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Wampee (Clausena lansium) is an important evergreen fruit tree native to southern China that has a long history of use for medicinal purposes. Here, a chromosome-level genome of C. lansium was constructed with a genome size of 282.9 Mb and scaffold N50 of 30.75 Mb. The assembled genome contains 48.70% repetitive elements and 24,381 protein-coding genes. Comparative genomic analysis showed that C. lansium diverged from Aurantioideae 15.91–24.95 million years ago. Additionally, some expansive and specific gene families related to methyltransferase activity and S-adenosylmethionine-dependent methyltransferase activity were also identified. Further analysis indicated that N-methyltransferase (NMT) is mainly involved in alkaloid biosynthesis and O-methyltransferase (OMT) participates in the regulation of coumarin accumulation in wampee. This suggested that wampee's richness in alkaloids and coumarins might be due to the gene expansions of NMT and OMT. The tandem repeat event was one of the major reasons for the NMT expansion. Hence, the reference genome of C. lansium will facilitate the identification of some useful medicinal compounds from wampee resources and reveal their biosynthetic pathways.

Abstract Skull-base chordoma is a rare, aggressive bone cancer with a high recurrence rate. Despite advances in genomic studies, its molecular characteristics and effective therapies remain unknown. Here, we conduct integrative genomics, transcriptomics, proteomics, and phosphoproteomics analyses of 187 skull-base chordoma tumors. In our study, chromosome instability is identified as a prognostic predictor and potential therapeutic target. Multi-omics data reveals downstream effects of chromosome instability, with RPRD1B as a putative target for radiotherapy-resistant patients. Chromosome 1q gain, associated with chromosome instability and upregulated mitochondrial functions, lead to poorer clinical outcomes. Immune subtyping identify an immune cold subtype linked to chromosome 9p/10q loss and immune evasion. Proteomics-based classification reveals subtypes (P-II and P-III) with high chromosome instability and immune cold features, with P-II tumors showing increased invasiveness. These findings, confirmed in 17 paired samples, provide insights into the biology and treatment of skull-base chordoma.

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan–Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

Abstract Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

Abstract Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)‐lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1‐lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06–6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1‐lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60–93.5), p = 0.015]. We obtained three‐tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high‐risk profile presented the highest recurrence rate compared with those in the medium‐risk and low‐risk profiles [HR: 3.54, (95% CI, 1.40–8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1‐lineage tumors. Typical morphological characteristics of high‐risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1‐lineage hormones.

Litchi exhibits a large number of flowers, many flowering batches, and an inconsistent ratio of male and female flowers, frequently leading to a low fruit-setting rate. Floral sexual differentiation is a crucial phase in perennial trees to ensure optimal fruit production. However, the mechanism behind floral differentiation remains unclear. The objective of the study was to identify the role of auxin in floral differentiation at the transcriptional level. The results showed that the ratio of female flowers treated with naphthalene acetic acid (NAA) was significantly lower than that of the control stage (M0/F0). The levels of endogenous auxin and auxin metabolites were measured in male and female flowers at different stages of development. It was found that the levels of IAA, IAA-Glu, IAA-Asp, and IAA-Ala were significantly higher in male flowers compared to female flowers. Next-generation sequencing and modeling were employed to perform an in-depth transcriptome analysis on all flower buds in litchi ‘Feizixiao’ cultivars (Litchi chinensis Sonn.). Plant hormones were found to exert a significant impact on the litchi flowering process and flower proliferation. Specifically, a majority of differentially expressed genes (DEGs) related to the auxin pathway were noticeably increased during male flower bud differentiation. The current findings will enhance our comprehension of the process and control mechanism of litchi floral sexual differentiation. It also offers a theoretical foundation for implementing strategies to regulate flowering and enhance fruit production in litchi cultivation.

Abstract Background Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based measurements among different instruments and laboratories. Results Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC–MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. Conclusion Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

The molecular differences between the two major gastric cancer subtypes diffuse-type gastric cancer (DGC) and intestinaltype gastric cancer (IGC) remain to be investigated. Here, integrated analysis of proteome, phospho-proteome and transcription factor activity for DGC and IGC reveals potential subtypes.

