Biology subjects

Pan, X.

Publications and source records attributed to Pan, X..

194 records · Page 11Linked to original sources

bioRxiv2019

Breeding of a high-yield strain for commercial cultivation by crossing Pholiota adiposa and P. limonella

Pholiota adiposa is an edible mushroom with excellent nutritional and medicinal properties. However, fruiting body yields are low, and the commercial cultivation potential of this fungus is limited. In the present study, 279 crossbred strains were obtained by mono-mono crossing of monokaryotic strains derived from P. adiposa HS5 and P. limonella HS4. Ligninolytic enzymes and mycelial growth rate were used as markers to screen the crossbred strains, and 18 were selected for further analysis. Crossbred strain A10B4 displayed the highest yield, i.e., 165.91 {+/-} 12.56 g per bag, which was 31.34 g and 74.48 g more than that of strains HS5 and HS4, respectively. The mycelial colonization time of A10B4 was 25.18 {+/-} 1.33 days, which was 5.64 days shorter than that of HS5. A10B4 was characterized by inter-simple sequence repeat molecular markers and antagonism tests. Differences in PCR products from parental and crossbred strains were observed. Therefore, the newly developed hybrid strain A10B4, named P. adiposa-limonella HS54 and having a high yield and desirable traits, might be suitable for commercial cultivation.

microbiology↗

bioRxiv2019

RecO impedes RecG-SSB binding to impair the strand annealing recombination pathway in E.coli

Faithful duplication of genomic DNA relies not only on the fidelity of DNA replication itself, but also on fully functional DNA repair and homologous recombination machinery. We report a molecular mechanism responsible for deciding homologous recombinational repair pathways during replication dictated by binding of RecO and RecG to SSB in E.coli. Using a RecG-yfp fusion protein, we found that RecG-yfp foci appeared only in the {Delta}recG, {Delta}recO and {Delta}recA, {Delta}recO double mutants. Surprisingly, foci were not observed in wild-type {Delta}recG, or double mutants where recG and either recF or, separately recR were deleted. In addition, formation of RecG-yfp foci in the {Delta}recO::kanR required wildtype ssb, as ssb-113 could not substitute. This suggests that RecG and RecO binding to SSB is competitive. We also found that the UV resistance of recO alone mutant increased to certain extent by supplementing RecG. In an ssb-113 mutant, RecO and RecG worked following a different pattern. Both RecO and RecG were able to participate in repairing UV damages when grown at permissive temperature, while they could also be involved in making DNA double strand breaks when grown at nonpermissive temperature. So, our results suggested that differential binding of RecG and RecO to SSB in a DNA replication fork in Escherichia coli.may be involved in determining whether the SDSA or DSBR pathway of homologous recombinational repair is used.\n\nAuthor summarySingle strand DNA binding proteins (SSB) stabilize DNA holoenzyme and prevent single strand DNA from folding into non-B DNA structures in a DNA replication fork. It has also been revealed that SSB can also act as a platform for some proteins working in DNA repair and recombination to access DNA molecules when DNA replication fork needs to be reestablished. In Escherichia coli, several proteins working primarily in DNA repair and recombination were found to participate in DNA replication fork resumption by physically interacting with SSB, including RecO and RecG etc. However the hierarchy of these proteins interacting with SSB in Escherichia coli has not been well defined. In this study, we demonstrated a differential binding of RecO and RecG to SSB in DNA replication was used to establish a RecO-dependent pathway of replication fork repair by abolishing a RecG-dependent replication fork repair. We also show that, RecG and RecO could randomly participate in DNA replication repair in the absence of a functional SSB, which may be responsible for the generation of DNA double strand breaks in an ssb-113 mutant in Escherichia coli.

molecular biology↗

bioRxiv2019

Effects of simulated acid rain on soil respiration and microbial community in a Moso bamboo forest in subtropical China

Acid rain has been regarded as a global environmental concern due to its negative effects on global ecosystems. In this study, we investigated the effects of simulated acid rain (SAR) on soil respiration rate and soil bacterial diversity in a Moso bamboo (phyllostachyspubescens) forest in subtropical China. Experimental results showed a similar seasonal pattern of soil respiration rates underdifferent SAR treatments. Seasonal mean soil respiration rates for CK (control, deionized water, pH 6.7), T1 (pH 5.6), T2 (pH 4.0) and T3 (pH 2.5) treatments were 3.44, 4.80, 4.35 and 4.51 mol m-2 s-1, respectively. One-way analysis of variance indicated that the SAR exposure had no significant effect on soil respiration (p>0.1) and soil microbial biomass (p>0.1). Soil bacterial community diversity was calculated as the Shannon-Wiener diversity index and the results showed that only T3 treatment had significant effects on soil bacterial diversity. The DGGE analysis results revealed that T1 and CK soils had closer association and were related to the T2 soil, while T3 soil was distinctly different from the other treatments. This work highlights that the effects of SAR are important to consider in assessing the soil respiration rate, particularly under the scenario of increasing acid rain pollution.

