Clb2 is the major B-type mitotic cyclin required for entry into mitosis in the budding yeast Saccharomyces cerevisiae. We showed that accumulation of CLB2 transcripts in G2 cells is controlled at the transcriptional level and identified a 55-bp upstream activating sequence (UAS) containing an Mcm1 binding site as being necessary and sufficient for cell cycle regulation. Sequences within the cell cycle-regulated UAS were shown to bind Mcm1 in vitro, and mutation which abolished Mcm1-dependent DNA binding activity eliminated cell cycle-regulated transcription in vivo. A second protein with no autonomous DNA binding activity was also recruited into Mcm1-UAS complexes, generating a ternary complex. A point mutation in the CLB2 UAS which blocked ternary complex formation, but still allowed Mcm1 to bind, resulted in loss of cell cycle regulation in vivo, suggesting that the ternary complex factor is also important in control of CLB2 transcription. We discuss the possibility that the CLB2 gene is coregulated with other genes known to be regulated with the same periodicity and suggest that Mcm1 and the ternary complex factor may coordinately regulate several other G2-regulated transcripts.

Abstract. Pneumonia is a lower respiratory tract infection that causes dramatic mortality worldwide. The present study aimed to investigate the pathogenesis of pneumonia and identify microRNA (miRNA) biomarkers as candidates for targeted therapy. RNA from the peripheral blood plasma of participants with pneumonia (severe, n=9; non-severe, n=9) and controls (n=9) was isolated and paired-end sequencing was performed on an Illumina HiSeq4000 system. Following the processing of raw reads, the sequences were aligned against the Genome Reference Consortium human genome assembly 38 reference genome using Bowtie2 software. Reads per kilobase of transcript per million mapped read values were obtained and the limma software package was used to identify differentially expressed miRNAs (DE-miRs). Then, DE-miR targets were predicted and subjected to enrichment analysis. In addition, a protein-protein interaction (PPI) network of the predicted targets was constructed. This analysis identified 11 key DE-miRs in pneumonia samples, including 6 upregulated miRNAs (including hsa-miR-34a and hsa-miR-455) and 5 downregulated miRNAs (including hsa-let-7f-1). All DE-miRs kept their upregulation/downregulation pattern in the control, non-severe pneumonia and severe pneumonia samples. Predicted target genes of DE-miRs in the subjects with non-severe pneumonia vs. the control and the subjects with severe pneumonia vs. the non-severe pneumonia group were markedly enriched in the adherens junction and Wnt signaling pathways. KALRN, Ras homolog family member A (RHOA), β-catenin (CTNNB1), RNA polymerase II subunit K (POLR2K) and amyloid precursor protein (APP) were determined to encode crucial proteins in the PPI network constructed. KALRN was predicted to be a target of hsa-mir-200b, while RHOA, CTNNB1, POLR2K and APP were predicted targets of hsa-let-7f-1. The results of the present study demonstrated that hsa-let-7f-1 may serve a role in the development of cancer and the Notch signaling pathway. Conversely, hsa-miR-455 may be an inhibitor of pneumonia pathogenesis. Furthermore, hsa-miR-200b might promote pneumonia via targeting KALRN.

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Aim This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer. Methods Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve. Results We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable. Conclusions This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

Background Cashmere goat is famous for its high-quality fibers. The growth of cashmere in secondary hair follicles exhibits a seasonal pattern arising from circannual changes in the natural photoperiod. Although several studies have compared and analyzed the differences in gene expression between different hair follicle growth stages, the selection of samples in these studies relies on research experience or morphological evidence. Distinguishing hair follicle growth cycle according to gene expression patterns may help to explore the regulation mechanisms related to cashmere growth and the effect of melatonin from a molecular level more accurately. Results In this study, we applied RNA-sequencing to the hair follicles of three normal and three melatonin-treated Inner Mongolian cashmere goats sampled every month during a whole hair follicle growth cycle. A total of 3559 and 988 genes were subjected as seasonal changing genes (SCGs) in the control and treated groups, respectively. The SCGs in the normal group were divided into three clusters, and their specific expression patterns help to group the hair follicle growth cycle into anagen, catagen and telogen stages. Some canonical pathways such as Wnt, TGF-beta and Hippo signaling pathways were detected as promoting the hair follicle growth, while Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Jak-STAT, Fc epsilon RI, NOD-like receptor, Rap1, PI3K-Akt, cAMP, NF-kappa B and many immune-related pathways were detected in the catagen and telogen stages. The PI3K-Akt signaling, ECM-receptor interaction and Focal adhesion were found in the transition stage between telogen to anagen, which may serve as candidate biomarkers for telogen-anagen regeneration. A total of 16 signaling pathways, 145 pathway mRNAs, and 93 lncRNAs were enrolled to construct the pathway-mRNA-lncRNA network, which indicated the function of lncRNAs through interacting with their co-expressed mRNAs. Pairwise comparisons between the control and melatonin-treated groups also indicated 941 monthly differentially expressed genes (monthly DEGs). These monthly DEGs were mainly distributed from April and September, which revealed a potential signal pathway map regulating the anagen stage triggered by melatonin. Enrichment analysis showed that Wnt, Hedgehog, ECM, Chemokines and NF-kappa B signaling pathways may be involved in the regulation of non-quiescence and secondary shedding under the influence of melatonin. Conclusions Our study decoded the key regulators of the whole hair follicle growth cycle, laying the foundation for the control of hair follicle growth and improvement of cashmere yield.