Macrophage is located in different tissue to serve diverse functions. Here the authors use mass spectrometry and bulk RNA-sequencing to profile 11 mouse macrophage populations from 8 tissues, and combine their de novo data with public datasets to report an integrated proteomic and transcriptomic landscape of mouse macrophage as a valuable resource.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with poor prognosis. Proteogenomic characterization and integrative proteomic analysis provide a functional context to annotate genomic abnormalities with prognostic value. Methods We performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 217 PDAC tumors with paired non-tumor adjacent tissues. In vivo functional experiments were performed to further illustrate the biological events related to PDAC tumorigenesis and progression. Results A comprehensive proteogenomic landscape revealed that TP53 mutations upregulated the CDK4-mediated cell proliferation process and led to poor prognosis in younger patients. Integrative multi-omics analysis illustrated the proteomic and phosphoproteomic alteration led by genomic alterations such as KRAS mutations and ADAM9 amplification of PDAC tumorigenesis. Proteogenomic analysis combined with in vivo experiments revealed that the higher amplification frequency of ADAM9 (8p11.22) could drive PDAC metastasis, though downregulating adhesion junction and upregulating WNT signaling pathway. Proteome-based stratification of PDAC revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Immune clustering defined a metabolic tumor subset that harbored FH amplicons led to better prognosis. Functional experiments revealed the role of FH in altering tumor glycolysis and in impacting PDAC tumor microenvironments. Experiments utilizing both in vivo and in vitro assay proved that loss of HOGA1 promoted the tumor growth via activating LARP7-CDK1 pathway. Conclusions This proteogenomic dataset provided a valuable resource for researchers and clinicians seeking for better understanding and treatment of PDAC.

Genome-wide physical protein–protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we reported a high-efficiency BiFC-seq method, yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation (yEGFP-BiFC) coupled with next-generation DNA sequencing, for interactome mapping. We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus. Two-thirds (9/14) of known interactions of EBOV were recaptured, and five novel interactions were discovered. Next, we used the BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53, more than three-quarters of which were novel. Furthermore, in a more complex background, we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid, and economical method for genome-wide interactome mapping.

After decades of development, protein and peptide drugs have now grown into a major drug class in the marketplace. Target identification and validation are crucial for the discovery of protein and peptide drugs, and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection. However, owing to the developmental history in the pharmaceutical industry, previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs, while studies related to protein and peptide drugs are lacking. Here, we systematically explore the target spaces in the human genome specifically for protein and peptide drugs. Compared with other proteins, both successful protein and peptide drug targets have many special characteristics, and are also significantly different from those of small-molecule drugs in many aspects. Based on these features, we develop separate effective genome-wide target prediction models for protein and peptide drugs. Finally, a user-friendly web server, Predictor Of Protein and PeptIde drugs’ therapeutic Targets (POPPIT) (http://poppit.ncpsb.org.cn/), is established, which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

Abstract Background Although cholesterol metabolism is a common pathway for the development of antitumor drugs, there are no specific targets and drugs for clinical use. Here, based on our previous study of sterol O-acyltransferase 1 (SOAT1) in hepatocelluar carcinoma, we sought to screen an effective targeted drug for precise treatment of hepatocelluar carcinoma and, from the perspective of cholesterol metabolism, clarify the relationship between cholesterol regulation and tumorigenesis and development. Methods In this study, we developed a virtual screening integrated affinity screening technology for target protein drug screening. A series of in vitro and in vivo experiments were used for drug activity verification. Multi-omics analysis and flow cytometry analysis were used to explore antitumor mechanisms. Comparative analysis of proteome and transcriptome combined with survival follow-up information of patients reveals the clinical therapeutic potential of screened drugs. Results We screened three compounds, nilotinib, ABT-737, and evacetrapib, that exhibited optimal binding with SOAT1. In particular, nilotinib displayed a high affinity for SOAT1 protein and significantly inhibited tumor activity both in vitro and in vivo. Multi-omics analysis and flow cytometry analysis indicated that SOAT1-targeting compounds reprogrammed the cholesterol metabolism in tumors and enhanced CD8+ T cells and neutrophils to suppress tumor growth. Conclusions Taken together, we reported several high-affinity SOAT1 ligands and demonstrated their clinical potential in the precision therapy of liver cancer, and also reveal the potential antitumor mechanism of SOAT1-targeting compounds. Graphical Abstract

BackgroundSurgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.MethodsPatients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.Results118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.ConclusionThe nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.

