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MotivationTandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years,in silicomethods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds’ three-dimensional (3D) conformations, and thus neglected critical structural information.ResultsWe present the3D Molecular Network forMassSpectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.687 and 0.475 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that themolecular representationlearned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time (ET) in the liquid chromatography and the Collisional Cross Section (CCS) measured by ion mobility spectrometry (IMS), both of which are often used to improve compound identification.Contacthatang@indiana.eduSupplementary informationThe codes of 3DMolMS is available athttps://github.com/JosieHong/3DMolMSand the web service is athttps://spectrumprediction.gnps2.org.

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g. the commercial software Byonic) analyzes the individual dataset and does not exploit the redundant spectra of glycopeptides presented in the related datasets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic datasets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic datasets showed that the concurrent approach can identify 105% - 224% more spectra as glycopeptides compared to the glycopeptide identification on individual datasets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

To explore the cross-sectional relationship between the body mass index (BMI) and physical fitness index (PFI) of Chinese college students.In total, 30 497 (male 16 197, 55.5%) Chinese college students aged 19-22 were tested for height, weight and five physical fitness indicators (50-m sprint, sit and reach, standing long jump, 1000/800-m run, pull-up/bent-leg sit-up). Stratified according to age and gender, according to BMI percentile, divided into BMI 10th Percentile (A), 10th ≤ BMI 25th Percentile (B), 25th ≤ BMI 75th Percentile (C), 75th ≤ BMI 90th Percentile (D), BMI ≥ 90th Percentile (E), a total of 5 groups, and the PFI composed of 5 indicators of physical fitness was calculated according to age and gender. The comparison of PFI between different BMI groups was carried out using the effect size, and the non-linear relationship between BMI-Z and PFI was analyzed.The relationship between BMI-Z and PFI of male and female college students in China showed an inverted "U"-shaped curve. With comparable BMI, PFI change was greater in males than in females.The relationship between BMI and PFI of Chinese college students is nonlinear. The physical fitness level of college students who are underweight or overweight and those with obesity is lower than that of normal-weight students.

To achieve security in wireless sensor networks (WSNs), it is important to be able to encrypt messages sent among sensor nodes by using shared keys between them. Due to resource constraints, achieving such key agreement in WSNs is nontrivial. Previous research indicates that key management schemes using deployment knowledge can significantly improve the performance of WSNs. Nevertheless, in these schemes, resilient local connectivity and resilient global connectivity become unstable when deployment error changes. To resolve the above problem, in this article, a one-way associated key management model is proposed. In this model, the key pool consists of two layers: 1) the global layer and 2) the local layer. According to different deployment errors, the number of keys allocated from the global key pool and local key pools can be dynamically adjusted, thereby improving the stability of networks’ performance. In multiphase sensor networks, analysis and simulation indicate that our scheme has better adaptability in applications where deployment error changes as compared with related schemes.

We present the first proteomic analysis on the cellular response to severe acute respiratory syndrome-associated coronavirus (SARS-CoV) infection. The differential proteomes of Vero E6 cells with and without infection of the SARS-CoV were resolved and quantitated with two-dimensional differential gel electrophoresis followed by ESI-MS/MS identification. Moreover isotope-coded affinity tag technology coupled with two-dimensional LC-MS/MS were also applied to the differential proteins of infected cells. By combining these two complementary strategies, 355 unique proteins were identified and quantitated with 186 of them differentially expressed (at least 1.5-fold quantitative alteration) between infected and uninfected Vero E6 cells. The implication for cellular responses to virus infection was analyzed in depth according to the proteomic results. Thus, the present work provides large scale protein-related information to investigate the mechanism of SARS-CoV infection and pathogenesis.

