Deciphering the functions of Stromal Interaction Molecule-1 in amelogenesis using AmelX-iCre mice.

Introduction: The intracellular Ca2+ sensor stromal interaction molecule 1 (STIM1) is thought to play a critical role in enamel development, as its mutations cause Amelogenesis Imperfecta (AI). We recently established an ameloblast-specific (AmelX-iCre) Stim1 conditional deletion mouse model to investigate the role of STIM1 in controlling ameloblast function and differentiation in vivo (Stim1 cKO). Our pilot data (Said et al., J. Dent. Res., 2019, 98, 1002-1010) support our hypothesis for a broad role of Stim1 in amelogenesis. This paper aims to provide an in-depth characterization of the enamel phenotype observed in our Stim1 cKO model. Methods: We crossed AmelX-iCre mice with Stim1-floxed animals to develop ameloblast-specific Stim1 cKO mice. Scanning electron microscopy, energy dispersive spectroscopy, and micro- CT were used to study the enamel phenotype. RNAseq and RT-qPCR were utilized to evaluate changes in the gene expression of several key ameloblast genes. Immunohistochemistry was used to detect the amelogenin, matrix metalloprotease 20 and kallikrein 4 proteins in ameloblasts. Results: Stim1 cKO animals exhibited a hypomineralized AI phenotype, with reduced enamel volume, diminished mineral density, and lower calcium content. The mutant enamel phenotype was more severe in older Stim1 cKO mice compared to younger ones and changes in enamel volume and mineral content were more pronounced in incisors compared to molars. Exploratory RNAseq analysis of incisors' ameloblasts suggested that ablation of Stim1 altered the expression levels of several genes encoding enamel matrix proteins which were confirmed by subsequent RT-qPCR. On the other hand, RT-qPCR analysis of molars' ameloblasts showed non-significant differences in the expression levels of enamel matrix genes between control and Stim1-deficient cells. Moreover, gene expression analysis of incisors' and molars' ameloblasts showed that Stim1 ablation caused changes in the expression levels of several genes associated with calcium transport and mitochondrial kinetics. Conclusions: Collectively, these findings suggest that the loss of Stim1 in ameloblasts may impact enamel mineralization and ameloblast gene expression.

Silver: Forging almost Gold Standard Datasets.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods; however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene-gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

Juxtapose: a gene-embedding approach for comparing co-expression networks.

BACKGROUND: Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. METHODS: A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. RESULTS: We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. CONCLUSIONS: Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. AVAILABILITY: A development version of the software used in this paper is available at https://github.com/klovens/juxtapose.

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population.

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

Size matters: how sample size affects the reproducibility and specificity of gene set analysis.

BACKGROUND: Gene set analysis is a well-established approach for interpretation of data from high-throughput gene expression studies. Achieving reproducible results is an essential requirement in such studies. One factor of a gene expression experiment that can affect reproducibility is the choice of sample size. However, choosing an appropriate sample size can be difficult, especially because the choice may be method-dependent. Further, sample size choice can have unexpected effects on specificity. RESULTS: In this paper, we report on a systematic, quantitative approach to study the effect of sample size on the reproducibility of the results from 13 gene set analysis methods. We also investigate the impact of sample size on the specificity of these methods. Rather than relying on synthetic data, the proposed approach uses real expression datasets to offer an accurate and reliable evaluation. CONCLUSION: Our findings show that, as a general pattern, the results of gene set analysis become more reproducible as sample size increases. However, the extent of reproducibility and the rate at which it increases vary from method to method. In addition, even in the absence of differential expression, some gene set analysis methods report a large number of false positives, and increasing sample size does not lead to reducing these false positives. The results of this research can be used when selecting a gene set analysis method from those available.

Computational identification of harmful mutation regions to the activity of transposable elements.

BACKGROUND: Transposable elements (TEs) are interspersed DNA sequences that can move or copy to new positions within a genome. TEs are believed to promote speciation and their activities play a significant role in human disease. In the human genome, the 22 AluY and 6 AluS TE subfamilies have been the most recently active, and their transposition has been implicated in many inherited human diseases and in various forms of cancer. Therefore, understanding their transposition activity is very important and identifying the factors that affect their transpositional activity is of great interest. Recently, there has been some work done to quantify the activity levels of active Alu TEs based on variation in the sequence. Given this activity data, an analysis of TE activity based on the position of mutations is conducted. RESULTS: A method/simulation is created to computationally predict so-called harmful mutation regions in the consensus sequence of a TE; that is, mutations that occur in these regions decrease the transpositional activity dramatically. The methods are applied to the most active subfamily, AluY, to identify the harmful regions, and seven harmful regions are identified within the AluY consensus with q-values less than 0.05. A supplementary simulation also shows that the identified harmful regions covering the AluYa5 RNA functional regions are not occurring by chance. This method is then applied to two additional TE families: the Alu family and the L1 family, to computationally detect the harmful regions in these elements. CONCLUSIONS: We use a computational method to identify a set of harmful mutation regions. Mutations within the identified harmful regions decrease the transpositional activity of active elements. The correlation between the mutations within these regions and the transpositional activity of TEs are shown to be statistically significant. Verifications are presented using the activity of AluY elements and the secondary structure of the AluYa5 RNA, providing evidence that the method is successfully identifying harmful mutation regions.

Speed improvements of peptide-spectrum matching using single-instruction multiple-data instructions.

Peptide-spectrum matching is one of the most time-consuming portion of the database search method for assignment of tandem mass spectra to peptides. In this study, we develop a parallel algorithm for peptide-spectrum matching using Single-Instruction Multiple Data (SIMD) instructions. Unlike other parallel algorithms in peptide-spectrum matching, our algorithm parallelizes the computation of matches between a single spectrum and a given peptide sequence from the database. It also significantly reduces the number of comparison operations. Extra improvements are obtained by using SIMD instructions to avoid conditional branches and unnecessary memory access within the algorithm. The implementation of the developed algorithm is based on the Streaming SIMD Extensions technology that is embedded in most Intel microprocessors. Similar technology also exists in other modern microprocessors. A simulation shows that the developed algorithm achieves an 18-fold speedup over the previous version of Real-Time Peptide-Spectrum Matching algorithm [F. X. Wu et al., Rapid Commun. Mass Sepctrom. 2006, 20, 1199-1208]. Therefore, the developed algorithm can be employed to develop real-time control methods for MS/MS.