PROTAC is a rapidly developing engineering technology for targeted protein degradation using the ubiquitin–proteasome system, which has promising applications for inflammatory diseases, neurodegenerative diseases, and malignant tumors. This paper gives a brief overview of the development and design principles of PROTAC, with a special focus on PROTAC-based explorations in recent years aimed at achieving controlled protein degradation and improving the bioavailability of PROTAC, as well as TPD technologies that use other pathways such as autophagy and lysosomes to achieve targeted protein degradation.

Squamous cell carcinomas are an aggressive cancer type which can occur in multiple organ systems. Here, the authors analyse the proteome of SCC cancers from 17 organs and show commonly dysregulated proteins independent of location.

Abstract Background Dysfunctional p53 signaling is one of the major causes of hepatocellular carcinoma (HCC) tumorigenesis and development, but the mechanisms underlying p53 inactivation in HCC have not been fully clarified. The role of Krüppel-associated box (KRAB)-type zinc-finger protein ZNF498 in tumorigenesis and the underlying mechanisms are poorly understood. Methods Clinical HCC samples were used to assess the association of ZNF498 expression with clinicopathological characteristics and patient outcomes. A mouse model in which HCC was induced by diethylnitrosamine (DEN) was used to explore the role of ZNF498 in HCC initiation and progression. ZNF498 overexpression and knockdown HCC cell lines were employed to examine the effects of ZNF498 on cellular proliferation, apoptosis, ferroptosis and tumor growth. Western blotting, immunoprecipitation, qPCR, luciferase assays and flow cytometry were also conducted to determine the underlying mechanisms related to ZNF498 function. Results ZNF498 was found to be highly expressed in HCC, and increased ZNF498 expression was positively correlated with advanced pathological grade and poor survival in HCC patients. Furthermore, ZNF498 promoted DEN-induced hepatocarcinogenesis and progression in mice. Mechanistically, ZNF498 directly interacted with p53 and suppressed p53 transcriptional activation by inhibiting p53 Ser46 phosphorylation. ZNF498 competed with p53INP1 for p53 binding and suppressed PKCδ- and p53INP1-mediated p53 Ser46 phosphorylation. In addition, functional assays revealed that ZNF498 promoted liver cancer cell growth in vivo and in vitro in a p53-dependent manner. Moreover, ZNF498 inhibited p53-mediated apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation. Conclusions Our results strongly suggest that ZNF498 suppresses apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation in hepatocellular carcinogenesis, revealing a novel ZNF498-PKCδ-p53INP1-p53 axis in HCC cells that would enrich the non-mutation p53-inactivating mechanisms in HCC.

Hepatocellular carcinoma (HCC) accounts for over 80% of cases among liver cancer, with high incidence and poor prognosis. Thus, it is of valuable clinical significance for discovery of potential biomarkers and drug targets for HCC. In this study, based on the proteomic profiling data of paired early-stage HCC samples, we found that RNF149 was strikingly upregulated in tumor tissues and correlated with poor prognosis in HCC patients, which was further validated by IHC staining experiments of an independent HCC cohort. Consistently, overexpression of RNF149 significantly promoted cell proliferation, migration, and invasion of HCC cells. We further proved that RNF149 stimulated HCC progression via its E3 ubiquitin ligase activity, and identified DNAJC25 as its new substrate. In addition, bioinformatics analysis showed that high expression of RNF149 was correlated with immunosuppressive tumor microenvironment (TME), indicating its potential role in immune regulation of HCC. These results suggest that RNF149 could exert protumor functions in HCC in dependence of its E3 ubiquitin ligase activity, and might be a potential prognostic marker and therapeutic target for HCC treatment.

