Background: MicroRNAs (miRNAs) are a class of short regulatory RNAs encoded in the genome of DNA viruses, some single cell organisms, plants and animals. With the rapid development of technology, more and more miRNAs are being discovered. However, the origin and evolution of most miRNAs remain obscure. Here we report the origin and evolution dynamics of a human miRNA family. Results: We have shown that all members of the miR-1302 family are derived from MER53 elements. Although the conservation scores of the MER53-derived pre-miRNA sequences are low, we have identified 36 potential paralogs of MER53-derived miR-1302 genes in the human genome and 58 potential orthologs of the human miR-1302 family in placental mammals. We suggest that in placental species, this miRNA family has evolved following the birth-and-death model of evolution. Three possible mechanisms that can mediate miRNA duplication in evolutionary history have been proposed: the transposition of the MER53 element, segmental duplications and Alu-mediated recombination. Finally, we have found that the target genes of miR-1302 are over-represented in transportation, localization, and system development processes and in the positive regulation of cellular processes. Many of them are predicted to function in binding and transcription regulation. Conclusions: The members of miR-1302 family that are derived from MER53 elements are placental-specific miRNAs. They emerged at the early stage of the recent 180 million years since eutherian mammals diverged from marsupials. Under the birth-and-death model, the miR-1302 genes have experienced a complex expansion with some members evolving by segmental duplications and some by Alu-mediated recombination events.

Alzheimer’s disease (AD) is a severe neurodegenerative disorder for which identification of differentially expressed genes is one way to find new therapeutic targets. Here, we conducted analysis to identify age-independent, AD-specific genes. We found that the MET, WIF1, and NPTX2 genes are downregulated in AD. WIF1 and MET are implicated in Wnt and MET signaling and regulate GSK3β activity and are thus linked with AD. Importantly, we found that the GMPR gene exhibited a gradual increase in AD progression. A logistic model based on GMPR has good ability to classify AD cases. GMPR’s product GMPR1 is in the AMPK and adenosine receptor pathways and is thus associated with Tau phosphorylation in AD. This allows GMPR1 to be a therapeutic target. Therefore, we screened five possible inhibitors to GMPR1 by docking GMPR1 with 1,174 approved drugs. Among them, lumacaftor is ideal. We then tested the effects of lumacaftor on AD model mice. After 20 days of oral administration, we observed that β-Amyloid accumulation was slowed down, and phosphorylation of Tau was almost eliminated in the treated mice. We highlight the elevated expression level of GMPR in AD and propose a therapeutic strategy of inhibiting GMPR1 with lumacaftor.

While our understanding of cellular and molecular processes has grown exponentially, issues related to the cell microenvironment and cellular heterogeneity have sparked a new debate concerning the cell identity. Cell composition (chromatin and nuclear architecture) poses a strong risk for dynamic changes in the diseased condition. Since chromatin accessibility patterns play a major role in human diseases, it is therefore anticipated that a deconvolution tool based on open chromatin data will provide better performance in identifying cell composition. Herein, we have designed the deconvolution tool “DeconPeaker,” which can precisely define the uniqueness among subpopulations of cells using open chromatin datasets. Using this tool, we simultaneously evaluated chromatin accessibility and gene expression datasets to estimate cell types and their respective proportions in a mixture of samples. In comparison to other known deconvolution methods, we observed the lowest average root-mean-square error (RMSE = 0.042) and the highest average correlation coefficient (r = 0.919) between the prediction and “true” proportion. As a proof-of-concept, we also tested chromatin accessibility data from acute myeloid leukemia (AML) and successfully obtained unique cell types associated with AML progression. Furthermore, we showed that chromatin accessibility represents more essential characteristics in the identification of cell types than gene expression. Taken together, DeconPeaker as a powerful tool has the potential to combine different datasets (primarily, chromatin accessibility and gene expression) and define different cell types in mixtures. The Python package of DeconPeaker is now available at https://github.com/lihuamei/DeconPeaker.

