Global optimization radiometric calibration method for an infrared system with a wide dynamic range.

Considering the conventional calibration restriction of the complicated calibration procedures, narrow dynamic range, and less correlation in the calibration data, a global optimization radiometric calibration method is proposed in this paper. First, a unified database is generated by integrating different gray-level images, neutral density attenuators, integration times, and target radiations under the deduced infrared physical model. Then, the calibration coefficients are automatically learned through the relative error backward propagation network. Finally, experiments are conducted on a large-aperture ground-based infrared system to evaluate the effectiveness of the proposed method. The results indicate the proposed method can solve the problem of learning imbalance with large fluctuations of infrared radiation, ensure global measurement precision with a simpler calibration procedure, and accurately measure the internal stray radiation of the optical system.

Accurate de novo peptide sequencing using fully convolutional neural networks.

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

Longitudinal Serum Proteomics Characterization of CD19-CAR-T Cell Therapy for B-Cell Malignancies.

Chimeric antigen receptor (CAR)-modified T cells have demonstrated remarkable efficacy in treating B-cell leukemia. However, treated patients may potentially develop side effects, such as cytokine release syndrome (CRS), the mechanisms of which remain unclear. Here, we collected 43 serum samples from eight patients with B-cell acute lymphoblastic leukemia (B-ALL) before and five time points after CD19-specific CAR-T cell treatment. Using TMTpro 16-plex-based quantitative proteomics, we quantified 1151 proteins and profiled the longitudinal proteomes analysis of each patient. Seven days after therapy, we found the most dysregulated inflammatory proteins. Lipid metabolism proteins, including APOA1, decreased after therapy, reached their minimum after 7 days, and then gradually recovered. Hence, APOA1 has been selected as a potential biomarker of the CRS disease progression. Furthermore, we identified CD163 as a potential biomarker of CRS severity. These two biomarkers were successfully validated using targeted proteomics in an independent cohort. Our study provides new insights into CAR-T cell therapy-induced CRS. The biomarkers we identified may help develop targeted drugs and monitoring strategies.

Apigenin attenuates atherosclerosis and non-alcoholic fatty liver disease through inhibition of NLRP3 inflammasome in mice.

Apigenin (APN), a flavone found in several plant foods with various biological properties such as anti-obesity, anti-inflammation and other abilities, alleviates atherosclerosis and non-alcoholic fatty liver disease (NAFLD) induced by a high fat diet (HFD) in mice. However, the underlying mechanisms have not been fully understood. In this study, we investigated the role of NLRP3 in anti-atherosclerosis and anti-NAFLD effect of APN in mouse models with NLRP3 deficiency. Atherosclerosis and NAFLD models were established by treatment of low density lipoprotein receptor-deficient (Ldlr-/-) mice and NLRP3-/- Ldlr-/- mice with a HFD diet (20% fat and 0.5% cholesterol) with or without APN. En face lipid accumulation analysis, plasma lipid levels, hepatic lipid accumulation and inflammation were analyzed and quantified. For in vitro experiments, HepG2 cells were stimulated by LPS plus oleic acid (OA) in the absence or presence of APN (50 µM). Lipid accumulation and the effect of APN on the NLRP3/NF-κB signaling pathway were investigated. APN administration partly reversed atherosclerosis and hepatic lipid accumulation, and decreased body weight and plasma lipid levels in Ldlr-/- mice when fed a HFD. Compared with Ldlr-/- mice, NLRP3-/- Ldlr-/- mice showed more severe atherosclerosis and hepatic lipid accumulation. Treating the HepG2 cells with APN reduced lipid accumulation. APN also inhibited activation of the NLRP3/ NF-κB signaling pathway stimulated by OA together with LPS. Our results indicate that APN supplementation prevents atherosclerosis and NAFLD via NLRP3 inhibition in mice, and suggest that APN might be a potential therapeutic agent for the prevention of atherosclerosis and NAFLD.

3DMolMS: prediction of tandem mass spectra from 3D molecular conformations.

