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Temperature differences over time and space have been hypothesized to cause variation in the rate of molecular evolution of species, but empirical evidence is mixed. To further test this hypothesis, we utilized a large exon-capture sequence data of Australian Eugongylinae skinks, exemplifying a radiation of temperature-sensitive ectotherms spanning a large latitudinal gradient. The association between temperature (and other species traits) and long-term substitution rate was assessed based on 1268 sequenced exons of 44 species pairs from the Eugongylinae subfamily using regression analyses. Temperature is the strongest, positively correlated predictor of variation in substitution rate across the Australian Eugongylinae. It explains 45% of variation in synonymous substitution rate, and 11% after controlling for all the other factors. Synonymous substitution rate is also negatively associated with body size, with a 6% variation explained by body size after controlling for the effects of temperature. Other factors are not associated with synonymous substitution rate after controlling for temperature. Overall, this study points to temperature as a strong predictor of the molecular evolution rate in the Eugongylinae subfamily, and demonstrates the power of large-scale exonic data to identify correlates of the rate of molecular evolution.

Low grade gliomas are invasive brain tumors that mostly occur among young adults. Previous studies showed that LGG is characterized by IDH1/2 mutations; however, there were some cases where the cancer patients suffer low mutation rate of those genes. In this study, two genes are proposed as new biomarkers: IQGAP and HspB8. IQGAP has been found to be correlated with various types of cancer, while HspB8 role in LGG has not been determined, according to F-Census database. This study is aimed to identify the expressions of IQGAP and HspB8 genes among LGG patients and assess their potential to be the next biomarkers. The expression data was derived from The Cancer Genome Atlas project and downloaded using TCGA Assembler. Then, IQGAP - miRNA expression correlations and HspB8 expression analysis were conducted using Matlab and Microsoft Excel, respectively. The results were validated using miRTarBase and Firebrowse. The results showed that the strongest IQGAP - miRNA negative correlation was dominated by IQGAP2, peaked at -0.41. HspB8, on the other hand, was found to be highly expressed in LGG. However, due to lack of researches, their roles have not been validated yet. Therefore, the results from this study might become basis for further wet-lab studies.

Thyroid carcinoma (THCA) incidence has already increased by 6.2% per year and become the sixth most common malignancy diagnosed in women. The IQGAP-miRNA interaction was known to affect many cancer types, including the carcinoma. Through in silico process known as miTS, a miRNA biomarker was researched along with its molecular pathway intervention. The input of the pipeline was THCA patients from GDC Data Portal ID and it underwent data mining, statistical, and visualization process using bioinformatics tools and databases such as TCGA Assembler in R Studio, Matlab, and STRING database. The aim of this study is to discover a potential microRNA biomarker regulating the expression of IQGAP in THCA along with its acknowledged pathways. Our study shows that (1) IQGAP1 become a promoter to the growth of THCA through supporting the growth of adherens junctions in cytoskeleton, (2) IQGAP2 was a cancer suppressor, (3) miR-146b have relation with MMP16, (4) it is still unknown whether miR-146b upregulate or downregulate the PDGFRA expression in THCA. Furthermore, the pipeline of the process and command line used was provided in an open-source package (https://github.com/stefanuswibowo/miTS_Pathway).

Triple-negative breast cancer is one of the deadliest diseases for women, according to the World Health Organization. The most common drugs used and in development for this disease are based upon the proteomics approach, but this so far has not accounted for the whole repertoire of human genome expression. However, a novel approach is currently under development to model the molecular interaction between miR-145 and the 3' untranslated region (UTR) of ARF6 mRNA. This approach should eventually be useful for transcriptomics-based drug development. The utilized methods are molecular-docking and dynamics based, using open-source software. It was found that there was a fine-grained molecular interaction between miR-145 and the 3' UTR of ARF6 mRNA. It is concluded that the information from the interaction could be utilized as the basis for drug development.

AbstractThe variation of evolutionary histories along the genome presents a challenge for phylogenomic methods to identify the non-recombining regions and reconstruct the phylogenetic tree for each region. To address this problem, many studies used the non-overlapping window approach, often with an arbitrary selection of fixed window sizes that potentially include intra-window recombination events. In this study, we proposed an information theoretic approach to select a window size that best reflects the underlying histories of the alignment. First, we simulated chromosome alignments that reflected the key characteristics of an empirical dataset and found that the AIC is a good predictor of window size accuracy in correctly recovering the tree topologies of the alignment. Due to the issue of missing data in empirical datasets, we then designed a stepwise non-overlapping window approach and applied this method to the genomes of erato-sara Heliconius butterflies and great apes. We found that the best window sizes for the butterflies’ chromosomes ranged from <125bp to 250bp, which are much shorter than those used in a previous study even though this difference in window size did not significantly change the most common topologies across the genome. On the other hand, the best window sizes for great apes’ chromosomes ranged from 500bp to 1kb with the proportion of the major topology (grouping human and chimpanzee) falling between 60% and 87%, consistent with previous findings. Additionally, we observed a notable impact of stochastic error and concatenation when using small and large windows, respectively. For instance, the proportion of the major topology for great apes was 50% when using 250bp windows, but reached almost 100% for 64kb windows. In conclusion, our study highlights the challenges associated with selecting a window size in non-overlapping window analyses and proposes the AIC as a more objective way to select the optimal window size for whole genome alignments.Competing Interest StatementThe authors have declared no competing interest.

CHO is the cell line of choice for the manufacturing of many complex biotherapeutics. The constant upgrading of cell productivity is needed to meet the growing demand for these life-saving drugs. Manipulation of small non-coding RNAs—miRNAs—is a good alternative to a single gene knockdown approach due to their post-transcriptional regulation of entire cellular pathways without posing translational burden to the production cell. In this study, we performed a high-throughput screening of 2042-human miRNAs and identified several candidates able to increase cell-specific and overall production of Erythropoietin and Etanercept in CHO cells. Some of these human miRNAs have not been found in Chinese hamster cells and yet were still effective in them. We identified miR-574-3p as being able, when overexpressed in CHO cells, to improve overall productivity of Erythropoietin and Etanercept titers from 1.3 to up to 2-fold. In addition, we validated several targets of miR-574-3p and identified p300 as a main target of miR-574-3p in CHO cells. Furthermore, we demonstrated that stable CHO cell overexpressing miRNAs from endogenous CHO pri-miRNA sequences outperform the cells with human pri-miRNA sequences. Our findings highlight the importance of flanking genomic sequences, and their secondary structure features, on pri-miRNA processing offering a novel, cost-effective and fast strategy as a valuable tool for efficient miRNAs engineering in CHO cells.