ecology↗

bioRxiv2019

Inferring disease-associated microRNAs using semi-supervised multi-label graph convolutional networks

MicroRNAs (miRNAs) play crucial roles in many biological processes involved in diseases. The associations between diseases and protein coding genes (PCGs) have been well investigated, and further the miRNAs interact with PCGs to trigger them to be functional. Thus, it is imperative to computationally infer disease-miRNA associations under the context of interaction networks.\n\nIn this study, we present a computational method, DimiG, to infer miRNA-associated diseases using semi-supervised Graph Convolutional Network model (GCN). DimiG is a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations and tissue expression profiles. DimiG is trained on disease-PCG associations and a graph constructed from interaction networks of PCG-PCG and miRNA-PCG using semi-supervised GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set collected from verified disease-miRNA associations. Our results demonstrate that the new DimiG yields promising performance and outperforms the best published baseline method not trained on disease-miRNA associations by 11% and is also superior to two state-of-the-art supervised methods trained on disease-miRNA associations. Three case studies of prostate cancer, lung cancer and Inflammatory bowel disease further demonstrate the efficacy of DimiG, where the top miRNAs predicted by DimiG for them are supported by literature or databases.

bioinformatics↗

bioRxiv2019

Motoneuron-derived Activinβ regulates Drosophila body size and tissue-scaling during larval growth and adult development

Correct scaling of body and organ size is crucial for proper development and survival of all organisms. Perturbations in circulating hormones, including insulins and steroids, are largely responsible for changing body size in response to both genetic and environmental factors. Such perturbations typically produce adults whose organs and appendages scale proportionately with final size. The identity of additional factors that might contribute to scaling of organs and appendages with body size is unknown. Here we report that loss-of-function mutations in Drosophila Activin{beta} (Act{beta}), a member of the TGF-{beta} superfamily, lead to production of small larvae/pupae and undersized rare adult escapers. Morphometric measurements of escaper adult appendage size (wings, legs), as well as heads, thoraxes, and abdomens, reveal a disproportional reduction in abdominal size compared to other tissues. Similar size measurements of selected Act{beta} mutant larval tissues demonstrate that somatic muscle size is disproportionately smaller when compared to fat body, salivary glands, prothoracic glands, imaginal discs and brain. We also show that Act{beta} control of body size is dependent on canonical signaling through the transcription-factor dSmad2 and that it modulates the growth rate, but not feeding behavior, during the third instar period. Tissue and cell-specific knockdown and overexpression studies reveal that motoneuron derived Act{beta} is essential for regulating proper body size and tissue scaling. These studies suggest that, unlike in vertebrates where Myostatin, and certain other Activin-like factors act as systemic negative regulators of muscle mass, in Drosophila Act{beta} is a positive regulator of muscle mass that is directly delivered to muscles by motoneurons. We discuss the importance of these findings in coordinating proportional scaling of insect muscle mass to appendage size.

developmental biology↗

bioRxiv2019

Fecal microbiota as a non-invasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver

Iron is an essential trace mineral for the growth, systemic metabolism, and immune response. Imbalance of tissue iron absorption and storage leads to various diseases. The excessive iron accumulation is associated with inflammation and cancer while iron deficiency leads to growth retardation. Studies investigated in Kenyan infants and school children suggests that both low and high iron intake result in dysbiosis of gut microbiota. This would lead to the disruption of microbial diversity, an increase of pathogen abundance and the induction of intestinal inflammation. Despite this progress, in-depth studies investigating the relationship between iron availability and gut microbiota is not completely explored. In the current study, we established a murine model to study the connection between iron and microbiota by feeding mice with either iron-deprived or -fortified diet. To identify key microbiota related to iron levels, we combined the 16S rRNA amplicon sequencing with the innovated bioinformatic algorithms, such as RDA, co-occurrence, and machine learning to identify key microbiota. Manipulation of iron levels in the diet leads to systemic iron dysregulation and dysbiosis of gut microbiota. The bioinformatic algorithms used here detect five key bacteria that correlate with systemic iron levels. Leveraging on these key microbiotas, we also established a prediction model which could precisely distinguish the individual under either iron-deprived or iron-fortified physiological condition to further prove the link between microbiota and systemic iron homeostasis. This innovated and non-invasive approach could be potentially used for the early diagnosis and therapy of iron-dysregulation related diseases, e.g. anemia, inflammatory disease, fibrosis, and cancers.

