A novel HIV-1-encoded microRNA enhances its viral replication by targeting the TATA box region

BACKGROUND: A lot of microRNAs (miRNAs) derived from viral genomes have been identified. Many of them play various important roles in virus replication and virus-host interaction. Cellular miRNAs have been shown to participate in the regulation of HIV-1 viral replication, while the role of viral-encoded miRNAs in this process is largely unknown. RESULTS: In this report, through a strategy combining computational prediction and deep sequencing, we identified a novel HIV-1-encoded miRNA, miR-H3. MiR-H3 locates in the mRNA region encoding the active center of reverse transcriptase (RT) and exhibits high sequence conservation among different subtypes of HIV-1 viruses. Overexpression of miR-H3 increases viral production and the mutations in miR-H3 sequence significantly impair the viral replication of wildtype HIV-1 viruses, suggesting that it is a replication-enhancing miRNA. MiR-H3 upregulates HIV-1 RNA transcription and protein expression. A serial deletion assay suggests that miR-H3 targets HIV-1 5′ LTR and upregulates the promoter activity. It interacts with the TATA box in HIV-1 5′ LTR and sequence-specifically activates the viral transcription. In addition, chemically-synthesized small RNAs targeting HIV-1 TATA box activate HIV-1 production from resting CD4(+) T cells isolated from HIV-1-infected patients on suppressive highly active antiretroviral therapy (HAART). CONCLUSIONS: We have identified a novel HIV-1-encoded miRNA which specifically enhances viral production and provide a specific method to activate HIV-1 latency

WGCNA based identification of hub genes associated with cold response and development in Apis mellifera metamorphic pupae

Honeybee is a crucial pollinator in nature, and plays an indispensable role in both agricultural production and scientific research. In recent decades, honeybee was challenged with health problems by biotic and abiotic stresses. As a key ecological factor, temperature has been proved to have an impact on the survival and production efficiency of honeybees. Previous studies have demonstrated that low temperature stress can affect honeybee pupation and shorten adult longevity. However, the molecular mechanism underlying the effects of low temperatures on honeybee growth and development during their developmental period remain poorly understood. In this paper, the weighted gene co-expression analysis (WGCNA) was employed to explore the molecular mechanisms underpinnings of honeybees’ respond to low temperatures (20°C) during four distinct developmental stages: large-larvae, prepupae, early-pupae and mid-pupae. Through an extensive transcriptome analysis, thirteen gene co-expression modules were identified and analyzed in relation to honeybee development and stress responses. The darkorange module was found to be associated with low temperature stress, with its genes primarily involved in autophagy-animal, endocytosis and MAPK signaling pathways. Four hub genes were identified within this module, namely, loc726497, loc409791, loc410923, and loc550857, which may contribute to honeybee resistance to low temperature and provide insight into the underlying mechanism. The gene expression patterns of grey60 and black modules were found to correspond to the developmental stages of prepupae and early-pupae, respectively, with the hub genes loc409494, loc725756, loc552457, loc726158, Ip3k and Lcch3 in grey60 module likely involved in brain development, and the hub genes loc410555 in black module potentially related to exoskeleton development. The brown module genes exhibited a distinct pattern of overexpression in mid-pupae specimens, with genes primarily enriched in oxidative phosphorylation, citrate cycle and other pathways, which may be related to the formation of bee flying muscle. No related gene expression module was found for mature larvae stage. These findings provide valuable insights into the developmental process of honeybees at molecular level during the capped brood stage

Neglected Effects of Inoculum Preservation on the Start-Up of Psychrophilic Bioelectrochemical Systems and Shaping Bacterial Communities at Low Temperature

