Month: November 2020

Toll-like receptors, an important family of the PRRs which are well characterized in vertebrates, initiate a signaling cascade which leads to activation of the Myeloid differentiation factor 88 and the transcription factor nuclear factorkappaB. MyD88 consists of a Toll/interleukin-1 receptor domain and a death domain and is the common signaling adaptor protein shared by all TLRs except TLR3. The TIR domain is essential to the interactions between TLRs and MyD88. The death domain, in turn, associates with the death domain of interleukin-1 receptor associated kinase, to trigger downstream signaling cascades that lead to the activation of the NF-kB. MyD88 was first identified in 1990 and has been extensively studied in many species, including human, porcine, mouse, chicken, reptiles, fish, scallop and flies. To date, variants of MyD88 have been reported only in several vertebrates such as humans, mice and chicken. Recent studies have shown that MyD88 genes are duplicated in common carp. Although the MyD88 gene has been sequenced in diplostraca and anostraca little is known about its extence in in penaeidae. The Pacific white shrimp, Litopenaeus vannamei, is distributed along the Pacific coast ranging from the Gulf of California to the northern Peru and has become one of the most important economic penaeid shrimps worldwide, particularly in the Eastern Pacific region and Asia. With rapid expansion of farming, bacterial and viral diseases have become a major concern, causing substantial economic losses in many countries since the 1990s. For example, the white spot syndrome virus is one of the most common and destructive pathogen and is able to cause 100% mortality within 3 days to 10 days after infection. Study on the L. vannamei CHIR-99021 immune system is much needed in order to design better strategies for disease prevention and control. Our previous studies suggest that a signaling cascade similar to the TLR/MyD88/Tube/Pelle/TRAF6/NF-kB pathway may exist in L. vannamei, and could be activated by wsv449, to upregulate the expression of wsv069, wsv303, and wsv371. Several key genes in this pathway, including the Toll genes, the Pelle gene, the TRAF6 gene, and the Rel/NF-kB homologous genes, have been characterized. In this study, two variants of the MyD88 gene were identified in L. vannamei and their function was studied in signal transduction in response to different stimuli. To better understand the roles of LvMyD88 in response to exposure to various potential pathogens in L. vannamei, the expression of LvMyD88 was investigated in hemocytes after stimulation with the ligands of different TLRs, gram-negative bacterium V. parahaemolyticus, gram-positive bacterium S. aureus and viral pathogen WSSV. After poly I:C stimulation, the transcriptional level of the LvMyD88 was lower than that of the control group at all time points except 4 h and 12 h. LvMyD88 was upregulated after stimulation with LPS and CpG-ODN2006, V. parahaemolyticus, S. aureus and WSSV. Particularly, LvMyD88 were strongly upregulated after LPS and S. aureus challenged. Probably due to the differences in the TLRs involved in recognizing the different PAMPs involved in each stimulation experiment. All the results suggested that LvMyD88 may play a role in innate immune in L. vannamei. In Drosophila, it is known that the Toll pathway is central to host anti-bacterial and anti-viral response by regulating the expression of the immune related genes, including AMPs.