microbiology↗

bioRxiv2018

Pleistocene Mammal Population Fluctuation Patterns Inferred by Their Genomes

Paleoclimate fluctuations critically affect paleoecological systems and influence mammal populations, even resulting in population differentiation [1]. Historical effective population size (Ne) can reflect these influences [2, 3]. Dozens of recent studies have investigated the relationship between variations in Ne values of one or a small number of mammalian species, inferred from genomic data, and fluctuations in paleoclimate [4-7]. However, there lacks an integrated and comprehensive study on the relationship between the fluctuations in paleoclimate and variations in Ne values inferred from genome sequencing data of a wide range of mammals. To investigate patterns in mammalian Ne values during the the Pleistocene, we gathered whole genome sequencing data of 60 mammals from 35 species distributed across Afro-Eurasia and the Americas, then inferred their Ne curves using the Pairwise Sequentially Markovian Coalescent (PSMC) method; 30 mammalian Ne curves almost simultaneously started to contract at the turning point of the Middle Pleistocene Transition (MPT); then the population of seven mammals started to expand at the turning point of the Middle Brunhes Event (MBE), while the contraction of other mammals populations was prolonged to the later different time periods. Eight mammals experienced a severe population contraction around the Last Glaciation Maximum, as some aves did [8], while four potential ruminant beneficiaries showed an expanding population. Sus scrofa and Bos taurus experienced an internal population differentiation in the MPT. To conclude, the phenomenon that critical paleoclimate events facilitated contemporaneous animal population fluctuations in the paleoecological system is showed by our Ne curve analysis.

bioinformatics↗

bioRxiv2018

Predicting circRNA-RBP interaction sites using a codon-based encoding and hybrid deep neural networks

Circular RNAs (circRNAs), with their crucial roles in gene regulation and disease development, have become a rising star in the RNA world. A lot of previous wet-lab studies focused on the interaction mechanisms between circRNAs and RNA-binding proteins (RBPs), as the knowledge of circRNA-RBP association is very important for understanding functions of circRNAs. Recently, the abundant CLIP-Seq experimental data has made the large-scale identification and analysis of circRNA-RBP interactions possible, while no computational tool based on machine learning has been developed yet.\n\nWe present a new deep learning-based method, CRIP (CircRNAs Interact with Proteins), for the prediction of RBP binding sites on circRNAs, using only the RNA sequences. In order to fully exploit the sequence information, we propose a stacked codon-based encoding scheme and a hybrid deep learning architecture, in which a convolutional neural network (CNN) learns high-level abstract features and a recurrent neural network (RNN) learns long dependency in the sequences. We construct 37 datasets including sequence fragments of binding sites on circRNAs, and each set corresponds to one RBP. The experimental results show that the new encoding scheme is superior to the existing feature representation methods for RNA sequences, and the hybrid network outperforms conventional classifiers by a large margin, where both the CNN and RNN components contribute to the performance improvement. To the best of our knowledge, CRIP is the first machine learning-based tool specialized in the prediction of circRNA-RBP interactions, which is expected to play an important role for large-scale function analysis of circRNAs.

bioinformatics↗

bioRxiv2018

D-GPM: a deep learning method for gene promoter methylation inference

BackgroundGene promoter methylation plays a critical role in a wide range of biological processes, such as transcriptional expression, gene imprinting, X chromosome inactivation, etc. Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. Moreover, the methylation level of the promoter is usually negatively correlated with its corresponding gene expression. This result inspired us to propose that the methylation level of the promoters might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling.\n\nResultsHere, we developed a deep learning model (D-GPM) to predict the whole-genome promoter methylation level based on the methylation profile of the landmark genes. We benchmarked D-GPM against three machine learning methods, namely, linear regression (LR), regression tree (RT) and support vector machine (SVM), based on two criteria: the mean absolute deviation (MAE) and the Pearson correlation coefficient (PCC). After profiling the methylation beta value (MBV) dataset from the TCGA, with respect to MAE and PCC, we found that D-GPM outperforms LR by 9.59% and 4.34%, RT by 27.58% and 22.96% and SVM by 6.14% and 3.07% on average, respectively. For the number of better-predicted genes, D-GPM outperforms LR in 92.65% and 91.00%, RT in 95.66% and 98.25% and SVM in 85.49% and 81.56% of the target genes.\n\nConclusionsD-GPM acquires the least overall MAE and the highest overall PCC on MBV-te compared to LR, RT, and SVM. For a genewise comparative analysis, D-GPM outperforms LR, RT, and SVM in an overwhelming majority of the target genes, with respect to the MAE and PCC. Most importantly, D-GPM predominates among the other models in predicting a majority of the target genes according to the model distribution of the least MAE and the highest PCC for the target genes.

