Nutritional changes in trees during drought-induced mortality: A comprehensive meta-analysis and a field study.

Both macronutrients and micronutrients are essential for tree growth and development through participating in various ecophysiological processes. However, the impact of the nutritional status of trees on their ability to withstand drought-induced mortality remains inconclusive. We thus conducted a comprehensive meta-analysis, compiling data on 11 essential nutrients from 44 publications (493 independent observations). Additionally, a field study was conducted on Pinus sylvestris L. trees with varying drought-induced vitality loss in the "Visp" forest in southern Switzerland. No consistent decline in tree nutritional status was observed during tree mortality. The meta-analysis revealed significantly lower leaf potassium (K), iron (Fe), and copper (Cu) concentrations with tree mortality. However, the field study showed no causal relationships between nutritional levels and the vitality status of trees. This discrepancy is mainly attributed to the intrinsic differences in the two types of experimental designs and the ontogenetic stages of target trees. Nutrient reductions preceding tree mortality were predominantly observed in non-field conditions, where the study was conducted on seedlings and saplings with underdeveloped root systems. It limits the nutrient uptake capacity of these young trees during drought. Furthermore, tree nutritional responses are also influenced by many variables. Specifically, (a) leaf nutrients are more susceptible to drought stress than other organs; (b) reduced tree nutrient concentrations are more prevalent in evergreen species during drought-induced mortality; (c) of all biomes, Mediterranean forests are most vulnerable to drought-induced nutrient deficiencies; (d) soil types affect the direction and extent of tree nutritional responses. We identified factors that influence the relationship between tree nutritional status and drought survival, and proposed potential early-warning indicators of impending tree mortality, for example, decreased K concentrations with declining vitality. These findings contribute to our understanding of tree responses to drought and provide practical implications for forest management strategies in the context of global change.

Deciphering the free energy landscapes of SARS-CoV-2 wild type and Omicron variant interacting with human ACE2.

The binding of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein to the host cell receptor angiotensin-converting enzyme 2 (ACE2) is the first step in human viral infection. Therefore, understanding the mechanism of interaction between RBD and ACE2 at the molecular level is critical for the prevention of COVID-19, as more variants of concern, such as Omicron, appear. Recently, atomic force microscopy has been applied to characterize the free energy landscape of the RBD-ACE2 complex, including estimation of the distance between the transition state and the bound state, xu. Here, using a coarse-grained model and replica-exchange umbrella sampling, we studied the free energy landscape of both the wild type and Omicron subvariants BA.1 and XBB.1.5 interacting with ACE2. In agreement with experiment, we find that the wild type and Omicron subvariants have similar xu values, but Omicron binds ACE2 more strongly than the wild type, having a lower dissociation constant KD.

Interaction of SARS-CoV-2 with host cells and antibodies: experiment and simulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the devastating global COVID-19 pandemic announced by WHO in March 2020. Through unprecedented scientific effort, several vaccines, drugs and antibodies have been developed, saving millions of lives, but the fight against COVID-19 continues as immune escape variants of concern such as Delta and Omicron emerge. To develop more effective treatments and to elucidate the side effects caused by vaccines and therapeutic agents, a deeper understanding of the molecular interactions of SARS-CoV-2 with them and human cells is required. With special interest in computational approaches, we will focus on the structure of SARS-CoV-2 and the interaction of its spike protein with human angiotensin-converting enzyme-2 (ACE2) as a prime entry point of the virus into host cells. In addition, other possible viral receptors will be considered. The fusion of viral and human membranes and the interaction of the spike protein with antibodies and nanobodies will be discussed, as well as the effect of SARS-CoV-2 on protein synthesis in host cells.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta.

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-ß (Aß42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the ß-content of Aß42; however, it increases the tendency of the monomeric Aß42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aß42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aß42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Whole-brain neural connectivity to cholinergic neurons in the nucleus basalis of Meynert.