Abstract Background Litchi is a well-known subtropical fruit crop. However, irregular bearing attributed to unstable flowering is a major ongoing problem for the development of the litchi industry. In a previous study, our laboratory proved that litchi flowering was induced by low temperature and that a FLOWERING LOCUS T (FT) homologue gene named LcFT1 played a pivotal role in this process. The present study aimed to understand the natural variation in FT among litchi germplasm resources and designed markers to verify easy- and difficult-flowering litchi germplasms. A grafting experiment was also carried out to explore whether it could shorten the seedling stage of litchi seedlings. Results Two types of LcFT1 promoter existed in different litchi germplasm resources, and we named them the ‘easy-flowering type of LcFT1 promoter’ and ‘difficult-flowering type of LcFT1 promoter’, which resulted in three different LcFT1 genotypes of litchi germplasm resources, including the homozygous easy-flowering type of the LcFT1 genotype, homozygous difficult-flowering type of the LcFT1 genotype and heterozygous LcFT1 genotype of litchi germplasm resources. The homozygous easy-flowering type of the LcFT1 genotype and heterozygous LcFT1 genotype of the litchi germplasm resources completed their floral induction more easily than the homozygous difficult-flowering type of the LcFT1 genotype of litchi germplasm resources. Herein, we designed two kinds of efficient molecular markers based on the difference in LcFT1 promoter sequences and applied them to identify of the easy- and difficult-flowering litchi germplasm resources. These two kinds of molecular markers were capable of clearly distinguishing the easy- from difficult-flowering litchi germplasm resources at the seedling stage and provided the same results. Meanwhile, grafting the scion of seedlings to the annual branches of adult litchi trees could significantly shorten the seedling stage. Conclusions Understanding the flowering characteristics of litchi germplasm resources is essential for easy-flowering litchi breeding. In the present study, molecular markers provide a rapid and accurate approach for identifying the flowering characteristics. The application of these molecular markers not only significantly shortened the artificial crossbreeding cycle of easy-flowering litchi cultivars but also greatly saved manpower, material resources and land.

Abstract Background With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. Results Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. Conclusions PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

Summary: Mass spectrometry-based proteomic technology has greatly improved and has been widely applied in various biological science fields. However, proteome-wide accurate quantification of proteins in signaling pathways remains challenging. Here, we report a genome-wide amino acid coding-decoding quantitative proteomic (GwAAP) system to facilitate precise proteome quantification. For each protein, a unique code peptide was assigned and incorporated into the N-terminus of the targeted protein and used for identification and quantification. As a proof of principle, we systematically tagged 40 yeast proteins with codes and employed mass spectrometry to decode. We successfully recovered all 40 code peptides with a large and consistent quantitative dynamic range (CV slope 0.8). We further verified the alteration of the glucose and galactose metabolism pathways in yeast under different carbon source conditions. The GwAAP system could potentially provide a strategy to achieve absolute quantification of the entire yeast proteome without bias.

Abstract A comprehensive analysis of the humoral immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential in understanding COVID-19 pathogenesis and developing antibody-based diagnostics and therapy. In this work, we performed a longitudinal analysis of antibody responses to SARS-CoV-2 proteins in 104 serum samples from 49 critical COVID-19 patients using a peptide-based SARS-CoV-2 proteome microarray. Our data show that the binding epitopes of IgM and IgG antibodies differ across SARS-CoV-2 proteins and even within the same protein. Moreover, most IgM and IgG epitopes are located within nonstructural proteins (nsps), which are critical in inactivating the host’s innate immune response and enabling SARS-CoV-2 replication, transcription, and polyprotein processing. IgM antibodies are associated with a good prognosis and target nsp3 and nsp5 proteases, whereas IgG antibodies are associated with high mortality and target structural proteins (Nucleocapsid, Spike, ORF3a). The epitopes targeted by antibodies in patients with a high mortality rate were further validated using an independent serum cohort (n = 56) and using global correlation mapping analysis with the clinical variables that are associated with COVID-19 severity. Our data provide fundamental insight into humoral immunity during SARS-CoV-2 infection. SARS-CoV-2 immunogenic epitopes identified in this work could also help direct antibody-based COVID-19 treatment and triage patients.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes a broad clinical spectrum of coronavirus disease 2019 (COVID-19). The development of COVID-19 may be the result of a complex interaction between the microbial, environmental, and host genetic components. To reveal genetic determinants of susceptibility to COVID-19 severity in the Chinese population, we performed a genome-wide association study on 885 severe or critical COVID-19 patients (cases) and 546 mild or moderate patients (controls) from two hospitals, Huoshenshan and Union hospitals at Wuhan city in China. We identified two loci on chromosome 11q23.3 and 11q14.2, which are significantly associated with the COVID-19 severity in the meta-analyses of the two cohorts (index rs1712779: odds ratio [OR] = 0.49; 95% confidence interval [CI], 0.38–0.63 for T allele; P = 1.38 × 10−8; and index rs10831496: OR = 1.66; 95% CI, 1.38–1.98 for A allele; P = 4.04 × 10−8, respectively). The results for rs1712779 were validated in other two small COVID-19 cohorts in the Asian populations (P = 0.029 and 0.031, respectively). Furthermore, we identified significant eQTL associations for REXO2, C11orf71, NNMT, and CADM1 at 11q23.3, and CTSC at 11q14.2, respectively. In conclusion, our findings highlight two loci at 11q23.3 and 11q14.2 conferring susceptibility to the severity of COVID-19, which might provide novel insights into the pathogenesis and clinical treatment of this disease.

