Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

PB2 residue 473 contributes to the mammalian virulence of H7N9 avian influenza virus by modulating viral polymerase activity via ANP32A.

Since the first human infection reported in 2013, H7N9 avian influenza virus (AIV) has been regarded as a serious threat to human health. In this study, we sought to identify the virulence determinant of the H7N9 virus in mammalian hosts. By comparing the virulence of the SH/4664 H7N9 virus, a non-virulent H9N2 virus, and various H7N9-H9N2 hybrid viruses in infected mice, we first pinpointed PB2 as the primary viral factor accounting for the difference between H7N9 and H9N2 in mammalian virulence. We further analyzed the in vivo effects of individually mutating H7N9 PB2 residues different from the closely related H9N2 virus and consequently found residue 473, alongside the well-known residue 627, to be critical for the virulence of the H7N9 virus in mice and the activity of its reconstituted viral polymerase in mammalian cells. The importance of PB2-473 was further strengthened by studying reverse H7N9 substitutions in the H9N2 background. Finally, we surprisingly found that species-specific usage of ANP32A, a family member of host factors connecting with the PB2-627 polymorphism, mediates the contribution of PB2 473 residue to the mammalian adaption of AIV polymerase, as the attenuating effect of PB2 M473T on the viral polymerase activity and viral growth of the H7N9 virus could be efficiently complemented by co-expression of chicken ANP32A but not mouse ANP32A and ANP32B. Together, our studies uncovered the PB2 473 residue as a novel viral host range determinant of AIVs via species-specific co-opting of the ANP32 host factor to support viral polymerase activity.IMPORTANCEThe H7N9 avian influenza virus has been considered to have the potential to cause the next pandemic since the first case of human infection reported in 2013. In this study, we identified PB2 residue 473 as a new determinant of mouse virulence and mammalian adaptation of the viral polymerase of the H7N9 virus and its non-pathogenic H9N2 counterparts. We further demonstrated that the variation in PB2-473 is functionally linked to differential co-opting of the host ANP32A protein in supporting viral polymerase activity, which is analogous to the well-known PB2-627 polymorphism, albeit the two PB2 positions are spatially distant. By providing new mechanistic insight into the PB2-mediated host range determination of influenza A viruses, our study implicated the potential existence of multiple PB2-ANP32 interfaces that could be targets for developing new antivirals against the H7N9 virus as well as other mammalian-adapted influenza viruses.

Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2.

SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus' evolution.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

Characterization of the metabolites of Eucommiae Cortex in rats provides a further insight into its estrogen-like effective substances.

Eucommiae Cortex is one of important traditional Chinese medicines (TCMs) used in Asia for preventing and treating osteoporosis induced by estrogen deficiency. However, the low exposure of prototype components in Eucommiae Cortex in vivo is difficult to interpret its efficacy. Under the guidance of UPLC-Q/TOF-MS, 42 metabolites including 32 lignans and 10 phenolics, 21 of which were new compounds, were isolated from rat urine and feces after oral administration of aqueous extract of E. ulmoides Oliv. by various chromatographic techniques. Their structures were determined based on extensive physicochemical analyses and spectral data. Their absolute configurations were determined by experimental and calculated ECD spectra, along with the calculated NMR with DP4 evaluation. Additionally, all isolated metabolites were evaluated for their estrogen-like activities, and there are 15 metabolites having estrogen-like effects after assessing influences in MCF-7 cells. Further, Dual Luciferase Reporter Gene Assay was used to determine their activation with estrogen receptor, M10 and M11 mixtures, M14, M19, M33, M27, M31, M38-M41 could activate ERα, and M19 and M41 could activate ERß. These results not only clarify the pharmacological substances of Eucommiae Cortex, but also provide a basis for guiding its clinical application.

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19.

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

Crop residue return sustains global soil ecological stoichiometry balance.

Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%-12%), N (7.63%-9.2%), and P (2.62%-5.13%) contents, with an increase in C:N (2.51%-3.42%) and C:P (7.27%-8.00%) ratios, and grain yields (6.12%-8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13-16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win-win situation for agricultural production, ecological protection, and climate change mitigation.

Molecular epidemiology analysis of symptomatic and asymptomatic norovirus infections in Chinese infants.