Recent advances in viral vector engineering, as well as an increased understanding of the cellular and molecular mechanism of retinal diseases, have led to the development of novel gene therapy approaches. Furthermore, ease of accessibility and ocular immune privilege makes the retina an ideal target for gene therapies. In this study, the nuclear hormone receptor gene Nr2e3 was evaluated for efficacy as broad-spectrum therapy to attenuate early to intermediate stages of retinal degeneration in five unique mouse models of retinitis pigmentosa (RP). RP is a group of heterogenic inherited retinal diseases associated with over 150 gene mutations, affecting over 1.5 million individuals worldwide. RP varies in age of onset, severity, and rate of progression. In addition, ~40% of RP patients cannot be genetically diagnosed, confounding the ability to develop personalized RP therapies. Remarkably, Nr2e3 administered therapy resulted in reduced retinal degeneration as observed by increase in photoreceptor cells, improved electroretinogram, and a dramatic molecular reset of key transcription factors and associated gene networks. These therapeutic effects improved retinal homeostasis in diseased tissue. Results of this study provide evidence that Nr2e3 can serve as a broad-spectrum therapy to treat multiple forms of RP.

The ability to predict tandem mass (MS/MS) spectra from peptide sequences can significantly enhance our understanding of the peptide fragmentation process and could improve peptide identification in proteomics. However, current approaches for predicting high-energy collisional dissociation (HCD) spectra are limited to predict the intensities of expected ion types, i.e., the a/b/c/x/y/z ions and their neutral loss derivatives (referred to as backbone ions). In practice, backbone ions only account for < 70% of total ion intensities in HCD spectra, indicating many intense ions are ignored by current predictors. In this paper, we present a deep learning approach that can predict the complete spectra (both backbone and non-backbone ions) directly from peptide sequences. We made no assumptions or expectations on which kind of ions to predict but instead predicting the intensities for all possible m/z. Training this model needs no annotations of fragment ion nor any prior knowledge of the fragmentation rules. Our analyses show that the predicted 2+ and 3+ HCD spectra are highly similar to the experimental spectra, with average full-spectrum cosine similarities of 0.820 (±0.088) and 0.786 (±0.085), respectively, very close to the similarities between the experimental replicated spectra. In contrast, the best-performed backbone only models can only achieve an average similarity below 0.75 and 0.70 for 2+ and 3+ spectra, respectively. Furthermore, we developed a multi-task learning (MTL) approach for predicting spectra of insufficient training samples, which allows our model to make accurate predictions for electron transfer dissociation (ETD) spectra and HCD spectra of less abundant charges (1+ and 4+).

Cancer-associated fibroblasts (CAFs) play crucial roles in enhancing cell survival, proliferation, invasion, and metastasis. We previously showed that hepatocellular carcinoma-derived CAFs (H-CAFs) promoted proliferation of hepatocellular carcinoma (HCC) cells. This study aimed to further explore the role of CAFs in HCC epithelial-mesenchymal transition (EMT) and the underlying mechanism. High CAF density was significantly associated with liver cirrhosis, inferior clinicopathologic characteristics, elevated EMT-associated markers, and poorer survival in human HCC. Within HCC cells, EMT was induced after co-culture with H-CAFs. Secretomic analysis showed that IL-6 and HGF were the key EMT-stimulating cytokines secreted by H-CAFs. Proteomic analysis revealed that TG2 was significantly upregulated in HCC cells with EMT phenotypes. Overexpression of TG2 promoted EMT of HCC cells, and knockdown of TG2 remarkably attenuated the H-CAF-induced EMT. Furthermore, during EMT, TG2 expression was enhanced after HCC cells were stimulated by IL-6, but not HGF. Inhibition of the IL-6/STAT3 signaling decreased TG2 expression. The principal TG2 transcription control element and a potential STAT3 binding site were identified using promoter analysis. Hence, H-CAFs facilitates HCC cells EMT mediated by IL-6, which in turn activates IL-6/IL6R/STAT3 axis to promote TG2 expression.