Intercropping is widely used in agricultural production due to its capability of raising land productivity and providing an opportunity to achieve sustainable intensification of agriculture. In this study, soil samples from 10 to 20 cm depth of intercropping Pinto peanut in litchi orchard and litchi monoculture mode were established to determine soil attributes, enzyme activities, as well as the effect on soil bacterial diversity. On this basis, 16S rRNA V4-V5 region of soil bacterial communities in litchi/Pinto peanut intercropping (LP) mode and litchi monoculture mode (CK) was detected by the Illumina MiSeq sequencing platform. The results showed that the content of available potassium (AK) in LP was significantly higher than that in CK by 138.9%, and the content of available nitrogen (AN) in LP was significantly lower than that in CK by 19.6%. The soil enzyme activities were higher in LP as a whole, especially sucrase (SC) and acid protease (PT) were significantly higher by 154.4 and 76.5%, respectively. The absolute abundance and alpha diversity of soil microbiota were significantly higher in the intercropping group. Most importantly, endemic species with a significant difference in LP was higher by ~60 times compared to CK treatment. In the aspect of soil bacterial community structure, the dominant phyla of the two groups were Acidobacteria, Proteobacteria, Chloroflexi, and Actinobacteria. At the genus level, the absolute abundance of Flavobacterium and Nitrososphaera was significantly higher by 79.20 and 72.93%, respectively, while that of Candidatus_Koribacter was significantly lower with an amplitude of 62.24% in LP than in CK. Furthermore, the redundancy analysis (RDA) suggested that AK, which was highly associated with the dominant genera and phyla, is the vitally dominating environmental factors in LP groups, while in CK groups, it is AN and pH. In addition, PICRUSt2 analysis indicated that intercropping improved the metabolic activity of bacteria which can be correlated to the resistance of litchi root systems to soil-borne diseases. Overall, this study is expected to provide a theoretical basis and technical support for the healthy intercropping cultivation of litchi.

Abstract Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it’s greater for domains among different proteins than those within the same proteins. Some clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs.

The metabolite-caused taste variation during rambutan maturation is unknown due to a lack of systematic investigation of all components. In this study, three growing stages, including unripe (S1), half-ripe (S2), and full-ripe (S3) BY2 and BY7 rambutans were compared and profiled by UPLC–MS/MS-based widely targeted metabolomics analysis. We demonstrated that the sugar-acid ratios of two rambutans were greatly improved between the S2 and S3 stages. A total of 821 metabolites were identified, including 232, 205, 204, and 12 differential metabolites (DMs) in BY2-S1 vs. BY2-S2, BY2-S2 vs. BY2-S3, BY7-S1 vs. BY7-S2, and BY7-S2 vs. BY7-S3, respectively. A correlation analysis showed that gamma-aminobutyric acid (GABA) could be the sugar-acid ratio biomarker of BY2 rambutan. Methionine (Met), alanine (Ala), and S-methyl-L-cysteine (SMC) could be total amino acid biomarkers of BY2 and BY7 rambutans. In addition, UPLC-MS/MS-based quantitative verification of the above biomarkers exhibited the same variations as metabolomics analysis. This study not only provides useful nutritive information on rambutans but also valuable metabolic data for rambutan breeding strategies.

