Biology subjects

Ning, K.

Publications and source records attributed to Ning, K..

52 records · Page 3Linked to original sources

bioRxiv2020

The APETALA2 transcription factor LsAP2 regulates seed shape in lettuce

Seeds are major vehicles of propagation and dispersal in plants. A number of transcription factors, including APETALA2 (AP2), play crucial roles during the seed development process in various plant species. However, genes essential for seed development and the regulatory networks during seed development remain unclear in lettuce. Here, we identified a lettuce AP2 (LsAP2) gene that was highly expressed at the early stages of seed development. LsAP2 knockout plants obtained by the CRISPR/Cas9 system were used to explore the biological function of LsAP2. Compared with wild-type, the seeds of the Lsap2 mutant plants had increased length and decreased width, and developed an extended tip at the seed top. After further investigating the seed structural characteristics of Lsap2 mutant plants, we proposed a new function of LsAP2 in seed dispersal. Moreover, we identified several interactors of LsAP2. Our results showed that LsAP2 directly interacted with the lettuce homolog of BREVIPEDICELLUS (LsBP) and promoted the expression of LsBP. Transcriptome analysis revealed that LsAP2 might also be involved in brassinosteroid biosynthesis and signaling pathways. Taken together, our data indicate that LsAP2 has a significant function in regulating seed shape in lettuce.

plant biology↗

bioRxiv2020

Ontology-Aware Deep Learning Enables Ultrafast, Accurate and Interpretable Source Tracking among Sub-Million Microbial Community Samples from Hundreds of Niches

The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MSTs superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

bioinformatics↗

bioRxiv2020

An Engineered Genetic Circuit for Lactose Intolerance Alleviation Coupled with Gut Microbiota Recovery

BackgroundLactose malabsorption occurs in around 68% of the world populations, causing lactose intolerance (LI) symptoms such as abdominal pain, bloating and diarrhoea. To alleviate LI, previous studies mainly focused on strengthening intestinal {beta}-galactosidase activity but neglected the inconspicuous colon pH drop caused by gut microbes fermentation on non-hydrolysed lactose. The colon pH drop will reduce intestinal {beta}-galactosidase activity and influence the intestinal homeostasis. ResultsHere, we synthesized a tri-stable-switch circuit equipped with high {beta}-galactosidase activity and pH rescue ability. This circuit can switch in functionality between expression of {beta}-galactosidase and expression of l-lactate dehydrogenase in respond to intestinal lactose signal and intestinal pH signal. We confirmed the circuit functionality was efficient using 12-hrs in vitro culture at a range of pH levels, as well as 6-hrs in vivo simulations in mice colon. Moreover, another 21-days mice trial indicated that this circuit can recover lactose-effected gut microbiota of mice to the status (enterotypes) similar to that of mice without lactose intake. ConclusionsTaken together, the tri-stable-switch circuit can serve as a promising prototype for LI symptoms relief, especially by flexibly adapting to environmental variation, stabilizing colon pH and restoring gut microbiota.

bioengineering↗

bioRxiv2020

Long Period Modeling SARS-CoV-2 Infection of in Vitro Cultured Polarized Human Airway Epithelium

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates throughout human airways. The polarized human airway epithelium (HAE) cultured at an airway-liquid interface (HAE-ALI) is an in vitro model mimicking the in vivo human mucociliary airway epithelium and supports the replication of SARS-CoV-2. However, previous studies only characterized short-period SARS-CoV-2 infection in HAE. In this study, continuously monitoring the SARS-CoV-2 infection in HAE-ALI cultures for a long period of up to 51 days revealed that SARS-CoV-2 infection was long lasting with recurrent replication peaks appearing between an interval of approximately 7-10 days, which was consistent in all the tested HAE-ALI cultures derived from 4 lung bronchi of independent donors. We also identified that SARS-CoV-2 does not infect HAE from the basolateral side, and the dominant SARS-CoV-2 permissive epithelial cells are ciliated cells and goblet cells, whereas virus replication in basal cells and club cells was not detectable. Notably, virus infection immediately damaged the HAE, which is demonstrated by dispersed Zonula occludens-1 (ZO-1) expression without clear tight junctions and partial loss of cilia. Importantly, we identified that SARS-CoV-2 productive infection of HAE requires a high viral load of 2.5 x 105 virions per cm2 of epithelium. Thus, our studies highlight the importance of a high viral load and that epithelial renewal initiates and maintains a recurrent infection of HAE with SARS-CoV-2.

