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Mucin-type-O-glycosylation on proteins is integrally involved in human health and disease and is coordinated by an enzyme family of 20 N -acetylgalactosaminyltransferases (GalNAc-Ts). Detailed knowledge on the biological effects of site-specific O-glycosylation is limited due to lack of information on specific glycosylation enzyme activities and O-glycosylation site-occupancies. Here we present a systematic analysis of the isoform-specific targets of all GalNAc-Ts expressed within a tissue-forming human skin cell line, and demonstrate biologically significant effects of O-glycan initiation on epithelial formation. We find over 300 unique glycosylation sites across a diverse set of proteins specifically regulated by one of the GalNAc-T isoforms, consistent with their impact on the tissue phenotypes. Notably, we discover a high variability in the O-glycosylation site-occupancy of 70 glycosylated regions of secreted proteins. These findings revisit the relevance of individual O-glycosylation sites in the proteome, and provide an approach to establish which sites drive biological functions. Information about O-glycosylation site regulation and occupancy in the human proteome is limited. Here, the authors identify GalNAc transferase-specific glycan sites in human keratinocytes and describe their occupancy.

Enterotoxigenic Escherichia coli (ETEC) infections are a leading cause of diarrheal illness, responsible for an estimated 100,000 deaths annually. ETEC pathogenesis is driven by various virulence factors, including toxins, adhesins, and noncanonical factors such as the protease EatA. The first line of host defense against intestinal pathogenic bacterial infections is the protective intestinal mucus layer. Here, we demonstrate the mechanism by which EatA degrades the core mucus component MUC2, thereby facilitating access to the epithelial cell surface and promoting infection. We identify the specific cleavage site region localized at the C-terminal of MUC2. EatA’s protease activity depends on the interaction between two distinct, uniquely spaced domains in human MUC2, which defines species specificity. We confirm this using a novel transgenic mouse model exclusively expressing human MUC2, which allows us to study the role of the mucus layer in the infection by human intestinal pathogens. These findings highlight how ETEC is adapted to specifically degrade the mucus layer of its human host. In this work, authors show how the enterotoxigenic Escherichia coli (ETEC) protease EatA cleaves the human mucus protein MUC2 at a C-terminal site, allowing bacteria to cross the intestinal mucus, reach epithelial cells, and promote infection, as demonstrated using a human MUC2 transgenic mouse model.

Background Both the expression and activities of LINE-1 (L1) retrotransposons are known to occur in numerous cell-types and are implicated in pathobiological contexts such as aging-related inflammation, autoimmunity, and in cancers. L1s encode two proteins that are translated from bicistronic transcripts. The translation product of ORF1 (ORF1p) has been robustly detected by immunoassays and shotgun mass spectrometry (MS). Yet, more sensitive detection methods would enhance the use of ORF1p as a clinical biomarker. In contrast, until now, no direct evidence of endogenous L1 ORF2 translation to protein (ORF2p) has been shown. Instead, assays for ORF2p have been limited to ectopic L1 ORF over-expression contexts and to indirect detection of endogenous ORF2p enzymatic activity, such as by the sequencing of de novo genomic insertions. Immunoassays for endogenous ORF2p have been problematic, producing apparent false positives due to cross-reactivities, and shotgun MS has not yielded reliable evidence of ORF2p peptides in biological samples. Results Here we present targeted mass spectrometry assays, selected and parallel reaction monitoring (SRM and PRM, respectively) to detect and quantify L1 ORF1p and ORF2p at their endogenous abundances. We were able to quantify ORF1p and ORF2p present in our samples down to a range in the low attomoles. Confident in our ability to affinity enrich ORF2p, we describe an interactome associated with endogenous ORF2-containing macromolecular assemblies. Conclusions This is the first assay to demonstrate sensitive and robust quantitation of endogenous ORF2p. The ability to assay ORF2p directly and quantitatively will improve our understanding of the developmental and diseased cell states where L1 expression and its activity naturally occur. The ability to simultaneously assay endogenous L1 ORF1p and ORF2p is an important step forward for L1 analytical biochemistry. Endogenous ORF2p interactomes can now be presented with confidence that ORF2p is among the enriched proteins.