MOTIVATION: Tandem 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 silico methods 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' 3D conformations, and thus neglected critical structural information. RESULTS: We present the 3D Molecular Network for Mass Spectra 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 0.691 and 0.478 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 the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. AVAILABILITY AND IMPLEMENTATION: The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.

Non-Specific Signal Peptidase Processing of Extracellular Proteins in Staphylococcus aureus N315.

Staphylococcus aureus is one of the major community-acquired human pathogens, with growing multidrug-resistance, leading to a major threat of more prevalent infections to humans. A variety of virulence factors and toxic proteins are secreted during infection via the general secretory (Sec) pathway, which requires an N-terminal signal peptide to be cleaved from the N-terminus of the protein. This N-terminal signal peptide is recognized and processed by a type I signal peptidase (SPase). SPase-mediated signal peptide processing is the crucial step in the pathogenicity of S. aureus. In the present study, the SPase-mediated N-terminal protein processing and their cleavage specificity were evaluated using a combination of N-terminal amidination bottom-up and top-down proteomics-based mass spectrometry approaches. Secretory proteins were found to be cleaved by SPase, specifically and non-specifically, on both sides of the normal SPase cleavage site. The non-specific cleavages occur at the relatively smaller residues that are present next to the -1, +1, and +2 locations from the original SPase cleavage site to a lesser extent. Additional random cleavages at the middle and near the C-terminus of some protein sequences were also observed. This additional processing could be a part of some stress conditions and unknown signal peptidase mechanisms.

GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching.

Liquid chromatography coupled with tandem mass spectrometry 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 data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

Relationship between body mass index and physical fitness index in Chinese college students: Results from a cross-sectional survey.

BACKGROUND: To explore the cross-sectional relationship between the body mass index (BMI) and physical fitness index (PFI) of Chinese college students. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Association between Mutation in SMARCAD1 and Basan Syndrome with Cutaneous Squamous Cell Carcinoma.

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.

Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data.

BACKGROUND: A 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. Video Abstract METHODS: Here, 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). RESULTS: We 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. CONCLUSIONS: The 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.

Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics.

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.

Nr2e3 is a genetic modifier that rescues retinal degeneration and promotes homeostasis in multiple models of retinitis pigmentosa.

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.

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

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.

Cancer-associated Fibroblasts induce epithelial-mesenchymal transition via the Transglutaminase 2-dependent IL-6/IL6R/STAT3 axis in Hepatocellular Carcinoma.

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.

A Fast and Memory-Efficient Spectral Library Search Algorithm Using Locality-Sensitive Hashing.

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.

Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network.

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, that is, 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 nonbackbone 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 multitask 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+).

Decitabine enhances cytotoxic effect of T cells with an anti-CD19 chimeric antigen receptor in treatment of lymphoma.

Background: CD19-directed chimeric antigen receptor (CAR) T cells have substantial benefit in the treatment of patients with B-cell malignancies. However, despite encouraging therapeutic efficiency, there is limited overall response rate when anti-CD19 CAR-T cells are used to treat patients with relapsed and refractory (R/R) B cell lymphomas. Therefore, it further investigation is urgently needed to improve treatment efficacy. Method: A combined treatment protocol of CAR-T cell with decitabine (DAC) to treat B cell lymphoma was developed and tested on lymphoma cell lines first, and then efficacy and the underlying mechanism were investigated. After ethical approval was granted, the combined treatment protocol was applied to treat two patients with R/R B-cell lymphomas. Results: CAR-T cells were prepared successfully, and they recognized CD19 antigen expressed on lymphoma cell lines specifically. Cell-line studies also showed that CD19 antigen expression was increased by DAC pretreatment, and the function of CAR-T cells was not compromised. The cell-line study further demonstrated that lymphoma cells pretreated by DAC responded more to the treatment of CAR-T cells. Two patients with R/R B cell lymphoma were pretreated with DAC then treated with CAR-T, and both achieved complete remission (CR). Conclusions: The epigenetic modifying drug DAC increases expression of the surface antigen CD19 on lymphoma cells. The DAC pretreatment protocol may lead patients with B cell lymphoma to be more susceptible to adoptive transfer of anti-CD19 CAR-T cells treKeywordsatment.