The cholinergic neurons in the nucleus basalis of Meynert (NBM) are a key structure in cognition, the dysfunction of which is associated with various neurological disorders, especially dementias. However, the whole-brain neural connectivity to cholinergic neurons in the NBM remains to be further and comprehensively researched. Using virus-based, specific, retrograde, and anterograde tracing, we illustrated the monosynaptic inputs and axon projections of NBM cholinergic neurons in choline acetyltransferase (ChAT)-Cre transgenic mice. Our results showed that NBM cholinergic neurons received mainly inputs from the caudate putamen and the posterior limb of the anterior commissure in the subcortex. Moreover, the majority of cholinergic terminals from the NBM were observed in the cortex mantle, including the motor cortex, sensory cortex, and visual cortex. Interestingly, although NBM cholinergic neurons received input projections from the caudate putamen, interstitial nucleus of the posterior limb of the anterior commissure, and central amygdaloid nucleus, NBM cholinergic neurons sparsely sent axon projection to innervate these areas. Furthermore, primary motor cortex, secondary motor cortex, and primary somatosensory cortex received abundant inputs from the NBM but sent few outputs to the NBM. Taken together, our results reveal the detailed and specific connectivity of cholinergic neurons of the NBM and provide a neuroanatomic foundation for further studies to explore the important physiological functions of NBM cholinergic neurons.

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.

BACKGROUND: Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare. METHODS: PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). RESULTS: Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%). CONCLUSIONS: The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aß, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Functional structure mediates the responses of productivity to addition of three nitrogen compounds in a meadow steppe.

Atmospheric nitrogen (N) deposition is altering grassland productivity and community structure worldwide. Deposited N comes in different forms, which can have different consequences for productivity due to differences in their fertilization and acidification effects. We hypothesize that these effects may be mediated by changes in plant functional traits. We investigated the responses of aboveground primary productivity and community functional composition to addition of three nitrogen compounds (NH4NO3, [NH4]2SO4, and CO[NH2]2) at the rates of 0, 5, 10, 20 g N m-2 yr-1. We used structural equation modeling (SEM) to evaluate how functional structure influences the responses of productivity to the three N compounds. Nitrogen addition increased community-level leaf chlorophyll content but decreased leaf dry matter content and phosphorus concentration. These changes were mainly due to intra-specific variation. Functional dispersion of traits was reduced by N addition through changes in species composition. SEM revealed that fertilization effects were more important than soil acidification for the responses of productivity to CO(NH2)2 addition, which enhanced productivity by decreasing functional trait dispersion. In contrast, the effects of (NH4)2SO4 and NH4NO3 were primarily due to soil acidification, influencing productivity via community-weighted means of functional traits. Our results suggest that N forms with different fertilizing and acidifying effects influence productivity via different functional traits pathways. Our study also emphasizes the need for in situ experiments with the relevant N compounds to accurately understand and predict the ecological effects of atmospheric N deposition on ecosystems.

Rhizosphere activity in an old-growth forest reacts rapidly to changes in soil moisture and shapes whole-tree carbon allocation.

Drought alters carbon (C) allocation within trees, thereby impairing tree growth. Recovery of root and leaf functioning and prioritized C supply to sink tissues after drought may compensate for drought-induced reduction of assimilation and growth. It remains unclear if C allocation to sink tissues during and following drought is controlled by altered sink metabolic activities or by the availability of new assimilates. Understanding such mechanisms is required to predict forests' resilience to a changing climate. We investigated the impact of drought and drought release on C allocation in a 100-y-old Scots pine forest. We applied 13CO2 pulse labeling to naturally dry control and long-term irrigated trees and tracked the fate of the label in above- and belowground C pools and fluxes. Allocation of new assimilates belowground was ca. 53% lower under nonirrigated conditions. A short rainfall event, which led to a temporary increase in the soil water content (SWC) in the topsoil, strongly increased the amounts of C transported belowground in the nonirrigated plots to values comparable to those in the irrigated plots. This switch in allocation patterns was congruent with a tipping point at around 15% SWC in the response of the respiratory activity of soil microbes. These results indicate that the metabolic sink activity in the rhizosphere and its modulation by soil moisture can drive C allocation within adult trees and ecosystems. Even a subtle increase in soil moisture can lead to a rapid recovery of belowground functions that in turn affects the direction of C transport in trees.

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.

Amyloid-ß Tetramers and Divalent Cations at the Membrane/Water Interface: Simple Models Support a Functional Role.

