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1. In silico model for miRNA-mediated regulatory network in cancer

   https://doi.org/10.1093/bib/bbab264  

   Ahmed, Khandakar Tanvir; Sun, Jiao; Chen, William; Martinez, Irene; Cheng, Sze; Zhang, Wencai; Yong, Jeongsik; Zhang, Wei (January 2021, Briefings in Bioinformatics)

   null (Ed.)

   Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA–mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet 

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2. Deconvolution analysis of cell‐type expression from bulk tissues by integrating with single‐cell expression reference

   https://doi.org/10.1002/gepi.22494  

   Luo, Yutong; Fan, Ruzong (July 2022, Genetic Epidemiology)

   Abstract To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data. 

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3. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

   https://doi.org/10.1038/s41467-020-15816-6  

   Jew, Brandon; Alvarez, Marcus; Rahmani, Elior; Miao, Zong; Ko, Arthur; Garske, Kristina M.; Sul, Jae Hoon; Pietiläinen, Kirsi H.; Pajukanta, Päivi; Halperin, Eran (April 2020, Nature Communications)

   Abstract We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes. 

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4. Phenotypic variation in biting behavior associated with differences in expression of olfactory genes in the vector mosquito, Aedes albopictus (Diptera: Culicidae)

   https://doi.org/10.1093/jme/tjae003  

   Heilig, Mara; Sturiale, Samantha_L; Marzec, Sarah; Holzapfel, Christina_M; Bradshaw, William_E; Meuti, Megan_E; Armbruster, Peter_A; Syed, ed., Zainulabeuddin (February 2024, Journal of Medical Entomology)

   Abstract We evaluated miRNA and mRNA expression differences in head tissues between avid-biting vs. reluctant-biting Aedes albopictus (Skuse) females from a single population over a 20-min timescale. We found no differences in miRNA expression between avid vs. reluctant biters, indicating that translational modulation of blood-feeding behavior occurs on a longer timescale than mRNA transcription. In contrast, we detected 19 differentially expressed mRNAs. Of the 19 differentially expressed genes at the mRNA level between avid-biting vs. reluctant-biting A. albopictus, 9 are implicated in olfaction, consistent with the well-documented role of olfaction in mosquito host-seeking. Additionally, several of the genes that we identified as differentially expressed in association with phenotypic variation in biting behavior share similar functions with or are inferred orthologues of, genes associated with evolutionary variation in biting behaviors of Wyeomyia smithii (Coq.) and Culex pipiens (Lin.). A future goal is to determine whether these genes are involved in the evolutionary transition from a biting to a non-biting life history. 

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5. What can we learn when fitting a simple telegraph model to a complex gene expression model?

   https://doi.org/10.1371/journal.pcbi.1012118  

   Jiao, Feng; Li, Jing; Liu, Ting; Zhu, Yifeng; Che, Wenhao; Bleris, Leonidas; Jia, Chen (May 2024, PLOS Computational Biology)

   Finley, Stacey D (Ed.)

   In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data forE. coliand mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise. 

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