Biology subjects

Zhou, N.

Publications and source records attributed to Zhou, N..

9 recordsLinked to original sources

bioRxiv2018

Comparison of tri-exponential decay vs. bi-exponential decay and full fitting vs. segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI

PurposeTo determine whether bi- or tri-exponential models, and full or segmented fittings, better fit IVIM imaging signal of healthy livers.\n\nMaterials and methodsDiffusion-weighted images were acquired with a 3-T scanner using respiratory-triggered echo-planar sequence and 16 b-values (0[~]800 s/mm2). Eighteen healthy volunteers had liver scanned twice in the same session, and then once again in another session. Region of interest (ROI)-based measurements were processed with bi-exponential model full fitting and segmented fitting (threshold b-value = 80 s/mm2), as well as tri-exponential model full fitting and segmented fitting (threshold b-value = 200 s/mm2).\n\nResultsWith all scans signal averaged, bi-exponential model full fitting showed Dslow=1.14, Dfast=193.6x10-3 mm2/s, and PF=16.9%, and segmented fitting showed Dslow=1.03, Dfast=56.7x10-3 mm2/s, and PF=21.3%. IVIM parameters derived from tri-exponential model were similar for full fitting and segmented fitting, with a slow (Dslow=0.98x10-3 mm2/s; Fslow=76.4 or 76.6%), a fast (Dfast=15.1 or 15.4x10-3 mm2/s; Ffast=11.8 or 11.7%) and a very fast (DVfast=445.0 or 448.8x10-3 mm2/s; FVfast=11.8 or 11.7 %) diffusion compartments. Tri-exponential model provided an overall better fit than bi-exponential model. For bi-exponential model, full fitting provided better fit at very low and low b-values compared with segmented fitting with the later tended to underestimate Dfast, however, segmented method demonstrated lower error in signal prediction for high b-values. Compared with full fitting, tri-exponential segmented fitting offered better scan-rescan reproducibility.\n\nConclusionFor healthy liver, tri-exponential modelling is preferred than bi-exponential modelling. For bi-exponential model, segmented fitting underestimates Dfast, but offers more accurate estimation of Dslow.

biophysics↗

bioRxiv2018

RUNX proteins desensitize multiple myeloma to lenalidomide via protecting IKZFs from degradation

Ikaros family zinc finger protein 1 and 3 (IKZF1 and IKZF3) are transcription factors that promote multiple myeloma (MM) proliferation. The immunomodulatory imide drug (IMiD) lenalidomide promotes myeloma cell death via Cereblon (CRBN)-dependent ubiquitylation and proteasome-dependent degradation of IKZF1 and IKZF3. Although IMiDs have been used as first-line drugs for MM, the overall survival of refractory MM patients remains poor and demands the identification of novel agents to potentiate the therapeutic effect of IMiDs. Using an unbiased screen based on mass spectrometry, we identified the Runt-related transcription factor 1 and 3 (RUNX1 and RUNX3) as interactors of IKZF1 and IKZF3. Interaction with RUNX1 and RUNX3 inhibits CRBN-dependent binding, ubiquitylation and degradation of IKZF1 and IKZF3 upon lenalidomide treatment. Inhibition of RUNXs, via genetic ablation or a small molecule (AI-10-104), results in sensitization of myeloma cell lines and primary tumors to lenalidomide. Thus, RUNX inhibition represents a valuable therapeutic opportunity to potentiate IMiDs therapy for the treatment of multiple myeloma.

cancer biology↗

bioRxiv2018

New Drosophila long-term memory genes revealed by assessing computational function prediction methods.

A major bottleneck to our understanding of the genetic and molecular foundation of life lies in the ability to assign function to a gene and, subsequently, a protein. Traditional molecular and genetic experiments can provide the most reliable forms of identification, but are generally low-throughput, making such discovery and assignment a daunting task. The bottleneck has led to an increasing role for computational approaches. The Critical Assessment of Functional Annotation (CAFA) effort seeks to measure the performance of computational methods. In CAFA3 we performed selected screens, including an effort focused on long-term memory. We used homology and previous CAFA predictions to identify 29 key Drosophila genes, which we tested via a long-term memory screen. We identify 11 novel genes that are involved in long-term memory formation and show a high level of connectivity with previously identified learning and memory genes. Our study provides first higher-order behavioral assay and organism screen used for CAFA assessments and revealed previously uncharacterized roles of multiple genes as possible regulators of neuronal plasticity at the boundary of information acquisition and memory formation.

neuroscience↗

bioRxiv2018

Niacin fine-tunes energy homeostasis through canonical GPR109A signaling

Niacin has long been considered as a high-potency drug for beneficially treating lipid abnormalities, however, its anti-atherosclerotic effects have been challenged by recent studies. Here, we demonstrated that oral supplementation of niacin resulted in a significant reduction in body weight and fat mass without affecting food intake in high-fat diet-fed wild-type mice, but not in GPR109A-defeicient mice. Further investigation showed that niacin challenge led to a remarkable inhibition of hepatic lipogenesis via a GPR109A-dependent ERK1/2/AMPK pathway. Additionally, we demonstrated that niacin treatment stimulated thermogenesis in brown adipose tissue by induction of thermogenic genes via GPR109A. Moreover, we observed that mice exposed to niacin exhibited a dramatic decrease in intestinal absorption of fatty acids. Together, our data demonstrate that acting on GPR109A, niacin shows the potential to maintain energy homeostasis by fine-tuning hepatic lipogenesis, BAT/beige thermogenesis and intestinal fat absorption, representing a potential approach to the treatment of lipid abnormalities.

cell biology↗

bioRxiv2018

Using molecular ecological network analysis to explore the effects of chemotherapy on intestinal microbial communities of colorectal cancer patients