Background. Basan syndrome is a rare autosomal-dominant ectodermal dysplasia with certain clinic-pathological features caused by mutations in the SMARCAD1 gene. Currently, no skin malignancy related to Basan syndrome has been reported. This study was aimed at identifying related gene mutations in a new Chinese pedigree with Basan syndrome and discovering the possible association between Basan syndrome and cutaneous squamous cell carcinoma (cSCC). Methods. We report a case of Basan syndrome from China with family history of cSCC. The pedigree contains 28 individuals. Among them, 12 members had Basan syndrome, while 4 affected members were diagnosed with cSCC in the 1st and 2nd generations. Whole exome sequencing (WES) and Sanger sequencing were performed for 7 available individuals. The specific gene mutation on pre-mRNA splicing was also analyzed using in vitro Minigene assay. In addition, sequencing data was analyzed with bioinformatics workflow, aiming to discover the gene associated with cSCC. Results. Gene sequencing results showed a heterozygous mutation, c.378+5G>A, in the SMARCAD1 gene in all tested individuals with Basan syndrome. Minigene result implicated the specific mutation may cause splicing variations by exon skipping occurring in the targeted exons. Conclusion. To the best of our knowledge, this is the first study reported Basan syndrome with family history of cSCC. Despite in this study we cannot draw any conclusion about the association between Basan syndrome and cSCC at the genetic level, this study encourages future works to substantiate this potential but important issue.

Purpose: This study aimed to evaluate whether bathing newborns within 36 hours after birth has a temporary negative impact on skin barrier metrics and relates to atopic dermatitis (AD) development.Methods: 55 newborns were bathed with water at 24-36 hours after birth, while 102 newborns did not have bath. Skin barrier metrics, including facial and underarm transepidermal water loss (TEWL), stratum corneum hydration (SCH), and skin humidity, were evaluated 1 hour before and after bath (T0 and T1, respectively), as well as on the next day (T2). A follow-up questionnaire via telephone was conducted 12-month later (T3). AD screening was determined based on the questionnaire.Results: In the non-bathed newborns, no significant alteration in skin barrier function was observed. In the bathed newborns, the underarm SCH significantly deteriorated from 16.60±21.74 at T0 to 8.16±12.27 at T2 (51% deterioration, p=0.009). Deteriorations were also observed for the facial and underarm humidity right after bath (-5%, p=0.017 and -4%, p=0.012) as well as on the next day (-6%, p=0.003 and -5%, p=0.001). At T3, 5% non-bathed newborns were determined to have developed AD during the past 12-month, while this number increased by 4-fold (21%) in the bathed newborns (p=0.010).Conclusion: This study demonstrated a temporary negative impact on newborn’s skin barrier function caused by bathing within 36 hours after birth. Deteriorated skin barrier function in the temporary window may still leave the newborn susceptible to infection and allergy, increasing the risk of triggering AD onset.

Background: Apigenin, a flavone found in several plant foods with various biological properties including anti-inflammatory and other abilities, alleviated non-alcohol fatty liver disease (NAFLD) induced by a high fat diet (HFD) in mice. However, the mechanisms underlying this protection of inflammation and NAFLD has not been known clearly. Methods: Low density lipoprotein receptor-deficient (Ldlr-/-) mice were fed with HFD diet to induce NAFLD model and were treated with apigenin (50 mg/kg/day) for eight weeks. Hepatic lipid accumulation and inflammation in the livers were analyzed and quantified. In vitro experiments, HepG2 cells were stimulated by LPS plus oleic acid (OA) in the absence of presence of apigenin (50μM). Lipid accumulation and the effect of apigenin on NLRP3/NF-κB signaling pathway was investigated.Results: Apigenin administration reduce the weight, plasma lipid levels in Ldlr-/- mice when fed an HFD. Apigenin (50 mg/kg/day) treated mice displayed reduced hepatic lipid accumulation and inflammation in the livers of mice given the HFD diet. Treating the HepG2 cells with apigenin reduced lipid accumulation. And, apigenin also inhibited activation of NLRP3/NF-κB signaling pathway stimulated by OA together with LPS. Conclusions: Our results indicated that apigenin supplementation prevented NAFLD via down-regulating the NLRP3/NF-κB signaling pathway in mice, and suggested apigenin might be a potential therapeutic agent for the prevention of NAFLD.