Background: Both the expression and activities of LINE-1 (L1) retrotransposons are known to occur in numerous cell-types and are implicated in pathobiological contexts such as aging-related inflammation, autoimmunity, and in cancers. L1s encode two proteins that are translated from bicistronic transcripts. The translation product of ORF1 (ORF1p) has been robustly detected by immunoassays and shotgun mass spectrometry (MS). Yet, more sensitive detection methods would enhance the use of ORF1p as a clinical biomarker. In contrast, until now, no direct evidence of endogenous L1 ORF2 translation to protein (ORF2p) has been shown. Instead, assays for ORF2p have been limited to ectopic L1 ORF over-expression contexts and to indirect detection of endogenous ORF2p enzymatic activity, such as by the sequencing of de novo genomic insertions. Immunoassays for endogenous ORF2p have been problematic, producing apparent false positives due to cross-reactivities, and shotgun MS has not yielded reliable evidence of ORF2p peptides in biological samples. Results: Here we present targeted mass spectrometry assays, selected and parallel reaction monitoring (SRM and PRM, respectively) to detect and quantify L1 ORF1p and ORF2p at their endogenous abundances. We were able to quantify ORF1p and ORF2p present in our samples down to a range in the low attomoles. Confident in our ability to affinity enrich ORF2p, we describe an interactome associated with endogenous ORF2-containing macromolecular assemblies. Conclusions: This is the first assay to demonstrate sensitive and robust quantitation of endogenous ORF2p. The ability to assay ORF2p directly and quantitatively will improve our understanding of the developmental and diseased cell states where L1 expression and its activity naturally occur. The ability to simultaneously assay endogenous L1 ORF1p and ORF2p is an important step forward for L1 analytical biochemistry. Endogenous ORF2p interactomes can now be presented with confidence that ORF2p is among the enriched proteins.Competing Interest StatementJL reports grants, personal fees, and equity from Rome Therapeutics, outside the submitted 550 work. MT reports personal fees, and equity from Rome Therapeutics, outside the submitted work. JL, MIN., and 551 JCW. have a patent application pending, based on this work. The other authors declare no competing interests.

The COVID-19 pandemic elicited a substantial hike in journal submissions and a global push to get medical evidence quickly through the review process. Editorial decisions and peer-assessments were made under intensified time constraints, which may have amplified social disparities in the outcomes of peer-reviewing, especially for COVID-19 related research. This study quantifies the differential impact of the pandemic on the duration of the peer-review process for women and men and for scientists at different strata of the institutional-prestige hierarchy. Using mixed-effects regression models with observations clustered at the journal level, we analysed newly available data on the submission and acceptance dates of 78,085 medical research articles published in 2019 and 2020. We found that institution-related disparities in the average time from manuscript submission to acceptance increased marginally in 2020, although half of the observed change was driven by speedy reviews of COVID-19 research. For COVID-19 papers, we found more substantial institution-related disparities in review times in favour of authors from highly-ranked institutions. Descriptive survival plots also indicated that scientists with prestigious affiliations benefitted more from fast-track peer reviewing than did colleagues from less reputed institutions. This difference was more pronounced for journals with a single-blind review procedure compared to journals with a double-blind review procedure. Gender-related changes in the duration of the peer-review process were small and inconsistent, although we observed a minor difference in the average review time of COVID-19 papers first authored by women and men.

교차성 분석은 단일 변수만 고려하는 것을 넘어, 성별과 인종, 또는 지리적 위치, 계급과의 교차점 등에서 발생하는 복합적 영향을 검토한다. 과학기술 분야의 교차성 분석 가이드라인(GIST)은 연구자, 학술지 편집자, 연구지원기관이 관련 과학기술 분야에서 교차성 분석을 체계적으로 통합하는 데 도움을 준다. 이 가이드라인은 연구의 전략적 우선순위 설정과 연구 문제 형성에서부터 자료 수집, 분석, 해석에 이르기까지 연구 과정 전반에 정량적 교차성 분석을 통합적으로 적용하기 위한 로드맵으로 사용될 수 있다. 여기에서는 저자 및 학술지 편집자가 가이드라인을 준수할 수 있도록 점검표를 제공한다. 우리는 GIST 점검표를 학술지의 저자 안내(Information for Authors) 에 포함할 것을 권장한다. 목표는 적절한 경우, 교차성 분석을 포함하는 것을 연구 기본으로 재설정하는 것이다. 교차성 분석은 더 나은 과학으로 이끈다. 정확한 연구는 효과적인 사회·환경 정책을 이끄는 가장 좋은 가이드이며, 이는 전 지구적 형평성과 지속가능성을 강화한다.

Citations are important building blocks for status and success in science. We used a linked dataset of more than 4 million authors and 26 million scientific papers to quantify trends in cumulative citation inequality and concentration at the author level. Our analysis, which spans 15 y and 118 scientific disciplines, suggests that a small stratum of elite scientists accrues increasing citation shares and that citation inequality is on the rise across the natural sciences, medical sciences, and agricultural sciences. The rise in citation concentration has coincided with a general inclination toward more collaboration. While increasing collaboration and full-count publication rates go hand in hand for the top 1% most cited, ordinary scientists are engaging in more and larger collaborations over time, but publishing slightly less. Moreover, fractionalized publication rates are generally on the decline, but the top 1% most cited have seen larger increases in coauthored papers and smaller relative decreases in fractional-count publication rates than scientists in the lower percentiles of the citation distribution. Taken together, these trends have enabled the top 1% to extend its share of fractional- and full-count publications and citations. Further analysis shows that top-cited scientists increasingly reside in high-ranking universities in western Europe and Australasia, while the United States has seen a slight decline in elite concentration. Our findings align with recent evidence suggesting intensified international competition and widening author-level disparities in science.

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/β-catenin–associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line. Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task‐driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type‐specific GEMs. An l ‐carnitine analog inhibits the proliferation of HepG2 cells. Graphical Abstract Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.

Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub ( https://github.com/mnielLab/NetTCR-2.2 ) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/ .