Charge polarization at the membrane interface is a fundamental process in biology. Despite the lower concentration compared to the abundant monovalent ions, the relative abundance of divalent cations (Ca2+, Mg2+, Zn2+, Fe2+, Cu2+) in particular spaces, such as the neuron synapse, raised many questions on the possible effects of free multivalent ions and of the required protection of membranes by the eventual defects caused by the free forms of the cations. In this work, we first applied a recent realistic model of divalent cations to a well-investigated model of a polar lipid bilayer, di-myristoyl phosphatidyl choline (DMPC). The full atomistic model allows a fairly good description of changes in the hydration of charged and polar groups upon the association of cations to lipid atoms. The lipid-bound configurations were analyzed in detail. In parallel, amyloid-ß 1-42 (Aß42) peptides assembled into tetramers were modeled at the surface of the same bilayer. Two of the protein tetramers' models were loaded with four Cu2+ ions, the latter bound as in DMPC-free Aß42 oligomers. The two Cu-bound models differ in the binding topology: one with each Cu ion binding each of the monomers in the tetramer; one with pairs of Cu ions linking two monomers into dimers, forming tetramers as dimers of dimers. The models here described provide hints on the possible role of Cu ions in synaptic plasticity and of Aß42 oligomers in storing the same ions away from lipids. The release of structurally disordered peptides in the synapse can be a mechanism to recover ion homeostasis and lipid membranes from changes in the divalent cation concentration.

Sphingopyxis yananensis sp. nov., a novel 2-nitropropane degrading bacterium isolated from a microbial fermentation bed substrate.

A rod-shaped, Gram-negative staining strain, FBM22T, was isolated from a microbial fermentation bed substrate from a pig farm. Its colonies appeared yellow and were 0.5-1.2 mm in diameter. Cells were 0.3-0.5 µm wide, 0.5-0.83 µm long. Optimal growth occurred at 30 °C and pH 7.0-8.0; NaCl was not required for growth. The strain performed denitrification and nitrate reduction functions. And it could produce catalase. FBM22-1T utilized the following organic substrates for growth: tyrosine, glutamic acid, D-glucose, and galactose. The novel isolate could degrade 2-nitropropane as carbon and nitrogen source. The dominant respiratory quinone was Q-10. The major polar lipids were diphosphatidylglycerol, phosphatidylcholine and phosphatidylethanolamine. C18:1 ω7c, C16:1 ω7c and/ or C16:1 ω6c, and C14:0 2-OH were the major (≥ 8%) fatty acids. The G+C content was 56.8 mol%. FBM22T was found to be a member of the genus Sphingopyxis in the family Sphingomonadaceae of the class Alphaproteobacteria. It had the highest sequence similarity with the type strains Sphingopyxis terrae subsp. ummariensis UI2T (96.47%) and Sphingopyxis terrae subsp. terrae NBRC 15098T (96.40%). Furthermore, FBM22T had 18.7% and 18.4% relatedness (based on digital DNA-DNA hybridization) with its two relatives (S. terrae subsp. ummariensis UI2T and S. terrae subsp. terrae NBRC 15098T). The morphological, physiological, and genotypic differences identified in this study support the classification of FBM22T as a novel species within the genus Sphingopyxis, for which the name Sphingopyxis yananensis sp. nov. is proposed. The type strain is FBM22T (= KCTC 82290T = CCTC AB2020286T).

Elastic moduli of normal and cancer cell membranes revealed by molecular dynamics simulations.

Recent studies indicate that there are mechanical differences between normal cells and cancer cells. Because the cell membrane takes part in a variety of vital processes, we test the hypothesis of whether or not two fundamental alterations in the cell membrane, i.e., the overexpression of phosphatidylserine lipids in the outer leaflet and a reduction in cholesterol concentration, could cause the softening in cancer cells. Adopting ten models of normal and cancer cell membranes, we carry out 1 µs all-atom molecular dynamics simulations to compare the structural properties and elasticity properties of two membrane types. We find that the overexpression of the phosphatidylserine lipids in the outer leaflet does not significantly alter the area per lipid, the membrane thickness, the lipid order parameters and the elasticity moduli of the cancer membranes. However, a reduction in the cholesterol concentration leads to clear changes in those quantities, especially decreases in the bending, tilt and twist moduli. This implies that the reduction of cholesterol concentration in the cancer membranes could contribute to the softening of cancer cells.

Protein aggregation rate depends on mechanical stability of fibrillar structure.