microbiology↗

bioRxiv2020

Time-course transcriptome landscape of achene development in lettuce

Lettuce (Lactuca sativa L.), which belongs to the large Asteraceae (Compositae) family, breeds by sexual reproduction and produce seeds. Actually, lettuce seeds are achenes, which are defined as fruits. However, few studies have described the morphological characteristics of the lettuce achenes, and genes essential for achene development are largely unknown in lettuce. To investigate the gene activity during achene development and determine the possible mechanisms that influence achene development in lettuce, we performed a time-course transcriptome analysis of lettuce achenes. A total of 23,790 expressed genes were detected at the five achene development stages. We investigated the gene expression patterns during achene development and identified the enriched biological processes at the corresponding stages. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed a variety of transcriptomic similarities and differentiation at different achene development stages. Further, transcription factors and phytohormones were found to play important roles during achene development. Finally, we proposed a working model to illustrate the gene expression modules and possible molecular mechanism underlying achene development. Our time-course transcriptome data also provides a foundation for future functional studies to reveal the genetic control of achene development in lettuce.

plant biology↗

bioRxiv2020

Decoding herbal materials of representative TCM preparations with the multi-barcoding approach

With the rapid development of high-throughput sequencing (HTS) technology, the techniques for the assessment of biological ingredients in Traditional Chinese Medicine (TCM) preparations have also advanced. By using HTS together with the multi-barcoding approach, all biological ingredients could be identified from TCM preparations in theory, as long as their DNA is present. The biological ingredients of a handful of classical TCM preparations were analyzed successfully based on this approach in previous studies. However, the universality, sensitivity and reliability of this approach used on TCM preparations remain unclear. Here, four representative TCM preparations, namely Bazhen Yimu Wan, Da Huoluo Wan, Niuhuang Jiangya Wan and You Gui Wan, were selected for concrete assessment of this approach. We have successfully detected from 77.8% to 100% prescribed herbal materials based on both ITS2 and trnL biomarkers. The results based on ITS2 have also shown a higher level of reliability than those of trnL at species level, and the integration of both biomarkers could provide higher sensitivity and reliability. In the omics big-data era, this study has undoubtedly made one step forward for the multi-barcoding approach for prescribed herbal materials analysis of TCM preparation, towards better digitization and modernization of drug quality control.

bioinformatics↗

bioRxiv2020

Defining the signalling determinants of a posterior ventral spinal cord identity in human neuromesodermal progenitor derivatives

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus it is a critical parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine and disease modelling applications. However, the in vitro generation of posterior spinal cord MNs has been challenging. Although the induction of cells resembling neuromesodermal progenitors (NMPs), the bona fide precursors of the mammalian spinal cord, offers a promising solution, the progressive specification of posterior MNs from these cells is not well-defined. Here we determine the signals guiding the transition of human NMP-like cells toward posterior ventral spinal cord neurectoderm. We show that combined WNT-FGF activities drive a posterior dorsal early neural state while suppression of TGFβ-BMP signalling pathways, combined with SHH stimulation, promotes a ventral identity. Based on these results, we define an optimised protocol for the generation of posterior MNs that can efficiently integrate within the neural tube of chick embryos. We expect that our findings will facilitate the functional comparison of hPSC-derived spinal cord cells of distinct axial identities.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗

bioRxiv2020

Establishment of a Replicon System for Bourbon Virus and Identification of Small Molecules that Efficiently Inhibit Virus Replication

