to identify temporally coherent sets of genes that act downstream of BMP6. Our analysis identified BMP6 regulation of previously reported pathways, such as the TGF-beta pathway. We also identified previously unknown connections between BMP6 and pathways such as Notch signaling and the MYB and BAF57 regulatory modules. In addition, we identify a super-network of pathways that are sequentially activated following BMP6 induction. Conclusion: In this work, we carried out a microarray-based temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization using GAGE method. This novel temporal analysis is more informative and powerful than the classical static pathway analysis in that: (1) it captures the interconnections between signaling pathways or functional modules and demonstrates the even higher level organization of molecular biological systems; (2) it describes the temporal perturbation patterns of each pathway or module and their dynamic roles in osteoblast differentiation. The same set of experimental and computational strategies employed in our work could be useful for studying other complex biological processes

GAGE: generally applicable gene set enrichment for pathway analysis

<p>Abstract</p> <p>Background</p> <p>Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.</p> <p>Results</p> <p>To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.</p> <p>GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature.</p> <p>Conclusion</p> <p>GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from <url>http://sysbio.engin.umich.edu/~luow/downloads.php</url>.</p

Influence of the Maritime Continent on the Boreal Summer Intraseasonal Oscillation

Abstract The e¤ect of the Maritime Continent (MC) on the propagation characteristics of the boreal summer intraseasonal oscillation (ISO) over the Indo-western Pacific region were investigated by performing high-resolution (T159) atmospheric general circulation model (AGCM) simulations that remove and retain the MC. The most significant di¤erence, as revealed by a finite domain wavenumber-frequency spectral analysis is the weakening of the northward propagation of ISO over the Asian monsoon region (65 -160 E) when the MC is removed; a less significant di¤erence is the enhancement of the eastward propagation along the equator. The diagnosis of the vertical structure of the simulated ISO and the model mean flow indicates that the weakening of the northward propagation is primarily attributed to the reduction of the background easterly shear, low-level southerly and meridional humidity gradient, all of which contribute to the weakening of meridional asymmetries of vorticity and humidity fields with respect to the ISO convection center. The enhanced eastward propagation is possibly attributed to the strengthening of the mean convection over the MC in association with the increase of the local surface moisture and moist static energy

Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets

<p>Abstract</p> <p>Background</p> <p>Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory.</p> <p>Results</p> <p>We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other.</p> <p>Conclusions</p> <p>Our results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.</p

Effects of precipitation changes on aboveground net primary production and soil respiration in a switchgrass field

Switchgrass (Panicum virgatum L.) is widely selected as a model feedstock for sustainable replacement of fossil fuels and climate change mitigation. However, how climate changes, such as altered precipitation (PPT), will influence switchgrass growth and soil carbon storage potential have not been well investigated. We conducted a two-year PPT manipulation experiment with five treatments: −50%, −33%, +0%, +33%, and +50% of ambient PPT, in an “Alamo” switchgrass field in Nashville, TN. Switchgrass aboveground net primary production (ANPP), leaf gas exchange, and soil respiration (SR) were determined each growing season. Data collected from this study was then used to test whether switchgrass ANPP responds to PPT changes in a double asymmetry pattern as framed by Knapp et al. (2017), and whether it is held true for other ecosystem processes such as SR. Results showed that the wet (+33%, and +50%) treatments had little effects on ANPP and leaf gas exchange compared to the ambient precipitation treatment, regardless of fertilization or not. The −33% treatment did not change ANPP and leaf photosynthesis, but significantly decreased transpiration and enhanced water use efficiency (WUE). Only the −50% treatment significantly decreased ANPP and LAI, without changing leaf photosynthesis. SR generally decreased under the drought treatments and increased under the wet treatments, while there was no significant difference between the two drought treatments or between the two wet treatments. Our results demonstrate that switchgrass ANPP responded in a single negative asymmetry model to PPT changes probably due to relative high PPT in the region. However, even in such a mesic ecosystem, SR responded strongly to PPT changes in an “S” curve model, suggesting that future climate changes may have greater but more complex effects on switchgrass belowground than aboveground processes. The contrasting models for switchgrass ANPP and SR in response to PPT indicate that extreme wet or dry PPT conditions may shift ecosystem from carbon accumulation toward debt, and in turn provide government and policy makers with useful information for sustainable management of switchgrass

High speed self-testing quantum random number generation without detection loophole

Quantum mechanics provides means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a self-testing manner that is independent of implementation devices. Here, we present an experimental demonstration of self-testing quantum random number generation based on an detection-loophole free Bell test with entangled photons. In the randomness analysis, without the assumption of independent identical distribution, we consider the worst case scenario that the adversary launches the most powerful attacks against quantum adversary. After considering statistical fluctuations and applying an 80 Gb $\times$ 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than $10^{-5}$. Such self-testing random number generators mark a critical step towards realistic applications in cryptography and fundamental physics tests.Comment: 34 pages, 10 figure