Abstract Background One striking feature of the large KRAB domain-containing zinc finger protein (KZFP) family is its rapid evolution, leading to hundreds of member genes with various origination time in a certain mammalian genome. However, a comprehensive genome-wide and across-taxa analysis of the structural and expressional features of KZFPs with different origination time is lacking. This type of analysis will provide valuable clues about the functional characteristics of this special family. Results In this study, we found several conserved paradoxical phenomena about this issue. 1) Ordinary young domains/proteins tend to be disordered, but most of KRAB domains are completely structured in 64 representative species across the superclass of Sarcopterygii and most of KZFPs are also highly structured, indicating their rigid and unique structural and functional characteristics; as exceptions, old-zinc-finger-containing KZFPs have relatively disordered KRAB domains and linker regions, contributing to diverse interacting partners and functions. 2) In general, young or highly structured proteins tend to be spatiotemporal specific and have low abundance. However, by integrated analysis of 29 RNA-seq datasets, including 725 samples across early embryonic development, embryonic stem cell differentiation, embryonic and adult organs, tissues in 7 mammals, we found that KZFPs tend to express ubiquitously with medium abundance regardless of evolutionary age and structural disorder degree, indicating the wide functional requirements of KZFPs in various states. 3) Clustering and correlation analysis reveal that there are differential expression patterns across different spatiotemporal states, suggesting the specific-high-expression KZFPs may play important roles in the corresponding states. In particular, part of young-zinc-finger-containing KZFPs are highly expressed in early embryonic development and ESCs differentiation into endoderm or mesoderm. Co-expression analysis revealed that young-zinc-finger-containing KZFPs are significantly enriched in five co-expression modules. Among them, one module, including 13 young-zinc-finger-containing KZFPs, showed an ‘early-high and late-low’ expression pattern. Further functional analysis revealed that they may function in early embryonic development and ESC differentiation via participating in cell cycle related processes. Conclusions This study shows the conserved and special structural, expressional features of KZFPs, providing new clues about their functional characteristics and potential causes of their rapid evolution.

Macrophages are highly heterogeneous immune cells that fulfill tissue-specific functions. Tissue-derived signals play a critical role in determining macrophage heterogeneity. However, these signals remain largely unknown. The BMP receptor activin receptor–like kinase 1 (ALK1) is well known for its role in blood vessel formation; however, its role within the immune system has never been revealed to our knowledge. Here, we found that BMP9/BMP10/ALK1 signaling controlled the identity and self-renewal of Kupffer cells (KCs) through a Smad4-dependent pathway. In contrast, ALK1 was dispensable for the maintenance of macrophages located in the lung, kidney, spleen, and brain. Following ALK1 deletion, KCs were lost over time and were replaced by monocyte-derived macrophages. These hepatic macrophages showed significantly reduced expression of the complement receptor VSIG4 and alterations in immune zonation and morphology, which is important for the tissue-specialized function of KCs. Furthermore, we found that this signaling pathway was important for KC-mediated Listeria monocytogenes capture, as the loss of ALK1 and Smad4 led to a failure of bacterial capture and overwhelming disseminated infections. Thus, ALK1 signaling instructs a tissue-specific phenotype that allows KCs to protect the host from systemic bacterial dissemination.

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

The Human Proteome Project (HPP) was launched in 2010 to enhance accurate annotation of the genome-encoded proteome. Ten years later, the HPP releases its first blueprint of the human proteome, annotating 90% of all known proteins at high-stringency and discussing the implications of proteomics for precision medicine.

Abstract Background Universal gene targets are in persistent demand by real‐time quantitative polymerase chain reaction (RT‐qPCR)‐based methods in acute leukemia (AL) diagnosis and monitoring. Human Krüppel‐like factor 3 (hKLF3), a newly cloned human transcription factor, has proved to be a regulator of hematopoiesis. Methods Sanger sequencing was performed in bone marrow (BM) samples from 17 AL patients for mutations in hKLF3 coding exons. hKLF3 expression in peripheral blood (PB) and BM samples from 45 AL patients was dynamically detected by RT‐qPCR. PB samples from 31 healthy donors were tested as normal controls. Results No mutation was sequenced in hKLF3 coding exons. hKLF3 expression in PB of AL was significantly lower than that in healthy donors [0.30 (0.02‐1.07) vs 1.18 (0.62‐3.37), P