The formation of the fibrillar structure of amyloid proteins/peptides is believed to be associated with neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Since the rate of aggregation can influence neurotoxicity, finding the key factors that control this rate is of paramount importance. It was recently found that the rate of protein aggregation is related to the mechanical stability of the fibrillar structure such that the higher the mechanical stability, the faster the fibril is formed. However, this conclusion was supported by a limited dataset. In this work, we expand the previous study to a larger dataset, including the wild type of Aß42 peptide and its 20 mutants, the aggregation rate of which was measured experimentally. By using all-atom steered molecular dynamics (SMD) simulations, we can assess the mechanical stability of the fibril structure, which is characterized by the rupture force, pulling work, and unbinding free energy barrier. Our result confirms that mechanical stability is indeed related to the aggregation rate. Since the estimation of the aggregation rate using all-atom simulations is almost forbidden by the current computational capabilities, our result is useful for predicting it based on information obtained from fast SMD simulations for fibrils.

Molecular dynamics simulation of cancer cell membrane perforated by shockwave induced bubble collapse.

It has been widely accepted that cancer cells are softer than their normal counterparts. This motivates us to propose, as a proof-of-concept, a method for the efficient delivery of therapeutic agents into cancer cells, while normal cells are less affected. The basic idea of this method is to use a water jet generated by the collapse of the bubble under shockwaves to perforate pores in the cell membrane. Given a combination of shockwave and bubble parameters, the cancer membrane is more susceptible to bending, stretching, and perforating than the normal membrane because the bending modulus of the cancer cell membrane is smaller than that of the normal cell membrane. Therefore, the therapeutic agent delivery into cancer cells is easier than in normal cells. Adopting two well-studied models of the normal and cancer membranes, we perform shockwave induced bubble collapse molecular dynamics simulations to investigate the difference in the response of two membranes over a range of shockwave impulse 15-30 mPa s and bubble diameter 4-10 nm. The simulation shows that the presence of bubbles is essential for generating a water jet, which is required for perforation; otherwise, pores are not formed. Given a set of shockwave impulse and bubble parameters, the pore area in the cancer membrane is always larger than that in the normal membrane. However, a too strong shockwave and/or too large bubble results in too fast disruption of membranes, and pore areas are similar between two membrane types. The pore closure time in the cancer membrane is slower than that in the normal membrane. The implications of our results for applications in real cells are discussed in some details. Our simulation may be useful for encouraging future experimental work on novel approaches for cancer treatment.

Ribosome Elongation Kinetics of Consecutively Charged Residues Are Coupled to Electrostatic Force.

The speed of protein synthesis can dramatically change when consecutively charged residues are incorporated into an elongating nascent protein by the ribosome. The molecular origins of this class of allosteric coupling remain unknown. We demonstrate, using multiscale simulations, that positively charged residues generate large forces that move the P-site amino acid away from the A-site amino acid. Negatively charged residues generate forces of similar magnitude but move the A- and P-sites closer together. These conformational changes, respectively, increase and decrease the transition state barrier height to peptide bond formation, explaining how charged residues mechanochemically alter translation speed. This mechanochemical mechanism is consistent with in vivo ribosome profiling data exhibiting proportionality between translation speed and the number of charged residues, experimental data characterizing nascent chain conformations, and a previously published cryo-EM structure of a ribosome-nascent chain complex containing consecutive lysines. These results expand the role of mechanochemistry in translation and provide a framework for interpreting experimental results on translation speed.

Ethylene-regulated leaf lifespan explains divergent responses of plant productivity to warming among three hydrologically different growing seasons.

Leaf senescence is known to be regulated by the plant hormone ethylene, but how leaf lifespan responds to global environmental change and links to ecosystem-level responses remains largely unexplored. Here we investigated the effects of climate warming and nitrogen addition on plant functional traits, plant hormone ethylene and net primary production in a 13-year field experiment in a desert steppe. Across the last 3 years of the experiment (2016-2018), plant productivity increased under warming only in 2016, when there was above normal precipitation, but consistently increased with nitrogen addition. Warming enhanced net photosynthesis, leaf nitrogen and ethylene production and reduced leaf lifespan in 2016 (a wet year), but not in 2017 (a drought year); the effect of warming in 2018 (a year with normal precipitation) was opposite to 2016, likely due to the below-normal precipitation in the mid-growing season in 2018. Nitrogen addition led to increases in leaf nitrogen, ethylene production and net photosynthesis, and declines in leaf lifespan in 2016 and 2018, but not in 2017. The ethylene-regulated lifespan was further evidenced by the addition of CoCl2 (an ethylene biosynthesis inhibitor) that reduced ethylene production and prolonged lifespan. Structural equation modeling showed that leaf lifespan had a negative effect on plant productivity, both directly and indirectly via its negative effect on net photosynthesis, across all 3 years. Our results demonstrate the divergent responses of leaf lifespan and, in turn, plant productivity to warming under inter-annual and intra-annual precipitation variation, thus linking plant hormone production, functional traits and ecosystem functioning in the face of global environmental change.