Unattended wireless sensor networks (UWSNs) are vulnerable to mobile sink (MS) replication attack. In this attack, using the compromised key information, an attacker can collect data from networks by impersonating sinks. To resist such an attack, some schemes have been proposed. To improve the resilience of MS replication attack of these schemes, we can integrate them with schemes based on deployment knowledge. However, there are the following defects: 1) the probability of mutual authentication between a MS and a sensor node is less than 1 and 2) during the authentication phase, the energy consumption of sensor nodes increases significantly as the deployment area expands. In this paper, we construct 3-D backward key chains based on deployment knowledge and propose a new authentication scheme based on these. As compared with these existing related schemes, the detailed theory analysis and simulation results indicate that the scheme can ensure that a MS can be authenticated by sensor nodes, and can improve the resilience of networks’ MS replication attack with low energy consumption.

Objective The morbidity and prevalence of type 2 diabetes mellitus (DM) are increasing in the elderly population. Interleukin 37 (IL-37) play important roles in anti-inflammatory and anti-bacteria immune responses, but its role in the development of type 2 DM in the elderly is unclear. Therefore, we investigated whether IL-37 is associated with type 2 DM in the elderly and the underlying mechanism. Methods Hospitalized patients (aged 65–95 years) with recently diagnosed type 2 diabetes mellitus were studied retrospectively and compared with healthy subjects without glucose metabolism abnormalities. A diabetic mouse model was established by feeding ob/ob mice (C57BL/6) a high-fat, carbohydrate-free diet. Plasma glucose and insulin levels were determined by glucose oxidase assay and radioimmunoassay, respectively. The IL-37 expression level was determined by real-time PCR, western blot and ELISA (Enzyme-linked immunoassay). Results Statistic analysis showed that the IL-37 level was significantly associated with type 2 DM and insulin resistance in the elderly. The patients were then divided into insulin therapy sensitive and resistant group according to their response to insulin therapy. Data showed that the IL-37 was highly expressed in the insulin therapy sensitive group. And this was related to the less severe gut microbiota dysbiosis. In the mice model, overexpressing the IL-37 could suppress the gut microbiota dysbiosis and also the diabetes development. Conclusion Thus our results showed that higher IL-37 was associated with increased insulin sensitive in elderly type 2 DM patients through suppressing the gut microbiota dysbiosis.

RATIONALE Proteins undergo post-translational modifications and proteolytic processing that can affect their biological function. Processing often involves the loss of single residues. Cleavage of signal peptides from the N-terminus is commonly associated with translocation. Recent reports have suggested that other processing sites also exist. METHODS The secreted proteins from S. aureus N315 were precipitated with trichloroacetic acid (TCA) and amidinated with S-methyl thioacetimidate (SMTA). Amidinated proteins were digested with trypsin and analyzed with a high-resolution orbitrap mass spectrometer. RESULTS Sixteen examples of Staphylococcus aureus secretory proteins that lose an N-terminal signal peptide during their export were identified using this amidination approach. The N-termini of proteins with and without methionine were identified. Unanticipated protein cleavages due to sortase and an unknown protease were also uncovered. CONCLUSIONS A simple N-terminal amidination based mass spectrometry approach is described that facilitates identification of the N-terminus of a mature protein and the discovery of unexpected processing sites.

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE–host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR–Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid–host and phage–host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant ‘Candidatus Microthrix parvicella’ population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes., CRISPR-based immunity and microbial community dynamics in a wastewater treatment plant over one and a half years.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts., Herold et al. present an integrated meta-omics framework to investigate how mixed microbial communities, such as oleaginous bacterial populations in biological wastewater treatment plants, respond with distinct adaptation strategies to disturbances. They show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity.

BackgroundA few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time.MethodsHere, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the High Abundance Proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification).ResultsWe tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data.ConclusionsThe HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.