BACKGROUND: Norovirus is a leading cause of acute gastroenteritis among children. Previous studies based on symptomatic infections indicated that mutations, rather than recombination drove the evolution of the norovirus ORF2. These characteristics were found in hospital-based symptomatic infections, whereas, asymptomatic infections are frequent and contribute significantly to transmission. METHODS: We conducted the first norovirus molecular epidemiology analysis covering both symptomatic and asymptomatic infections derived from a birth cohort study in the northern China. RESULTS: During the study, 14 symptomatic and 20 asymptomatic norovirus infections were detected in 32 infants. Out of the 14 strains that caused symptomatic infections, 12 strains were identified as GII.3[P12], and others were GII.4[P31]. Conversely, 17 asymptomatic infections were caused by GII.4[P31], two by GII.2[P16], and one by GII.4[P16]. Regardless of symptomatic and asymptomatic infections, the mutations were detected frequently in the ORF2 region, and almost all recombination were identified in the RdRp-ORF2 region. The majority of the mutations were located around the predefined epitope regions of P2 subdomain indicating a potential for immune evasion. CONCLUSION: The role of symptomatic as well as asymptomatic infections in the evolution of norovirus needs to be evaluated continuously.

Revealing the characteristics of ZIKV infection through tissue-specific transcriptome sequencing analysis.

BACKGROUND: Recently, Zika virus (ZIKV) re-emerged in India and was potentially associated with microcephaly. However, the molecular mechanisms underlying ZIKV pathogenesis remain to be explored. RESULTS: Herein, we performed a comprehensive RNA-sequencing analysis on ZIKV-infected JEG-3, U-251 MG, and HK-2 cells versus corresponding uninfected controls. Combined with a series of functional analyses, including gene annotation, pathway enrichment, and protein-protein interaction (PPI) network analysis, we defined the molecular characteristics induced by ZIKV infection in different tissues and invasion time points. Data showed that ZIKV infection and replication in each susceptible organ commonly stimulated interferon production and down-regulated metabolic-related processes. Also, tissue-specific immune responses or biological processes (BPs) were induced after ZIKV infection, including GnRH signaling pathway in JEG-3 cells, MAPK signaling pathway in U-251 MG cells, and PPAR signaling pathway in HK-2 cells. Of note, ZIKV infection induced delayed antiviral interferon responses in the placenta-derived cell lines, which potentially explains the molecular mechanism by which ZIKV replicates rapidly in the placenta and subsequential vertical transmission occurs. CONCLUSIONS: Together, these data may provide a systemic insight into the pathogenesis of ZIKV infection in distinct human tissue-derived cell lines, which is likely to help develop prophylactic and therapeutic strategies against ZIKV infection.

Genetic characterization of two G8P[8] rotavirus strains isolated in Guangzhou, China, in 2020/21: evidence of genome reassortment.

BACKGROUND: The G8 rotavirus genotype has been detected frequently in children in many countries and even became the predominant strain in sub-Saharan African countries, while there are currently no reports from China. In this study we described the genetic characteristics and evolutionary relationship between rotavirus strains from Guangzhou in China and the epidemic rotavirus strains derived from GenBank, 2020-2021. METHODS: Virus isolation and subsequent next-generation sequencing were performed for confirmed G8P[8] specimens. The genetic characteristics and evolutionary relationship were analyzed in comparison with epidemic rotavirus sequences obtained from GenBank. RESULTS: The two Guangzhou G8 strains were DS-1-like with the closest genetic distance to strains circulating in Southeast Asia. The VP7 genes of the two strains were derived from a human, not an animal G8 rotavirus. Large genetic distances in several genes suggested that the Guangzhou strains may not have been transmitted directly from Southeast Asian countries, but have emerged following reassortment events. CONCLUSIONS: We report the whole genome sequence information of G8P[8] rotaviruses recently detected in China; their clinical and epidemiological significance remains to be explored further.

Mesoporous ZnO nanosheet as gas sensor for sensitive triethylamine detection.

In this paper, mesoporous ZnO nanosheets were synthesized by the hydrothermal method using polystyrene-polyacrylic acid (PS-PAA) as the template. The morphology, structure, and composition of the samples were characterized by SEM, TEM, XRD, and XPS, and the physical properties of the samples were tested by N2 adsorption-desorption curve. The results showed that the mesoporous ZnO nanosheets presented a flower-like appearance. Each flower is composed of flake petals which consist of nanoparticles of different sizes, with a large specific surface area. Gas sensitivity test results show that the ZnO gas sensor has good triethylamine (TEA) sensing performance. Its response to 50 ppm TEA can reach 43.771, and the detection limit is as low as 1 ppm, showing the characteristics of rapid response/recovery.

Double-layer capsule of mesoporous ZnO@SnO2 for sensitive detection of triethylamine.

To overcome obstacles such as low response and poor selectivity of pure ZnO and SnO2 gas sensors, the ZnO@SnO2 sensor was synthesized by hydrothermal synthesis. The samples were characterized by XRD, XPS, SEM, HRTEM, N2 adsorption-desorption and other techniques. The results show that ZnO@SnO2 forms an n-n-type heterostructure and presents a double-layer capsule with a size of 0.5-4 µm. The results show that compared with pure ZnO and SnO2, the ZnO@SnO2 sensor exhibits a higher response (138.9) to 50 ppm triethylamine (TEA) at 152°C, which is 19.56 times that of the pure ZnO sensor and 21.7 times that of the SnO2 sensor. It has a short response/recovery time (11/11 s), excellent selectivity and cycling stability. Compared with other volatile organic compounds or gases, it has higher selectivity for TEA detection.

SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens.

B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope.

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Four novel bisabolane-type sesquiterpenes from Curcuma longa.

Four novel bisabolane sesquiterpenes, turmerone A (1), turmerone B (2), turmerone C (3), and turmerone D (4), were isolated from the rhizomes of Curcuma longa. Their structures were elucidated on the basis of chemical and spectral analysis, including 1D, 2D NMR analyses and HRESIMS.

SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen.

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study.

Proton pump inhibitors (PPIs) are extensively used for the treatment of gastric acid-related disorders. PPIs appear to be well tolerated and almost have no short-term side effects. However, the clinical adverse reactions of long-term PPI usage are increasingly reported in recent years. So far, there is no study that elucidates the side effect mechanisms of PPIs comprehensively and systematically. In this study, a well-defined small molecule perturbed microarray data set of 344 compounds and 1695 samples was analyzed. With this high-throughput data set, a new index (Identity, I) was designed to identify PPI-specific differentially expressed genes. Results indicated that (1) up-regulated genes, such as RETSAT, CYP1A1, CYP1A2, and UGT, enhanced vitamin A's metabolism processes in the cellular retinol metabolism pathway; and that (2) down-regulated genes, such as C1QA, C1QC, C4BPA, C4BPB, CFI, and SERPING1, enriched in the complement and coagulation cascades pathway. In addition, strong association was observed between these PPI-specific differentially expressed genes and the reported side effects of PPIs by the gene-disease association network analysis. One potential toxicity mechanism of PPIs as suggested from this systematic PPI-specific gene expression analysis is that PPIs are enriched in acidic organelles where they are activated and inhibit V-ATPases and acid hydrolases, and consequently block the pathways of antigen presentation, the synthesis and secretion of cytokines, and complement component proteins and coagulation factors. The strategies developed in this work could be extended to studies on other drugs.

A meta-analysis on crop growth and heavy metals accumulation with PGPB inoculation in contaminated soils.

Plant growth-promoting bacteria (PGPB) offer a promising solution for mitigating heavy metals (HMs) stress in crops, yet the mechanisms underlying the way they operate in the soil-plant system are not fully understood. We therefore conducted a meta-analysis with 2037 observations to quantitatively evaluate the effects and determinants of PGPB inoculation on crop growth and HMs accumulation in contaminated soils. We found that inoculation increased shoot and root biomass of all five crops (rice, maize, wheat, soybean, and sorghum) and decreased metal accumulation in rice and wheat shoots together with wheat roots. Key factors driving inoculation efficiency included soil organic matter (SOM) and the addition of exogenous fertilizers (N, P, and K). The phylum Proteobacteria was identified as the keystone taxa in effectively alleviating HMs stress in crops. More antioxidant enzyme activity, photosynthetic pigment, and nutrient absorption were induced by it. Overall, using PGPB inoculation improved the growth performance of all five crops, significantly increasing crop biomass in shoots, roots, and grains by 33 %, 35 %, and 20 %, respectively, while concurrently significantly decreasing heavy metal accumulation by 16 %, 9 %, and 37 %, respectively. These results are vital to grasping the benefits of PGPB and its future application in enhancing crop resistance to HMs.

One HA stalk topping multiple heads as a novel influenza vaccine.

Classic chimeric hemagglutinin (cHA) was designed to induce immune responses against the conserved stalk domain of HA. However, it is unclear whether combining more than one HA head domain onto one stalk domain is immunogenic and further induce immune responses against influenza viruses. Here, we constructed numerous novel cHAs comprising two or three fuzed head domains from different subtypes grafted onto one stalk domain, designated as cH1-H3, cH1-H7, cH1-H3-H7, and cH1-H7-H3. The three-dimensional structures of these novel cHAs were modelled using bioinformatics simulations. Structural analysis showed that the intact neutralizing epitopes were exposed in cH1-H7 and were predicted to be immunogenic. The immunogenicity of the cHAs constructs was evaluated in mice using a chimpanzee adenoviral vector (AdC68) vaccine platform. The results demonstrated that cH1-H7 expressed by AdC68 (AdC68-cH1-H7) induced the production of high levels of binding antibodies, neutralizing antibodies, and hemagglutinin inhibition antibodies against homologous pandemic H1N1, drifted seasonal H1N1, and H7N9 virus. Moreover, vaccinated mice were fully protected from a lethal challenge with the aforementioned influenza viruses. Hence, cH1-H7 cHAs with potent immunogenicity might be a potential novel vaccine to provide protection against different subtypes of influenza virus.