Biology subjects

Guo, Z.

Publications and source records attributed to Guo, Z..

256 records · Page 15Linked to original sources

bioRxiv2017

Effects of mestranol on estrogen receptors expression in zebrafish

The estrogen receptor (ER) genes, which encode a group of important ligand-activated transcriptional factors, can modulate estrogen-target gene activities. Zebrafish (Danio rerio) have three ER receptor genes, esr1, esr2a, and esr2b. In this study, we examined the mRNA expression levels of these ER receptors after treatment with mestranol (EE3ME). Zebrafish larvae were exposed to 0.01, 0.1, 1, and 10 mg/L from 6 hours post-fertilization (hpf) and the mRNA expression levels of the ER genes were determined at 24, 48, 72 and 96 hpf. Treatment with mestranol led to a significant stimulation of esr1 mRNA expression at lower concentration and reached maximum at 72 hpf, however, the esr1 mRNA levels were reduced at higher mestranol concentration during exposure. The gene expression of esr2b was markedly decreased and the esr2a remained unaffected at all concentration in the duration. Altogether, these results suggested mestranol might cause the disruption of endocrine activities in fish by mediating ER genes expression.

pharmacology and toxicology↗

bioRxiv2017

Early biomarker discovery in urine of Walker 256 subcutaneous rat model

Despite advances in cancer treatments, early detection of cancer is still the most promising way to improve outcomes. Without homeostatic control, urine reflects early changes in the body and can potentially be used for early cancer diagnosis. In this study, a Walker 256 tumor rat model was established by subcutaneous injection of Walker 256 tumor cells. To identify urinary proteome changes during cancer development, urine samples from Walker 256 tumor-bearing rats were collected at five time points corresponding to before cancer cell implantation, before tumor mass palpability, at tumor mass appearance, during rapid tumor growth, and at cachexia. The urinary protein patterns changed significantly as the tumors progressed, as measured using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The urinary proteome of tumor-bearing rats was identified using Fusion Lumos mass spectrometry with label-free quantification. Then, 30 dynamically changed urinary proteins during cancer progression were selected as more reliable cancer biomarkers, and they were validated by targeted proteomics. Combined with the results of label-free and targeted proteome quantification, a total of 10 urinary proteins (HPT, APOA4, CO4, B2MG, A1AG, CATC, VCAM1, CALB1, CSPG4, and VTDB) changed significantly even before a tumor mass was palpable, and these early changes in urine could also be identified with differential abundance at late stages of cancer. Our study indicated that urine is a sensitive biomarker source for early detection of cancer.

biochemistry↗

bioRxiv2017

A Multi-Parameter Analysis of Cellular Coordination of Multiple Transcriptome Regulation Mechanisms

To understand cellular coordination of multiple transcriptome regulation mechanisms, we simultaneously measured transcription rate (TR), mRNA abundance (RA) and translation activity (TA). This revealed multiple quantitative insights. First, the genomic profiles of the three parameters are systematically different in key statistical features. Sequentially more genes exhibit extreme low or high expression values from TR to RA, then to TA. That is, because of cellular coordination of these regulatory mechanisms, sequentially higher levels of gene expression selectivity are achieved as genetic information flow from the genome to the proteome. Second, the contribution of the stabilization-by-translation regulatory mechanism to the cellular coordination process was assessed. The data enabled an estimation of mRNA stability, revealing a moderate but significant positive correlation between the estimated mRNA stability and translation activity. Third, the proportion of a mRNA occupied by un-translated regions (UTR) exhibits a negative relationship with the level of this correlation, and is thus a major determinant of the mode of regulation of the mRNA. High-UTR-proportion mRNAs tend to defy the stabilization-by-translation regulatory mechanism, staying out of the polysome but remaining stable; mRNAs with little UTRs largely follow this regulation. In summary, we quantitatively delineated the relationship among multiple transcriptome regulation parameters, i.e., cellular coordination of corresponding regulatory mechanisms.

systems biology↗

bioRxiv2016

Identification of new bacterial type III secreted effectors with a recursive Hidden Markov Model profile-alignment strategy

To identify new bacterial type III secreted effectors is computationally a big challenge. At least a dozen machine learning algorithms have been developed, but so far have only achieved limited success. Sequence similarity appears important for biologists but is frequently neglected by algorithm developers for effector prediction, although large success was achieved in the field with this strategy a decade ago. In this study, we propose a recursive sequence alignment strategy with Hidden Markov Models, to comprehensively find homologs of known YopJ/P full-length proteins, effector domains and N-terminal signal sequences. Using this method, we identified 155 different YopJ/P-family effectors and 59 proteins with YopJ/P N-terminal signal sequences from 27 genera and more than 70 species. Among these genera, we also identified one type III secretion system (T3SS) from Uliginosibacterium and two T3SSs from Rhizobacter for the first time. Higher conservation of effector domains, N-terminal fusion of signal sequences to other effectors, and the exchange of N-terminal signal sequences between different effector proteins were frequently observed for YopJ/P-family proteins. This made it feasible to identify new effectors based on separate similarity screening for the N-terminal signal peptides and the effector domains of known effectors. This method can also be applied to search for homologues of other known T3SS effectors.

bioinformatics↗