bioinformatics↗

bioRxiv2018

Osteoblastic PLEKHO1 contributes to joint inflammation in rheumatoid arthritis

Osteoblasts participating in the inflammation regulation gradually obtain concerns. However, its role in joint inflammation of rheumatoid arthritis (RA) is largely unknown. Pleckstrin homology domain-containing family O member 1 (PLEKHO1) was previously identified as a negative regulator of osteogenic lineage activity. Here we demonstrated that PLEKHO1 was highly expressed in osteoblasts of articular specimens from RA patients and inflammatory arthritis mice. Genetic deletion of osteoblastic Plekho1 ameliorated joint inflammation in mice with collagen-induced arthritis (CIA) and K/BxN serum-transfer arthritis (STA), whereas overexpressing Plekho1 only within osteoblasts in CIA and STA mice demonstrated exacerbated local inflammation. Further in vitro studies indicated that PLEKHO1 was required for TRAF2-mediated RIP1 ubiquitination to activate NF-kB for inducing inflammatory cytokines production in osteoblasts. Moreover, osteoblastic PLEKHO1 inhibition improved joint inflammation and attenuated bone formation reduction in CIA mice and non-human primate arthritis model. These data strongly suggest that highly expressed PLEKHO1 in osteoblast mediates joint inflammation in RA. Targeting osteoblastic PLEKHO1 may exert dual therapeutic action of alleviating joint inflammation and promoting bone formation in RA.

cell biology↗

bioRxiv2017

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

RNA regulation is significantly dependent on its binding protein partner, which is known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized, especially on the structure point of view. Informative signals hiding and interdependencies between sequence and structure specificities are two challenging problems for both predicting RBP binding sites and accurate sequence and structure motifs mining.\n\nIn this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, which are appropriate for subsequent convolution operations. To reveal the hidden binding knowledge from the observations, the CNNs are applied to learn the abstract motif features. Considering the close relationship between sequences and predicted structures, we use the BLSTM to capture the long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets, and the results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage is that iDeepS is able to automatically extract both binding sequence and structure motifs, which will improve our transparent understanding of the mechanisms of binding specificities of RBPs. iDeepS is available at https://github.com/xypan1232/iDeepS.

bioinformatics↗

bioRxiv2017

Attention is required for knowledge-based sequential grouping of syllables into words

How the brain sequentially groups sensory events into temporal chunks and how this process is modulated by attention are fundamental questions in cognitive neuroscience. Sequential grouping includes bottom-up primitive grouping and top-down knowledge-based grouping. In speech perception, grouping acoustic features into syllables can rely on bottom-up acoustic continuity cues but grouping syllables into words critically relies on the listeners lexical knowledge. This study investigates whether top-down attention is required to apply lexical knowledge to group syllables into words, by concurrently monitoring neural entrainment to syllables and words using electroencephalography (EEG). When attention is directed to a competing speech stream or cross-modally to a silent movie, neural entrainment to syllables is weakened but neural entrainment to words largely diminishes. These results strongly suggest that knowledge-based grouping of syllables into words requires top-down attention and is a bottleneck for the neural processing of unattended speech.

neuroscience↗

bioRxiv2017

Malthusian Catastrophe: Species Extinction Caused By Oversized Population

There is one pseudo-extinction debt and four occurring conditions for real extinction debt. Since small and oversized populations have a high extinction risk, Pan threshold (upper limit) was calculated for Verhulst-Pear \"logistic\" growth model and logistic model with the Allee effect, an important parameter corresponding to Allee threshold (lower limit).

ecology↗

bioRxiv2016

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

BackgroundRNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation.\n\nResultsIn viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications.\n\nConclusionThe iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep

bioinformatics↗