Bioelectrochemical systems (BESs) are capable of simultaneous wastewater treatment and resource recovery at low temperatures. However, the direct enrichment of psychrophilic and electroactive biofilms in BESs at 4°C is difficult due to the lack of understanding in the physioecology of psychrophilic exoelectrogens. Here, we report the start-up and operation of microbial fuel cells (MFCs) at 4°C with pre-acclimated inocula at different temperatures (4°C, 10°C, 25°C, and −20°C) for 7 days and 14 days. MFCs with 7-day-pretreated inocula reached higher peak voltages than did those with 14-day-pretreated inocula. The highest power densities were obtained by MFCs with 25°C – 7-day-, 25°C – 14-day-, and 4°C – 7-day-pretreated inocula (650–700 mW/m2). In contrast, the control MFCs with untreated inocula were stable at 450 mW/m2. The power densities of MFCs with 7-day-pretreated inocula were higher than those obtained by MFCs with 14-day-pretreated inocula. The MFCs with 10°C – 7-day-pretreated inocula and the control MFCs showed higher chemical oxygen demand (COD) removal (90–91%) than other MFCs. Illumina HiSeq sequencing based on 16S rRNA gene amplicons indicated that bacterial communities of the anode biofilms were shaped by pretreated inocula at different temperatures. Compared with the control MFCs with untreated inocula, MFCs with temperature-pretreated inocula demonstrated higher microbial diversity, but did not do so with −20°C-pretreated inocula. Principal components analysis (PCA) revealed an obvious separation between the inocula pretreated at 4°C and those pretreated at 10°C, implying that bacterial community structures could be shaped by pretreated inocula at low temperatures. The pretreatment period also had a diverse impact on the abundance of exoelectrogens and non-exoelectrogens in MFCs with inocula pretreated at different temperatures. The majority of the predominant population was affiliated with Geobacter with a relative abundance of 17–70% at different pre-acclimated temperatures, suggesting that the exoelectrogenic Geobacter could be effectively enriched at 4°C even with inocula pretreated at different temperatures. This study provides a strategy that was previously neglected for fast enrichment of psychrophilic exoelectrogens in BESs at low temperatures

Rif1 Maintains Telomere Length Homeostasis of ESCs by Mediating Heterochromatin Silencing

SummaryTelomere length homeostasis is essential for genomic stability and unlimited self-renewal of embryonic stem cells (ESCs). We show that telomere-associated protein Rif1 is required to maintain telomere length homeostasis by negatively regulating Zscan4 expression, a critical factor for telomere elongation by recombination. Depletion of Rif1 results in terminal hyperrecombination, telomere length heterogeneity, and chromosomal fusions. Reduction of Zscan4 by shRNA significantly rescues telomere recombination defects of Rif1-depleted ESCs and associated embryonic lethality. Further, Rif1 negatively modulates Zscan4 expression by maintaining H3K9me3 levels at subtelomeric regions. Mechanistically, Rif1 interacts and stabilizes H3K9 methylation complex. Thus, Rif1 regulates telomere length homeostasis of ESCs by mediating heterochromatic silencing

Metasurface spectrometers beyond resolution-sensitivity constraints

Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum9, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultra-low-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times

A Novel Hierarchical Model in Ensemble Environment for Road Detection Application

As a popular research direction in the field of intelligent transportation, road detection has been extensively concerned by many researchers. However, there are still some key issues in specific applications that need to be further improved, such as the feature processing of road images, the optimal choice of information extraction and detection methods, and the inevitable limitations of detection schemes. In the existing research work, most of the image segmentation algorithms applied to road detection are sensitive to noise data and are prone to generate redundant information or over-segmentation, which makes the computation of segmentation process more complicated. In addition, the algorithm needs to overcome objective factors such as different road conditions and natural environments to ensure certain execution efficiency and segmentation accuracy. In order to improve these issues, we integrate the idea of shallow machine-learning model that clusters first and then classifies in this paper, and a hierarchical multifeature road image segmentation integration framework is proposed. The proposed model has been tested and evaluated on two sets of road datasets based on real scenes and compared with common detection methods, and its effectiveness and accuracy have been verified. Moreover, it demonstrates that the method opens up a new way to enhance the learning and detection capabilities of the model. Most importantly, it has certain potential for application in various practical fields such as intelligent transportation or assisted driving