Summary: The diffuse-type gastric cancer (DGC) constitutes a subgroup of gastric cancer with poor prognosis and no effective molecular therapies. Here, we report a phosphoproteomic landscape of DGC derived from 83 tumors together with their nearby tissues. Based on phosphorylation, DGC could be classified into three molecular subtypes with distinct overall survival (OS) and chemosensitivity. We identified 16 kinases whose activities were associated with poor OS. These activated kinases covered several cancer hallmark pathways, with the MTOR signaling network being the most frequently activated. We proposed a patient-specific strategy based on the hierarchy of clinically actionable kinases for prioritization of kinases for further clinical evaluation. Our global data analysis indicates that in addition to finding activated kinase pathways in DGC, large-scale phosphoproteomics could be used to classify DGCs into subtypes that are associated with distinct clinical outcomes as well as nomination of kinase targets that may be inhibited for cancer treatments. : Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology Subject Areas: Biological Sciences, Cancer Systems Biology, Proteomics, Systems Biology

Neutrophils and macrophages are both involved in the initiation of inflammation, but whether and how they may participate in inflammation resolution is unclear. Here the authors show that neutrophils may mediate the conversion of macrophage into a pro-resolution phenotype via reactive oxygen species production to promote liver repair.

Deubiquitinases have been shown to be involved in double strand break repair pathways. Here the authors reveal that USP15 deybiquitinase plays a role in homologues recombination repair by targeting BARD1 and affecting cells response to PARP inhibitors.

Background: Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates. Findings: In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a “patient-specific” hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity. Interpretation: Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer. Keywords: Ovarian cancer, Phosphoproteomics, Druggable kinase

In atherosclerotic plaques, transformation of macrophages into foam cells is a key step in initiating the inflammatory response. Here Fan et al. show that casein kinase 2-interacting protein-1 (CKIP-1) limits foam cell formation and atherosclerosis by preventing expression of the scavenger receptor LOX-1 through REGγ-mediated degradation of Oct-1.

Sox2 regulates pluripotency in neural progenitor cells (NPC) but how protein stability affects this is unclear. Here, the authors identify changes in ubiquitylation of Sox2 (by CUL4A-DET1-COP1 ligase and OTUD7B deubiquitylase) as controlling protein stability and so the differentiation state of NPCs.

Abstract Background How genome complexity affects organismal phenotypic complexity is a fundamental question in evolutionary developmental biology. Previous studies proposed various contributing factors of genome complexity and tried to find the connection between genomic complexity and organism complexity. However, a general model to answer this question is lacking. Here, we introduce a ‘two-level’ model for the realization of genome complexity at phenotypic level. Results Five representative species across Protostomia and Deuterostomia were involved in this study. The intrinsic gene properties contributing to genome complexity were classified into two generalized groups: the complexity and age degree of both protein-coding and noncoding genes. We found that young genes tend to be simpler; however, the mid-age genes, rather than the oldest genes, show the highest proportion of high complexity. Complex genes tend to be utilized preferentially in each stage of embryonic development, with maximum representation during the late stage of organogenesis. This trend is mainly attributed to mid-age complex genes. In contrast, young genes tend to be expressed in specific spatiotemporal states. An obvious correlation between the time point of the change in over- and under-representation and the order of gene age was observed, which supports the funnel-like model of the conservation pattern of development. In addition, we found some probable causes for the seemingly contradictory ‘funnel-like’ or ‘hourglass’ model. Conclusions These results indicate that complex and young genes contribute to organismal complexity at two different levels: Complex genes contribute to the complexity of individual proteomes in certain states, whereas young genes contribute to the diversity of proteomes in different spatiotemporal states. This conclusion is valid across the five species investigated, indicating it is a conserved model across Protostomia and Deuterostomia. The results in this study also support ‘funnel-like model’ from a new viewpoint and explain why there are different evo–devo relation models.

Biomaterial scaffolds are increasingly being used to drive tissue regeneration. The limited success so far in human tissues rebuilding and therapy application may be due to inadequacy of the functionality biomaterial scaffold. We developed a new decellularized method to obtain complete anatomical skin biomatrix scaffold in situ with extracellular matrix (ECM) architecture preserved, in this study. We described a skin scaffold map by integrated proteomics and systematically analyzed the interaction between ECM proteins and epidermal cells in skin microenvironment on this basis. They were used to quantify structure and function of the skin’s Matrisome, comprised of core ECM components and ECM-associated soluble signals that are key regulators of epidermal development. We especially revealed that ECM played a role in determining the fate of epidermal stem cells through hemidesmosome components. These concepts not only bring us a new understanding of the role of the skin ECM niche, they also provide an attractive combinational strategy based on tissue engineering principles with skin biomatrix scaffold materials for the acceleration and enhancement of tissue regeneration.