Bourbon virus (BRBV) was first isolated from a patient hospitalized at the University of Kansas Hospital in 2014. Since then, several deaths have been reported to be caused by BRBV infection in the Midwest and Southern United States. BRBV is a tick-borne virus that is widely carried by lone star ticks. It belongs to genus Thogotovirus of the Orthomyxoviridae family. Currently, there are no treatments or vaccines available for BRBV or thogotovirus infection caused diseases. In this study, we reconstituted a replicon reporter system, composed of plasmids expressing the RNA-dependent RNA polymerase (RdRP) complex (PA, PB1 and PB2), nucleocapsid (NP) protein, and a reporter gene flanked by the 3 and 5 UTR of the envelope glycoprotein (GP) genome segment. By using the luciferase reporter, we screened a few small molecule compounds of anti-endonuclease that inhibited the nicking activity by parvovirus B19 (B19V) NS1, as well as FDA-approved drugs targeting the RdRP of influenza virus. Our results demonstrated that myricetin, and an anti-B19V NS1 nicking inhibitor, efficiently inhibited the RdRP activity of BRBV and virus replication. The IC50 and EC50 of myricetin are 2.22 M and 4.6 M, respectively, in cells. Myricetin had minimal cytotoxicity in cells, and therefore the therapeutic index of the compound is high. In conclusion, the BRBV replicon system is a useful tool to study viral RNA replication and to develop antivirals, and myricetin may hold promise in treatment of BRBV infected patients.

microbiology↗

bioRxiv2018

Association of brain age with smoking, alcohol consumption, and genetic variants

The association of the degree of aging based on the whole-brain anatomical characteristics, or brain age, with smoking, alcohol consumption, and individual genetic variants is unclear. Here, we investigated these associations through analyzing data collected for UK Biobank subjects with an age range of 45 to 79 years old. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subjects brain age relative to peers, based on a randomly selected training set subjects (n=2,679). We then applied this model to the evaluation set subjects (n=6,252) and further tested the association of RBA with tobacco smoking, alcohol consumption, and 529,098 genetic variants. We found that daily or almost daily consumption of smoking or alcohol was significantly associated with increased RBA (P<0.05). Interestingly, there was no significant difference of RBA among subjects who smoked occasionally, only tried once or twice, or abstained from smoking. Further, there was no significant difference of RBA among subjects who consumed alcohol 1 to 3 times a month, at special occasions only, or abstained from alcohol consumption. Among the subjects who smoked on most or all days and did not abstain from alcohol drinking, RBA increased by 0.021 years for each addition pack-year of smoking (P<0.05) and by 0.014 years for each additional gram of alcohol consumed (P<0.05). We did not identify individual genetic variation significantly associate with RBA. Further exploration of genetic variation-brain aging association is warranted, where our current genetic association statistics may serve as prior knowledge.

neuroscience↗

bioRxiv2018

Microbial contamination screening and interpretation for biological laboratory environments

Advances in microbiome researches have led us to the realization that the composition of microbial communities of indoor environment is profoundly affected by the function of buildings, and in turn may bring detrimental effects to the indoor environment and the occupants. Thus investigation is warranted for a deeper understanding of the potential impact of the indoor microbial communities. Among these environments, the biological laboratories stand out because they are relatively clean and yet are highly susceptible to microbial contaminants. In this study, we assessed the microbial compositions of samples from the surfaces of various sites across different types of biological laboratories. We have qualitatively and quantitatively assessed these possible microbial contaminants, and found distinct differences in their microbial community composition. We also found that the type of laboratories has a larger influence than the sampling site in shaping the microbial community, in terms of both structure and richness. On the other hand, the public areas of the different types of laboratories share very similar sets of microbes. Tracing the main sources of these microbes, we identified both environmental and human factors that are important factors in shaping the diversity and dynamics of these possible microbial contaminations in biological laboratories. These possible microbial contaminants that we have identified will be helpful for people who aim to eliminate them from samples.\n\nImportanceMicrobial communities from biological laboratories might hamper the conduction of molecular biology experiments, yet these possible contaminations are not yet carefully investigated. In this work, a metagenomic approach has been applied to identify the possible microbial contaminants and their sources, from the surfaces of various sites across different types of biological laboratories. We have found distinct differences in their microbial community compositions. We have also identified the main sources of these microbes, as well as important factors in shaping the diversity and dynamics of these possible microbial contaminations. The identification and interpretation of these possible microbial contaminants in biological laboratories would be helpful for alleviate their potential detrimental effects.

microbiology↗

bioRxiv2018

Using QC-Blind for quality control and contamination screening of bacteria DNA sequencing data without reference genome

Quality control in next generation sequencing has become increasingly important as the technique becomes widely used. Tools have been developed for filtering possible contaminants in the sequencing data of species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species.\n\nIn this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline requires only very little information from the marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering and marker gene assignment.\n\nWhen evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.\n\nIMPORTANCEAt present, many sequencing projects are still performed on potentially contaminated samples, which bring into question their accuracies. However, current reference-based quality control method are limited as they need either the genome of target species or contaminations. In this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind is suitable for real-life samples where limited information is available for both target and contamination species.