Adipose tissue is central to lipid regulation, facilitating both the storage of fatty acids as neutral lipids within the lipid droplets of adipocytes, and regulating the release of fatty acids in response to both acute and DAPT chronic stimuli. In metabolic disorders these essential functions of adipose tissue are compromised. Determining the cellular mechanisms underlying the dysregulation of adipocytes is fundamentally important to understanding adipose tissue regulation and metabolism. The mobilization of fatty acids from adipose tissue in vivo is regulated by specific mechanisms. Catecholamines acutely stimulate lipolysis through the activation of betaadrenergic receptors at the adipocyte cell surface. This results in the activation of a well characterized cAMPdependent, G-protein coupled signal transduction cascade culminating with the phosphorylation and activation of proteins at the surface of LDs by protein kinase A, including the major structural protein in the adipocyte LD, perilipin A, and the primary diacylglycerol lipase, hormone-sensitive lipase. During fasting the mobilization of fatty acids can be chronically activated through a combination of increased adrenaline and glucagon and reduced levels of insulin. In addition cytokines such as tumor necrosis factor and interleukin-6 have also been shown to promote lipolysis both in vitro and in vivo. Together these data suggest that CAV1 and caveolae play pleiotropic roles in adipose tissue regulation and function. These roles are likely to include general regulatory mechanisms such as signaling and lipid transfer, together with context specific roles related to the adipose tissue microenvironment or specific metabolic challenges. Non-Small cell Lung Cancer is the most common type of lung cancer and is a result of accumulated molecular alterations leading to deregulation of several cellular processes including cell cycle control. In NSCLC, several cell-cycle regulators that play a critical role in cell cycle check point controls are altered, which allows the cancer cells bypass different checkpoints, especially at G1/S and G2/M with subsequent uncontrolled cellular proliferation. Cell division cycle 25A is a member of the CDC25 family of dual specific phosphatases and plays a critical role in cell cycle progression. CDC25A functions to remove the inhibitory phosphates from threonine and tyrosine residues in the ATP-binding sites of CDKs, promoting cell cycle progression. CDC25A is also a downstream target of Chk1-mediated checkpoint pathway: activation of Chk1 by DNA damaging conditions targets CDC25A for proteasome degradation, which prevents cells with chromosomal abnormalities from progressing through the cell cycle. While CDK1 plays a critical role for CDC25A stabilization during mitosis. CDC25A is frequently overexpressed in cancers including NSCLC. This overexpression is associated with a more aggressive clinical behavior and inferior survival. Though CDC25A has been extensively studied for its role in tumor progression and as a potential target for cancer treatment, the mechanisms of CDC25A overexpression in cancer remains to be investigated. Some studies have shown that overexpression of CDC25A in cancers could result from post-transcriptional deregulations such as overexpression of DUB3 ubiquitin hydrolase, inactivation of glycogen synthase kinase-3beta, which phosphorylates CDC25A to promote its proteolysis in early cellcycle phases.

Traditional and classical methods of genomics and microbiology allow researchers to study an individual microbial species obtained from the environment by isolating the organism into pure colonies using microbial culture techniques. However, this approach cannot capture the structure of the broader microbial community within the environmental sample, the relative representation of multiple genomes, and their interaction with each other and with the environment. Additionally, a large number of microbial species are very difficult, or impossible, to culture in vitro in the laboratory setting. The development of nextgeneration sequencing has advanced the field of metagenomics, thereby bypassing the need for microbial isolation through culturing. In a metagenomic experiment, a Niltubacin HDAC inhibitor sample is usually taken from a natural or a host-associated environment containing micro-organisms organized into communities or microbiomes. DNA is extracted from the environmental sample containing a mixture of multiple genomes and then sequenced without prior separation. The resulting dataset comprises millions of mixed sequence reads from the multiple genomes contained in the sample. Traditionally, DNA has been sequenced using Sanger sequencing technology and the reads generated are routinely 800–1000 base pairs long. However this technology is extremely cumbersome and costly. Recently next-generation sequencers, e.g., Illumina/Solexa, Applied Biosystems’ SOLiD, and Roche’s 454 Life Sciences sequencing systems, have emerged as the future of genomics with incredible ability to rapidly generate large amounts of sequence data. These new technologies greatly facilitate highthroughput while lowering the cost of metagenomic studies. However, the reads generated are of much shorter length making reads assembly and alignment more challenging. For example, Illumina/Solexa and SOLiD generate reads ranging between 35–100 base pairs while Roche 454 reads are approximately 100–400 base pairs in length. One goal of metagenomic studies is to identify what genomes are contained in the environmental sample and to estimate their relative abundance. Identification of genomes is complicated by the mixed nature of multiple genomes in the sample. A widely used approach is assigning the sequence reads to NCBI’s taxonomy tree based on sequence read homology alignment with known sequences catalogued in reference databases. The sequence reads are first aligned to the reference sequence databases using a sequence comparison program such as BLAST. Reads which have hits in the database are then assigned to the taxonomy tree based on the best match or multiple high-scoring hits. The challenge of this approach is that hits may be found in multiple genomes for a single read at a given threshold of bit-score or Expect value, due to sequence homology and overlaps associated with similarity among species. Strategy of weighting similarities for multiple BLAST hits has been used to estimate the relative genomic abundance and average size. Another representative and stand-alone analysis tool, MEGAN, assigns a read with hits in multiple genomes to their lowest common ancestor in the NCBI taxonomy tree. Thus assignments of reads to different ranks of taxonomy tree depend on what threshold for bit-score or Expect value is used. Furthermore, MEGAN assigns reads one at a time. As a consequence, the results have less false positives but lack specificity.