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Proper gene expression relies on a class of ubiquitously expressed, uridine-rich small nuclear RNAs (snRNAs) transcribed by RNA polymerase II (RNAPII). Vertebrate snRNAs are transcribed from a unique promoter, which is required for proper 3-end formation, and cleavage of the nascent transcript involves the activity of a poorly understood set of proteins called the Integrator complex. To examine 3-end formation in Drosophila melanogaster, we developed a cell-based reporter that monitors aberrant 3-end formation of snRNA through the gain in expression of green fluorescent protein (GFP). We used this reporter in Drosophila S2 cells to determine requirements for U7 snRNA 3-end formation and found that processing was strongly dependent upon nucleotides located within the 3 stem-loop as well as sequences likely to comprise the Drosophila equivalent of the vertebrate 3 box. Substitution of the actin promoter for the snRNA promoter abolished proper 3-end formation, demonstrating the conserved requirement for an snRNA promoter in Drosophila. We tested the requirement for all Drosophila Integrator subunits and found that Integrators 1, 4, 9, and 11 were essential for 3-end formation and that Integrators 3 and 10 may be dispensable for processing. Depletion of cleavage and polyadenylation factors or of histone pre-mRNA processing factors did not affect U7 snRNA processing efficiency, demonstrating that the Integrator complex does not share components with the mRNA 3-end processing machinery. Finally, flies harboring mutations in either Integrator 4 or 7 fail to complete development and accumulate significant levels of misprocessed snRNA in the larval stages.In eukaryotes, the major transcripts produced by RNA polymerase II (RNAPII) include the polyadenylated [poly (A) ϩ ] mRNAs, the replication-dependent histone mRNAs, and the Sm class of small nuclear RNAs (snRNAs). The 3Ј ends of these three general classes of RNAs are all formed by cotranscriptional cleavage, but each one has a distinct mechanism for 3Ј-end formation (for reviews, see references 29 and 32). In poly(A) ϩ and histone pre-mRNAs there are conserved upstream and downstream sequences that flank the cleavage site; factors bind to these sites and then recruit additional factors that initiate cleavage (53). In the case of poly(A) ϩ pre-mRNA, the upstream element is the canonical AAUAAA polyadenylation signal (PAS) and the downstream sequence is the G/Urich downstream element (DSE). Recognition of the PAS is carried out by the cleavage and polyadenylation specificity complex (CPSF) component CPSF160 via its RNA recognition motifs (RRM) (36), whereas the DSE is bound by the RRM of the cleavage stimulation factor (CstF) component CstF64 (28). Subsequent to this recognition event is recruitment of additional factors that activate the endonucleolytic cleavage between the PAS and the DSE.Histone pre-mRNA contains a distinct set of flanking elements. Upstream of the cleavage site is a conserved stem-loop structure (SL) and downstream a purine-rich element called the ...

Using a powerful computer-assisted analysis strategy, a large-scale search of small nucleolar RNA (snoRNA) genes in the recently released draft sequence of the rice genome was carried out. This analysis identified 120 different box C/D snoRNA genes with a total of 346 gene variants, which were predicted to guide 135 2'-O-ribose methylation sites in rice rRNAs. Though not exhaustive, this analysis has revealed that rice has the highest number of known box C/D snoRNAs among eukaryotes. Interestingly, although many snoRNA genes are conserved between rice and Arabidopsis, almost half of the identified snoRNA genes are rice specific, which may highlight further the differences in rRNA methylation patterns between monocotyledons and dicotyledons. In addition to 76 singletons, 70 clusters involving 270 snoRNA genes were also found in rice. The large number of the novel snoRNA polycistrons found in the introns of rice protein-coding genes is in contrast to the one-snoRNA-per-intron organization of vertebrates and yeast, and of Arabidopsis in which only a few intronic snoRNA gene clusters were identified. Furthermore, due to a high degree of gene duplication, rice snoRNA genes are clearly redundant and exhibit great sequence variation among isoforms, allowing generation of new snoRNAs for selection. Thus, the large snoRNA gene family in plants can serve as an excellent model for a rapid and functional evolution.