Intestinal microbiota is now widely known to be key roles in the nutrition uptake, metabolism, and the regulation of human immune responses. However, we do not know how changes the intestinal microbiota in response to the chemotherapy. In this study, we used network-based analytical approaches to explore the effects of five stages of chemotherapy on the intestinal microbiota of colorectal cancer patients. The results showed that chemotherapy greatly reduced the alpha diversity and changed the specie-specie interaction networks of intestinal microbiota, proved by the network size, network connectivity and modularity. The OTU167 and OTU8 from the genus Fusobacterium and Bacteroides were identified as keystone taxa by molecular ecological networks in the first two stages of chemotherapy, and were significantly correlated with tumor makers (P < 0.05). Five stages of chemotherapy did not make the intestinal micro-ecosystem regain a steady state, because of the lower alpha diversity and more complicated ecological networks compared to the healthy individuals. Furthermore, combing the changes of ecological networks with the tumor markers, the intestinal microbiota was closely linked with clinical chemotherapeutic effects.\n\nImportanceA deeply understanding of the role of intestinal microbiota contributes to help us find path forward for improving the prognosis of colorectal cancer patients. In addition, diet or probiotics interventions will be a possible attempt to improve the clinical chemotherapeutic effects for colorectal cancer patients.

microbiology↗

bioRxiv2018

Role of anterograde motor Kif5b in clathrin-coated vesicle uncoating and clathrin-mediated endocytosis

Kif5b-driven anterograde transport and clathrin-mediated endocytosis (CME) are responsible for opposite intracellular trafficking, contributing to plasma membrane homeostasis. However, whether and how the two trafficking processes coordinate remain unclear. Here, we show that Kif5b directly interacts with clathrin heavy chain (CHC) at a region close to that for uncoating catalyst (Hsc70) and preferentially localizes on large clathrin-coated vesicles (CCVs). Uncoating in vitro is decreased for CCVs from the cortex of kif5b conditional knockout (mutant) mouse and facilitated by adding CHC-binding Kif5b fragments, while cell peripheral distribution of CHC or Hsc70 keeps unaffected by Kif5b depletion. Furthermore, cellular entry of vesicular stomatitis virus that internalized into large CCV is inhibited in cells by Kif5b depletion or introducing a dominant-negative Kif5b fragment. These findings showed a new role of Kif5b in CCV uncoating and CME, indicating Kif5b as a molecular knot connecting anterograde transport to CME.

cell biology↗

bioRxiv2018

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.\n\nAuthor SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, but they require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel - the male flower of the corn plant - from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.

plant biology↗

bioRxiv2017

IVIM parameters have good scan-rescan reproducibility when evidential motion contaminated and poorly fitted image data are removed

BackgroundIntravoxel Incoherent Motion (IVIM) diffusion MRI is a promising technique for liver pathology evaluation, but this techniques scan-rescan reproducibility has been reported to be unsatisfactory.\n\nObjectiveTo understand whether IVIM MRI parameters for liver parenchyma can be good after removal of motion contaminated and/or poorly fitted image data.\n\nMaterial and MethodsEighteen healthy volunteers had liver scanned twice at the same session to assess scan-rescan repeatability, and again in another session after an average interval of 13 days to assess reproducibility. Diffusion weighted image were acquired with a 3T scanner using respiratory-triggered echo-planar sequence and 16 b-values (0 to 800 s/mm2). Measurement was performed on the right liver with segmented-unconstrained least square fitting. Image series with evidential anatomical mismatch, apparent artifacts, and poorly fitted signal intensity vs. b-value curve were excluded. A minimum of three slices was deemed necessary for IVIM parameter estimation of a liver.\n\nResultsWith total 54 examinations, 6 scans did not satisfy inclusion criteria, leading to a success rate of 89%; and 14 volunteers were finally included. With each scan a mean of 5.3 slices (range: 3-10 slices) were utilized for analysis. Using threshold b-value=80s/mm2, the coefficient of variation and within-subject coefficient of variation for repeatability and reproducibility were: 2.86% and 4.24% for Dslow, 3.81% and 4.24%, for PF, 18.16% and 24.88% for Dfast; and those for reproducibility were 2.48% and 3.24% for Dslow; 4.91% and 5.38% for PF; 21.18% and 30.89% for Dfast.\n\nConclusionIVIM parameter scan-rescan reproducibility can be potentially good.

biophysics↗

bioRxiv2017

Pan-cancer scale landscape of simple somatic mutations

Genome is the carrier of somatic mutations during the development of cancer. The catalogue of simple somatic mutations (SSM) is a subgroup of somatic mutations. It includes single base substitutions, small deletions and insertions of <= 200 bp, and multiple base substitutions of <= 200 bp. The comprehensive landscape of SSM has not been studied. After analysed 46,692,922 SSM of 10,878 samples, we proposed a pan-cancer scale landscape of SSM for 60 cancer projects in ICGC. In addition, the whole genome sequencing (WGS) and whole exome sequencing (WXS) techniques were compared according to the landscape of SSM. The result indicates numbers of SSM vary dramatically in different cancers. WGS can detect 10 times more single base substitutions and insertions than WXS. In terms of WXS, it called 10 times more deletions than insertions. Multiple base substitutions have not been well studied so they were just observed in a few cancer projects. Cancers generally show high prevalence of C > T substitutions at NpCpG trinucleotide contexts. Skin cancer showed distinct mutational spectra. Breast cancer, bladder cancer, and cervical cancer were found to have similar mutational spectra. Acute myeloid leukemia and lung cancer from South Korea, and colorectal cancer from China show high density of single base substitutions per mega base in chromosome Y. To sum up, our study and findings will be thought provoking in studying SSM in cancer.

genomics↗