Emerging evidence suggests an inverse association between cancer and neurodegenerative diseases (NDD). Although phenotypically different, both diseases display a significant imbalance in the ubiquitination/deubiquitination processes. Therefore, we particularly investigated the expression of ubiquitin C-terminal hydrolases (UCHs: UCH-L1, UCH-L3, UCH-L5 and BAP1), a subfamily of deubiquitinating enzymes (DUBs), using publically available datasets (GTEx, TCGA) and observed altered expression of UCH-L1, UCH-L3, UCH-L5 in 17 cancer types. Interestingly, UCH-L1 (known to be enriched in neurons and interacting with the Parkinson’s disease-associated protein α-synuclein) appeared to be a prognostic indicator of unfavorable outcome in endometrial and urothelial cancer, while increased expression of UCH-L3 and UCH-L5 was associated with poor survival in liver and thyroid cancer, respectively. In normal tissues, UCH-L1 was found to be strongly expressed in the cerebral cortex and hypothalamus, while UCH-L3 expression was somewhat higher in the testis. The occurrence of mutation rates in UCHs also suggests that BAP1 and UCH-L5 may play a more dominant role in cancers than UCH-L1 and UCH-L3. We also characterized the functional context and configuration of the repeat elements in the promoter of DUBs genes and found that UCHs are highly discriminatory for catabolic function and are mainly enriched with LINE/CR1 repeats. Regarding the thesis of an inverse association between cancer and NDD, we observed that among all DUBs, UCHs are the one most involved in both entities. Considering a putative therapeutic potential based on presumed common mechanisms, it will be useful to determine whether other DUBs can compensate for the loss of UCH activity under physiological conditions. However, experimental evidence is required to substantiate this argument.

The BAP1 (BRCA1-associated protein 1) gene is associated with a variety of human cancers. With its gene product being a nuclear ubiquitin carboxy-terminal hydrolase with deubiquitinase activity, BAP1 acts as a tumor suppressor gene with potential pleiotropic effects in multiple tumor types. Herein, we focused specifically on uveal melanoma (UM) in which BAP1 mutations are associated with a metastasizing phenotype and decreased survival rates. We identified the ubiquitin carboxyl hydrolase (UCH) domain as a major hotspot region for the pathogenic mutations with a high evolutionary action (EA) score. This also includes the mutations at conserved catalytic sites and the ones overlapping with the phosphorylation residues. Computational protein interaction studies revealed that distant BAP1-associated protein complexes (FOXK2, ASXL1, BARD1, BRCA1) could be directly impacted by this mutation paradigm. We also described the conformational transition related to BAP1-BRCA-BARD1 complex, which may pose critical implications for mutations, especially at the docking interfaces of these three proteins. The mutations affect - independent of being somatic or germline - the binding affinity of miRNAs embedded within the BAP1 locus, thereby altering the unique regulatory network. Apart from UM, BAP1 gene expression and survival associations were found to be predictive for the prognosis in several (n = 29) other cancer types. Herein, we suggest that although BAP1 is conceptually a driver gene in UM, it might contribute through its interaction partners and its regulatory miRNA network to various aspects of cancer. Taken together, these findings will pave the way to evaluate BAP1 in a variety of other human cancers with a shared mutational spectrum.

Background: The identification of cell type-specific genes (markers) is an essential step for the deconvolution of the cellular fractions, primarily, from the gene expression data of a bulk sample. However, the genes with significant changes identified by pair-wise comparisons cannot indeed represent the specificity of gene expression across multiple conditions. In addition, the knowledge about the identification of gene expression markers across multiple conditions is still paucity. Results: Herein, we developed a hybrid tool, LinDeconSeq, which consists of 1) identifying marker genes using specificity scoring and mutual linearity strategies across any number of cell types, and 2) predicting cellular fractions of bulk samples using weighted robust linear regression with the marker genes identified in the first stage. On multiple publicly available datasets, the marker genes identified by LinDeconSeq demonstrated better accuracy and reproducibility compared to MGFM and RNentropy. Among deconvolution methods, LinDeconSeq showed low average deviations (≤0.0958) and high average Pearson correlations (≥0.8792) between the predicted and actual fractions on the benchmark datasets. Importantly, the cellular fractions predicted by LinDeconSeq appear to be relevant in the diagnosis of acute myeloid leukemia (AML). The distinct cellular fractions in granulocyte-monocyte progenitor (GMP), lymphoid-primed multipotent progenitor (LMPP) and monocytes (MONO) were found to be closely associated with AML compared to the healthy samples. Moreover, the heterogeneity of cellular fractions in AML patients divided these patients into two subgroups, differing in both prognosis and mutation patterns. GMP fraction was the most pronounced between these two subgroups, particularly, in SubgroupA, which was strongly associated with the better AML prognosis and the younger population. Totally, the identification of marker genes by LinDeconSeq represents the improved feature for deconvolution. The data processing strategy with regard to the cellular fractions used in this study also showed potential for the diagnosis and prognosis of diseases. Conclusions: Taken together, we developed a freely-available and open-source tool LinDeconSeq (https://github.com/lihuamei/LinDeconSeq), which includes marker identification and deconvolution procedures. LinDeconSeq is comparable to other current methods in terms of accuracy when applied to benchmark datasets and has broad application in clinical outcome and disease-specific molecular mechanisms.