Summary: Ubiquitin chain specificity has been described for some deubiquitinases (DUBs) but lacks a comprehensive profiling in vivo. We used quantitative proteomics to compare the seven lysine-linked ubiquitin chains between wild-type yeast and its 20 DUB-deletion strains, which may reflect the linkage specificity of DUBs in vivo. Utilizing the specificity and ubiquitination heterogeneity, we developed a method termed DUB-mediated identification of linkage-specific ubiquitinated substrates (DILUS) to screen the ubiquitinated lysine residues on substrates modified with certain chains and regulated by specific DUB. Then we were able to identify 166 Ubp2-regulating substrates with 244 sites potentially modified with K63-linked chains. Among these substrates, we further demonstrated that cyclophilin A (Cpr1) modified with K63-linked chain on K151 site was regulated by Ubp2 and mediated the nuclear translocation of zinc finger protein Zpr1. The K48-linked chains at non-K151 sites of Cpr1 were mainly regulated by Ubp3 and served as canonical signals for proteasome-mediated degradation. : Molecular Biology; Omics Subject Areas: Molecular Biology, Omics

HRD1 is an E3 ligase known to play a role in targeting degradation of misfolded proteins in the ER. Here the authors show that HRD1 interacts with metabolic enzymes and its liver specific deficiency results in lower body weight, blood glucose and plasma lipids during high fat diet in mice.

Plant development: The complex control of seed size A family of genes, known as cell wall invertases (CWINs), control different stages of seed development in litchi, and could be used to breed more desirable fruits. Seed set and seed size are important characteristics of horticultural plants, with the seeds of some crops being significant food sources, while seedlessness or small seededness is sought after in many fruit crops. A team led by Jietang Zhao and Huicong Wang, of South China Agricultural University, studied the genes controlling seed set and size in small-seeded, large-seeded and seed-abortive cultivars of litchi (Litchi chinensis). They identified five members of the CWIN family, of which one, LcCWIN5, was shown to regulate early seed development, while another, LcCWIN2, was active during the later “seed-filling” stage. These results could aid the development of more attractive, small-seeded or seedless varieties of litchi.

Changes in glucose metabolism can lead to tumor development, but the involvement of splicing factors is unclear. Here, the authors screened for SR proteins and identified SRSF5 stability is enhanced in response to glucose elevation to promote alternative splicing of CCAR1 which facilitates tumor growth.

As a circadian organ, liver functions are regulated by circadian clock. Here, the authors present a comprehensive proteomics landscape of the mouse liver, including transcription factor binding profiles, phosphorylation and ubiquitylation patterns, nuclear and whole proteome, and the transcriptome.

Diffuse-type gastric cancer (DGC) accounts for 30% of gastric cancers and has few treatment options. Here the authors present a mutation and proteome dataset for 84 patients, identifying three major classes of DGC and indicating potential targets for therapy.

Protein stability modulation by E3 ubiquitin ligases is an important layer of functional regulation, but screening for E3 ligase-substrate interactions is time-consuming and costly. Here, the authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.

Abstract Plant grafting has been practiced widely in horticulture and proved as a useful tool in science. However, the mechanisms of graft healing or graft incompatibility remain poorly understood. In this study, Litchi chinensis cv. ‘Jingganghongnuo’ homograft (‘J/J’) and ‘Jingganghongnuo’/‘zhuangyuanhong’ heterograft (‘J/Z’) as compatible and incompatible combination, respectively, was used to study transcriptional changes between incompatible and compatible graft during graft union formation. Anatomical observation indicated that three stages (2 h, 14 d and 21 d after grafting) were critical for graft union formation and selected for high-throughput sequencing. Results indicated 6060 DEGs were differentially expressed in the compatible combination and 5267 DEGs exhibiting in the incompatible one. KEGG pathway enrichment analysis revealed that DEGs were involved in metabolism, wound response, phenylpropanoid biosynthesis and plant hormone signal transduction. The expression of 9 DEGs annotated in auxin pathway was up-regulated in compatible combination than that in incompatible combination. The IAA concentration confirmed that the IAA might promote the graft compatibility. In addition, 13 DEGs related to lignin biosynthesis were differentially expressed during graft healing process. Overall, our results provide abundant sequence resources for studying mechanisms underlying graft compatibility and establish a platform for further studies of litchi and other evergreen fruit trees.