bioinformatics↗

bioRxiv2018

Gut microbiome of an unindustrialized population have characteristic enrichment of SNPs in species and functions with the succession of seasons

Most studies investigating human gut microbiome dynamics are conducted in modern populations. However, unindustrialized populations are arguably better subjects in answering human-gut microbiome coevolution questions due to their lower exposure to antibiotics and higher dependence on natural resources. Hadza hunter-gatherers in Tanzania have been found to exhibit high biodiversity and seasonal patterns in their gut microbiome composition at family level, where some taxa disappear in one season and reappear at later time. However, such seasonal changes have previously been profiled only according to species abundances, with genome-level variant dynamics unexplored. As a result it is still elusive how microbial communities change at the genome-level under environmental pressures caused by seasonal changes. Here, a strain-level SNP analysis of Hadza gut metagenome is performed for 40 Hadza fecal samples collected in three seasons. First, we benchmarked three SNP calling tools based on simulated sequencing reads, and selected VarScan2 that has highest accuracy and sensitivity after a filtering step. Second, we applied VarScan2 on Hadza gut microbiome, with results showing that: with more SNP presented in wet season in general, eight prevalent species have significant SNP enrichments in wet season of which only three species have relatively high abundances. This indicates that SNP characteristics are independent of species abundances, and provides us a unique lens towards microbial community dynamics. Finally, we identify 83 genes with the most characteristic SNP distributions between wet season and dry season. Many of these genes are from Ruminococcus obeum, and mainly from metabolic pathways like carbon metabolism, pyruvate metabolism and glycolysis, as shown by KEGG annotation. This implies that the seasonal changes might indirectly impact the mutational patterns for specific species and functions for gut microbiome of an unindustrialized population, indicating the role of these variants in their adaptation to the changing environment and diets.\n\nImportanceBy analyzing the changes of SNP enrichments in different seasons, we have found that SNP characteristics are independent of species abundances, and could provide us a unique lens towards microbial community dynamics at the genomic level. Many of the genes in microbiome also presented characteristic SNP distributions between wet season and dry season, indicating the role of variants in specific species in their adaptation to the changing environment for an unindustrialized population.

microbiology↗

bioRxiv2018

Bi-clustering interpretation and prediction of correlation between gene expression and protein abundance

Most organisms transcript and protein level only moderately correlate for various reasons, such as regulation of transcription and protein degradation. Better prediction and understanding the correlation between gene expression and protein abundance has been possible by harnessing the matching RNA/protein datasets produced by modern high-throughput RNA-Seq and mass spectrometry methods. In this work, we have utilized some well-studied matching RNA/protein datasets, and explored for the first time a bi-clustering method to cluster genes that have consistent correlation patterns between gene expression and protein abundance. The clustering results have been interpreted from the perspective of both transcriptomic and proteomic features, which show that mRNA half-life, protein half-life and protein structure in concert significantly affect the correlation of gene expression and protein abundance. With these and other carefully selected features, a prediction model based on individual clusters, called Cluster-based Linear prediction Model (CLM), was built and tested on mouse liver mitochondrial, mouse brainstem mitochondrial, Saccharomyces cerevisiae and Danio rerio datasets. CLM could find genes for which protein abundance can be predicted from mRNA data. In summary, based on bi-clustering, feature selection and CLM model, we have established a new and valuable cluster-based protein abundance prediction method.

bioinformatics↗

bioRxiv2018

Holmes-ITS2: Consolidated ITS2 resources and search engines for plant DNA-based marker analyses