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Our previous study showed that cobalt chloride (CoCl2) could induce PC12 cell apoptosis and that the CoCl2-treated PC12 cells may serve as a simple in vitro model for the study of the mechanism of hypoxia-linked neuronal disorders. The aim of this study is to elucidate the mechanism of CoCl2-induced apoptosis in PC12 cells. Caspases are known to be involved in the apoptosis induced by various stimuli in many cell types. To investigate the involvement of caspases in CoCl2-induced apoptosis in PC12 cells, we generated PC12 cells that stably express the viral caspases inhibitor gene p35 and analyzed the effect of p35 on the process of apoptosis induced by CoCl2. We also examined the effect of cell-permeable peptide inhibitors of caspases. The results showed that the baculovirus p35 gene and the general caspases inhibitor Z-VAD-FMK significantly block apoptosis induced by CoCl2, confirming that caspase is involved in CoCl2-induced apoptosis. Further investigation showed that in this process the caspase-3-like activity is increased, as indicated by the cells' ability to cleave the fluorogenic peptide substrate Ac-Asp-Glu-Val-Asp-7-AMC and to degrade the DNA-repairing enzyme poly-(ADP-ribose) polymerase (PARP), an endogenous caspase-3 substrate. At the same time, caspase-3-specific inhibitors, namely, the peptide Ac-DEVD-CHO, Ac-DEVD-FMK, partially inhibit CoCl2-induced apoptosis. These findings suggested that caspase-3 or caspase-3-like proteases are involved in the apoptosis induced by CoCl2 in PC12 cells. Additionally, we have observed that another apoptotic marker, p38 mitogen-activated protein kinase (MAPK), is significantly activated in this process in a time-dependent manner and that a selective p38 MAPK inhibitor, SB203580, partially inhibits this cell death. The addition of SB203580 also partially suppresses caspase-3-like activity. All these results confirm that the CoCl2-treated PC12 cell is a useful in vitro model with which to study hypoxia-linked neuronal disorders. Furthermore, the results showing that the baculovirus p35 gene and caspase inhibitors possess a remarkable ability to rescue PC12 cells from CoCl2-induced cell death may have implications for future neuroprotective therapeutic approaches for the hypoxia-associated disorders.

Background The gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible biological basis of psychiatric disorders. With the advance of next-generation sequencing technology, characterization of the gut microbiota in schizophrenia (SZ) patients can provide rich clues for the diagnosis and prevention of SZ. Methods In this study, we compared the differences in the fecal microbiota between 82 SZ patients and 80 demographically matched normal controls (NCs) by 16S rRNA sequencing and analyzed the correlations between altered gut microbiota and symptom severity. Results The alpha diversity showed no significant differences between the NC and SZ groups, but the beta diversity revealed significant community-level separation in microbiome composition between the two groups (pseudo-F =3.337, p < 0.001, uncorrected). At the phylum level, relatively more Actinobacteria and less Firmicutes (p < 0.05, FDR corrected) were found in the SZ group. At the genus level, the relative abundances of Collinsella, Lactobacillus, Succinivibrio, Mogibacterium, Corynebacterium, undefined Ruminococcus and undefined Eubacterium were significantly increased, whereas the abundances of Adlercreutzia, Anaerostipes, Ruminococcus and Faecalibacterium were decreased in the SZ group compared to the NC group (p < 0.05, FDR corrected). We performed PICRUSt analysis and found that several metabolic pathways differed significantly between the two groups, including the Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism, Phenylpropanoid biosynthesis, Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways (p < 0.05, FDR corrected). Among the SZ group, the abundance of Succinivibrio was positively correlated with the total Positive and Negative Syndrome Scale (PANSS) scores (r = 0.24, p < 0.05, uncorrected) as well as the general PANSS scores (r = 0.22, p < 0.05, uncorrected); Corynebacterium was negatively related to the negative scores of PANSS (r = 0.22, p < 0.05, uncorrected). Conclusions Our findings provided evidence of altered gut microbial composition in SZ group. In addition, we found that Succinvibrio and Corynebacterium were associated with the severity of symptoms for the first time, which may provide some new biomarkers for the diagnosis of SZ.

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Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity.