Transcriptomic and functional analyses reveal the molecular mechanisms underlying Fe-mediated tobacco resistance to potato virus Y infection

Potato virus Y (PVY) mainly infects Solanaceous crops, resulting in considerable losses in the yield and quality. Iron (Fe) is involved in various biological processes in plants, but its roles in resistance to PVY infection has not been reported. In this study, foliar application of Fe could effectively inhibit early infection of PVY, and a full-length transcriptome and Illumina RNA sequencing was performed to investigate its modes of action in PVY-infected Nicotiana tabacum. The results showed that 18,074 alternative splicing variants, 3,654 fusion transcripts, 3,086 long non-coding RNAs and 14,403 differentially expressed genes (DEGs) were identified. Specifically, Fe application down-regulated the expression levels of the DEGs related to phospholipid hydrolysis, phospholipid signal, cell wall biosynthesis, transcription factors (TFs) and photosystem I composition, while those involved with photosynthetic electron transport chain (PETC) were up-regulated at 1 day post inoculation (dpi). At 3 dpi, these DEGs related to photosystem II composition, PETC, molecular chaperones, protein degradation and some TFs were up-regulated, while those associated with light-harvesting, phospholipid hydrolysis, cell wall biosynthesis were down-regulated. At 9 dpi, Fe application had little effects on resistance to PVY infection and transcript profiles. Functional analysis of these potentially critical DEGs was thereafter performed using virus-induced gene silencing approaches and the results showed that NbCat-6A positively regulates PVY infection, while the reduced expressions of NbWRKY26, NbnsLTP, NbFAD3 and NbHSP90 significantly promote PVY infection in N. benthamiana. Our results elucidated the regulatory network of Fe-mediated resistance to PVY infection in plants, and the functional candidate genes also provide important theoretical bases to further improve host resistance against PVY infection

A Pilot Study: Changes of Gut Microbiota in Post-surgery Colorectal Cancer Patients

Colorectal cancer (CRC) is a growing health problem throughout the world. Strong evidences have supported that gut microbiota can influence tumorigenesis; however, little is known about what happens to gut microbiota following surgical resection. Here, we examined the changes of gut microbiota in CRC patients after the surgical resection. Using the PCoA analysis and dissimilarity tests, the microbial taxonomic compositions and diversities of gut microbiota in post-surgery CRC patients (A1) were significantly different from those in pre-surgery CRC patients (A0) and healthy individuals (H). Compared with A0 and H, the Shannon diversity and Simpson diversity were significantly decreased in A1 (P < 0.05). Based on the LEfSe analysis, the relative abundance of phylum Proteobacteria in A1 was significantly increased than that in A0 and H. The genus Klebsiella in A1 had higher proportions than that in A0 (P < 0.05). Individual variation was distinct; however, 90% of CRC patients in A1 had more abundances of Klebsiella than A0. The Klebsiella in A1 was significantly associated with infectious diseases (P < 0.05), revealed by the correlation analysis between differentiated genera and metabolic pathway. The Klebsiella (Proteobacteria, Gammaproteobacteria, Enterobacteriales, Enterobacteriaceae) in A1 was significantly linked with lymphatic invasion (P < 0.05). Furthermore, the PCA of KEGG pathways indicated that gut microbiota with a more scattered distribution in A1 was noticeably different from that in A0 and H. The nodes, the links, and the kinds of phylum in each module in A1 were less than those in A0 and H, indicating that gut microbiota in A1 had a relatively looser ecologcial interaction network. To sum up, this pilot study identified the changes of gut microbiota in post-surgery CRC patients, and highlights future avenues in which the gut microbiota is likely to be of increasing importance in the care of surgical patients

Chemotherapy Alters the Phylogenetic Molecular Ecological Networks of Intestinal Microbial Communities

Intestinal microbiota is now widely known to play key roles in nutritional uptake, metabolism, and regulation of human immune responses. There are multiple studies assessing intestinal microbiota changes in response to chemotherapy. In this study, microbial phylogenetic molecular ecological networks (pMENs) were firstly used to study the effects of chemotherapy on the intestinal microbiota of colorectal cancer (CRC) patients. Based on the random network model, we demonstrated that overall network structures and properties were significantly changed by chemotherapy, especially in average path length, average clustering coefficient, average harmonic geodesic distance and modularity (P < 0.05). The taxa in the module tended to co-exclude rather than co-occur in CRC patient networks, indicating probably competition relationships. The co-exclude correlations were decreased by 37.3% from T0 to T5 in response to chemotherapy. Significantly negative correlations were observed in positive/negative OTU degree and tumor markers (P < 0.05). Furthermore, the topological roles of the OTUs (module hubs and connectors) were changed with the chemotherapy. For example, the OTU167, OTU8, and OTU9 from the genera Fusobacterium, Bacteroides, and Faecalibacterium, respectively, were identified as keystone taxa, which were defined as either “hubs” or OTUs with highest connectivity in the network. These OTUs were significantly correlated with tumor markers (P < 0.05), suggesting that they probably were influenced by chemotherapy. The pMENs constructed in this study predicted the potential effects of chemotherapy on intestinal microbial community co-occurrence interactions. The changes may have an effect on the therapeutic effects. However, larger clinical samples are required to identify the conclusion

Nanocellulose and Its Biohybrids for Water Purification : Atomic Force Microscopy as a Tool to Probe Surface Properties and Interactions

Nanocellulose has been explored extensively in recent years as an adsorbent due to its promising performance in the removal of charged contaminants from water. In this thesis, various atomic force microscopy (AFM) techniques are used to understand the surface characteristics and specific interactions of nanocellulose with water contaminants (heavy metal ions and dyes) and nanoscale entities (Graphene Oxide (GO) and Graphene Oxide nanocolloids (nanoGO)), and explain the mechanisms related to adsorption, metal ion clustering, self-assembly and mechanical reinforcement. AFM probes functionalised with microscale and nanoscale celluloses were used as colloidal probes to study specific surface interactions with heavy metal ions and dyes in the aqueous medium. This approach enabled quantitative measurements of the adhesion force between nanocellulose and the water pollutants under in situ conditions by direct or in-direct methods. Adhesion forces, including the piconewton range, were measured, and the forces depended on the surface groups present on the nanocellulose. AFM imaging in dry and/or wet conditions was successfully used to investigate the adsorption, self-assembly, morphology and mechanical properties of nanocellulose and its bio-hybrids. The self-assembly, the metal nanolayer and the nanoclusters on the surface of nanocellulose and its biohybrids after adsorption were confirmed and explained by advanced microscopy, spectroscopy and computational modelling. The adhesion and stiffness measurement of single nanocellulose fibers using in situ PeakForce Quantitative Nanomechanical (PF-QNM) characterization confirmed the adsorption of metal ions on the surface in the liquid medium. PF-QNM mapping of the freestanding biohybrid membranes also revealed the enhanced modulus of the biohybrid membrane compared with the TEMPO(2,2,6,6-tetramethylpiperidine-1-oxylradical)-mediated oxidation nanofibers (TOCNF) membrane, which explained the hydrolytic stability and recyclability of these membranes. The established methodology, which combines advanced microscopy with spectroscopy and modelling techniques, can be extended to other biobased macromolecular systems to investigate the adsorption behaviour and/or surface interactions in bio nanotechnology.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Manuscript. Paper 5: Manuscript.</p

Synergistic Mechanism of Rare-Earth Modification TiO2 and Photodegradation on Benzohydroxamic Acid

Rare earth elements are plentiful in Gannan area, China, and there is a large amount of wastewater from all kinds of mines. In this paper, rare-earth modification TiO2 composites (RE/TiO2, RE = La, Ce, Gd, Yb) was studied by theory computation and experimental performance. The prepared RE/TiO2 was investigated for the degradation of benzohydroxamic acid (BHA) as a typical residual reagent in wastewater from beneficiation. The crystallinity, morphology, specific surface area, light absorption, and composition of compound were investigated by various techniques. As a result of computation and experimentation, four different electron configurations of rare earth all retained the anatase phase of TiO2 and reduced the band gap of TiO2 to some degree compared with pure TiO2. Different rare-earth elements and calcination temperatures resulted in different removal effects on BHA. The optimum doping contents were 0.75% (500 °C), 0.20% (500 °C), 0.70% (500 °C) and 0.50% (450 °C) for La, Ce, Gd, Yb respectively. All the RE/TiO2 composites studied in this research still possessed good photoactivity after four runs, which supports the theoretical and practical basement for the photocatalytic treatment of mining and metallurgy wastewater

A Pilot Study: Favorable Effects of Clostridium butyricum on Intestinal Microbiota for Adjuvant Therapy of Lung Cancer

Probiotics as medications have previously been shown to change intestinal microbial characteristics, potentially influencing cancer therapy efficacy. Patients with non-squamous non-small cell lung cancer (NS-NSCLC) treated by bevacizumab plus platinum-based chemotherapy were randomized to obtain Clostridium butyricum supplement (CBS) or receive a placebo as adjuvant therapy. Clinical efficacy and safety were assessed using progression-free survival (PFS), overall survival (OS), and adverse events (AE). Intestinal microbiota was longitudinally explored between CBS and placebo groups over time. Patients who took CBS had significantly decreased bacterial richness and abundance, as well as increased the total richness of the genus Clostridium, Bifidobacterium, and Lactobacillus compared to the placebo group (p &lt; 0.05). Beta diversity and the interactional network of intestinal microbiota were distinctly different between CBS and placebo group. However, there were no significant variations between them in terms of microbial taxonomical taxa and alpha diversity. The potential opportunistic pathogen Shewanella was still detectable after treatment in the placebo group, while no distinguishing microbial markers were found in the CBS group. In terms of clinical efficacy, the CBS group had a significantly reduced AE compare to the placebo group (p &lt; 0.05), although no significantly longer PFS and OS. Therefore, favorable modifications in intestinal microbiota and significant improvements in drug safety make probiotics be promising adjunctive therapeutic avenues for lung cancer treatment

A Method for Community Detection of Complex Networks Based on Hierarchical Clustering

International audienceDue to the development and popularization of Internet, there is more and more research focusing on complex networks. Research shows that there exists community structure in complex networks. Finding out community structure helps to extract useful information in complex networks, so the research on community detection is becoming a hotspot in recent years. There are two remarkable problems in detecting communities. Firstly, the detection accuracy is normally not very high; Secondly, the assessment criteria are not very effective when real communities are unknown. This paper proposes an algorithm for community detection based on hierarchical clustering (CDHC Algorithm). CDHC Algorithm firstly creates initial communities from global central nodes, then expands the initial communities layer by layer according to the link strength between nodes and communities, and at last merges some very small communities into large communities. This paper also proposes the concept of extensive modularity, overcoming some weakness of modularity. The extensive modularity can better evaluate the effectiveness of algorithms for community detection. This paper verifies the advantage of extensive modularity through experiments and compares CDHC Algorithm and some other representative algorithms for community detection on some frequently used datasets, so as to verify the effectiveness and advantages of CDHC Algorithm

A Method for Community Detection of Complex Networks Based on Hierarchical Clustering

International audienceDue to the development and popularization of Internet, there is more and more research focusing on complex networks. Research shows that there exists community structure in complex networks. Finding out community structure helps to extract useful information in complex networks, so the research on community detection is becoming a hotspot in recent years. There are two remarkable problems in detecting communities. Firstly, the detection accuracy is normally not very high; Secondly, the assessment criteria are not very effective when real communities are unknown. This paper proposes an algorithm for community detection based on hierarchical clustering (CDHC Algorithm). CDHC Algorithm firstly creates initial communities from global central nodes, then expands the initial communities layer by layer according to the link strength between nodes and communities, and at last merges some very small communities into large communities. This paper also proposes the concept of extensive modularity, overcoming some weakness of modularity. The extensive modularity can better evaluate the effectiveness of algorithms for community detection. This paper verifies the advantage of extensive modularity through experiments and compares CDHC Algorithm and some other representative algorithms for community detection on some frequently used datasets, so as to verify the effectiveness and advantages of CDHC Algorithm

Self-Assembled TEMPO Cellulose Nanofibers: Graphene Oxide-Based Biohybrids for Water Purification

Nanocellulose, graphene oxide (GO), and their combinations there off have attracted great attention for the application of water purification recently because of their unique adsorption capacity, mechanical characteristics, coordination with transition metal ions, surface charge density, and so on. In the current study, (2,2,6,6-tetramethylpiperidine-1-oxylradical) (TEMPO)-mediated oxidized cellulose nanofibers (TOCNF) and GO sheets or graphene oxide nanocolloid (nanoGO) biohybrids were prepared by vacuum filtration method to obtain self-assembled adsorbents and membranes for water purification. The porous biohybrid structure, studied using advanced microscopy techniques, revealed a unique networking and self-assembling of TOCNF, GO, and nanoGO, driven by the morphology of the GO phase and stabilized by the intermolecular H-bonding between carboxyl groups and hydroxyl groups. The biohybrids exhibited a promising adsorption capacity toward Cu­(II) due to TOCNF and formed a unique “arrested state” in water because of ionic cross-linking between adsorbed Cu­(II) and the negatively charged TOCNF and GO phase. The mechanical performance of the freestanding biohybrid membranes investigated using PeakForce Quantative NanoMechanics characterization confirmed the enhanced modulus of the hybrid membrane compared to that of the TOCNF membrane. Besides, the TOCNF+nanoGO membrane shows unique hydrolytic stability and recyclability even under several cycles of adsorption and desorption and strong sonication. This study shows that TOCNF and nanoGO hybrids can generate new water-cleaning membranes with synergistic properties because of their high adsorption capacity, flexibility, hydrolytic stability, and mechanical robustness

Cellulose Nanofiber–Graphene Oxide Biohybrids: Disclosing the Self-Assembly and Copper-Ion Adsorption Using Advanced Microscopy and ReaxFF Simulations

The self-assembly of nanocellulose and graphene oxide into highly porous biohybrid materials has inspired the design and synthesis of multifunctional membranes for removing water pollutants. The mechanisms of self-assembly, metal ion capture, and cluster formation on the biohybrids at the nano- and molecular scales are quite complex. Their elucidation requires evidence from the synergistic combination of experimental data and computational models. The AFM-based microscopy studies of (2,2,6,6-tetramethylpiperidine-1-oxylradical)-mediated oxidized cellulose nanofibers (TOCNFs), graphene oxide (GO), and their biohybrid membranes provide strong, direct evidence of self-assembly; small GO nanoparticles first attach and accumulate along a single TOCNF fiber, while the long, flexible TOCNF filaments wrap around the flat, wide GO planes, thus forming an amorphous and porous biohybrid network. The layered structure of the TOCNFs and GO membrane, derived from the self-assembly and its surface properties before and after the adsorption of Cu­(II), is investigated by advanced microscopy techniques and is further clarified by the ReaxFF molecular dynamics (MD) simulations. The dynamics of the Cu­(II)-ion capture by the TOCNF and GO membranes in solution and the ion cluster formation during drying are confirmed by the MD simulations. The results of this multidisciplinary investigation move the research one step forward by disclosing specific aspects of the self-assembly behavior of biospecies and suggesting effective design strategies to control the pore size and robust materials for industrial applications