Plants are valuable resources for a variety of products in modern societies. Plant species identification is an integral part of research and practical application on plants. In parallel with high-throughput sequencing technology, the high-throughput screening of species is in high demand. Highly accurate and efficient DNA-based marker identification is essential for the effective analysis of plant species or biological constituents of a mixture of plants as well. Therefore, it is of general interests and significance to generate a comprehensive and accurate DNA-based marker sequence resource, as well as to build efficient sequence search engines, for the accurate and fast identification of plant species.\n\nIn this work, we have firstly established a high-quality ITS2 sequence database of plant species containing more than 150,000 entries, through the systematical collection and manually collation of the published ITS2 sequencing data of plant species, data quality control, as well as representative sequence refinement based on clustering method. Secondly, an accurate and efficient plant species identification system based on ITS2 sequence was constructed, which is the proper combination of sequence search algorithms including BLAST and Kraken. Through the deployment of high-performance and frequently updated web service, its expected to serve for a wide range of researchers involving the taxonomy classification of plant species, as well as for deciphering of plant mixed systems including herbal materials in TCM preparations.\n\nThe Holmes-ITS2 web service is freely accessible at: http://its2.tcm.microbioinformatics.org/. The input of this web service could be multiple sequences in a single fasta format, to search for matching ITS2 biomarker sequences already annotated in the database. This sequence-based search is based on two engines: BLAST, and k-mer based Kraken. Alternatively, users can directly search for species name for the corresponding ITS2 biomarker sequences. The web service has been put to the test by more than 50 experts from China, Denmark and US, and the average running time for the search ranges from 3-30 seconds for up to 100 sequences as a batch query.

bioinformatics↗

bioRxiv2018

Data-mining of Antibiotic Resistance Genes Provides Insight into the Community Structure of Ocean Microbiome

BackgroundAntibiotics have been spread widely in environments, asserting profound effects on environmental microbes as well as antibiotic resistance genes (ARGs) within these microbes. Therefore, investigating the associations between ARGs and bacterial communities become an important issue for environment protection. Ocean microbiomes are potentially large ARG reservoirs, but the marine ARG distribution and its associations with bacterial communities remain unclear.\n\nMethodswe have utilized the big-data mining techniques on ocean microbiome data to analysis the marine ARGs and bacterial distribution on a global scale, and applied comprehensive statistical analysis to unveil the associations between ARG contents, ocean microbial community structures, and environmental factors by reanalyzing 132 metagenomic samples from the Tara Oceans project.\n\nResultsWe identified in total 1,926 unique ARGs and found that: firstly, ARGs are more abundant and diverse in the mesopelagic zone than other water layers. Additionally, ARG-enriched genera are closely connected in co-occurrence network. We also found that ARG-enriched genera are often more abundant than their ARG-less neighbors. Furthermore, we found that samples from the Mediterranean that is surrounded by human activities often contain more ARGs.\n\nConclusionOur research for investigating the marine ARG distribution and revealing the association between ARG and bacterial communities provide a deeper insight into the marine bacterial communities. We found that ARG-enriched genera were often more abundant than their ARG-less neighbors in the same environment, indicating that genera enriched with ARGs might possess an advantage over others in the competition for survival in the oceanic microbial communities.

microbiology↗

bioRxiv2018

Agricultural Pollution Risks Influence Microbial Ecology in Honghu Lake

BackgroundAgricultural activities, such as stock-farming, planting industry, and fish aquaculture, can influence the physicochemistry and biology of freshwater lakes. However, the extent to which these agricultural activities, especially those that result in eutrophication and antibiotic pollution, effect water and sediment-associated microbial ecology, remains unclear.\n\nMethodsWe performed a geospatial analysis of water and sediment associated microbial community structure, as well as physicochemical parameters and antibiotic pollution, across 18 sites in Honghu lake, which range from impacted to less-impacted by agricultural pollution. Furthermore, the co-occurrence network of water and sediment were built and compared accorded to the agricultural activities.\n\nResultsPhysicochemical properties including TN, TP, NO3--N, and NO2--N were correlated with microbial compositional differences in water samples. Likewise, in sediment samples, Sed-OM and Sed-TN correlated with microbial diversity. Oxytetracycline and tetracycline concentration described the majority of the variance in taxonomic and predicted functional diversity between impacted and less-impacted sites in water and sediment samples, respectively. Finally, the structure of microbial co-associations was influenced by the eutrophication and antibiotic pollution.\n\nConclusionThese analyses of the composition and structure of water and sediment microbial communities in anthropologically-impacted lakes are imperative for effective environmental pollution monitoring. Likewise, the exploration of the associations between environmental variables (e.g. physicochemical properties, and antibiotics) and community structure is important in the assessment of lake water quality and its ability to sustain agriculture. These results show agricultural practices can negatively influence not only the physicochemical properties, but also the biodiversity of microbial communities associated with the Honghu lake ecosystem. And these results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity; and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu lake.

microbiology↗