Molecular Mechanism of Ultrasound-Induced Structural Defects in Liposomes: A Nonequilibrium Molecular Dynamics Simulation Study.

The use of ultrasound in combination with liposomes is a promising approach to improve drug delivery. To achieve an optimal drug release rate, it is important to understand how ultrasound induces pathways on the liposome surface where drugs can be released from the liposome. To this end, we carry out large-scale ultrasound-induced molecular dynamics simulations for three single lipid component liposomes formed from the commonly used phospholipids: 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dipalmitoylphosphatidylcholine (DPPC), or phosphatidylcholine (POPC). The results show that ultrasound induces the detachment of two leaflets of the DOPC surface, suggesting that the drug release pathway may be through the low lipid packing areas on the stretched surface. In contrast, ultrasound induces pore formation on the surface of DPPC and DOPC, where drugs could escape from the liposomes. While the leaflet detachment and transient pore formation are the mechanisms of DOPC and DPPC, respectively, in both liquid-ordered and liquid-disordered phases, the leaflet detachment mechanism is switched to the transient pore formation mechanism on going from the liquid-ordered phase to the liquid-disordered phase in the POPC liposome. By adding 30% mol cholesterol, the leaflet detachment mechanism is observed in all liposomes. We found that the molecular origin that determines a mechanism is the competition between the intraleaflet and interleaflet interacting energy of lipids. The connection to experimental and theoretical modeling is discussed in some detail.

Influenza A virus protein PA-X suppresses host Ankrd17-mediated immune responses.

Influenza A virus (IAV) PA-X is a critical ribonuclease protein involved in host cell shutoff but its role in modulating the host immune response to IAV infection remains to be addressed. In this study, host cellular proteins that directly interact with PA-X were screened to investigate the biological function of PA-X in the pathogenesis of IAV infection. The protein ankyrin repeat domain 17 (Ankrd17), a positive regulator of inflammatory responses via the retinoic acid-inducible gene-I (RIG-I)-like receptor (RLR) signaling pathway, was identified as a specific PA-X binding partner that preferred PA-X to the PA protein. The N-terminal ankyrin repeats of Ankrd17 are the key domain for the interaction with PA-X rather than PA, which is required for the function of Ankrd17 in elevating the host immune response. Using Ankrd17 knockout and overexpression, we confirmed that PA-X significantly affected the Ankrd17-mediated response to infection in host cells. Our data therefore reveal a novel function for PA-X in the regulation of innate immune pathways via the interaction between PA-X and Ankrd17.

Pilot-scale enrichment of anammox biofilm using secondary effluent as source water.

Enough biomass of anaerobic ammonium oxidation (anammox) bacteria is essential for maintaining a stable partial nitrification/anammox (PN/A) wastewater treatment system. Present enrichment procedures are mainly labor-intensive and inconvenient for up-scaling. A simplified procedure was developed for enrichment of anammox biofilm by using secondary effluent as source water with no supplement of mineral medium and unstrict control of influent dissolved oxygen (DO). Anammox biofilm was successfully enriched in two pilot-scale reactors (XQ-cul and BT-cul) within 250 and 120 days, respectively. The specific anammox activity increased rapidly during the last 2 months in both reactors and achieved 2.54 g N2-N/(m2·d) in XQ-cul and 1.61 g N2-N/(m2·d) in BT-cul. Similar microbial diversity and community structure were obtained in the two reactors despite different secondary effluent being applied from two wastewater treatment plants. Anaerobic ammonium oxidizing bacteria genera abundance reached up to 37.4% and 43.1% in XQ-cul and BT-cul biofilm, respectively. Candidatus Brocadia and Ca. Kuenenia dominated the enriched biofilm. A negligible adverse effect of residual organics and influent DO was observed by using secondary effluent as source water. This anammox biofilm enrichment procedure could facilitate the inoculation and/or bio-augmentation of large-scale mainstream PN/A reactors.