Molecular omics最新文献

{"title":"Metabolite-centric identification of antimetabolite drug targets across cancer and neurodegenerative diseases.","authors":"Regan Odongo, Tunahan Çakır","doi":"10.1093/molecular-omics/aaiag018","DOIUrl":"https://doi.org/10.1093/molecular-omics/aaiag018","url":null,"abstract":"<p><p>Antimetabolites, primarily studied in cancer, are novel drugs targeting metabolic networks by mimicking and inhibiting disease-causing metabolites, enabling poly-pharmacologic effects essential for complex diseases. Moreover, predicting patients likely to positively respond to antimetabolite drugs is necessary to simplify clinical applications. However, existing computational approaches for antimetabolite target discovery lack incorporation of disease-induced metabolic state perturbations, and their applicability beyond cancer remains unexplored. We introduce MATADOR (Metabolite-centric Analysis of TARgets for Drug ORientation), a computational workflow that integrates patient-derived omic data with metabolic networks to identify, evaluate and prioritize antimetabolite targets based on metabolic state transformation. Applying MATADOR to RNA-seq data from breast, colon, lung, and liver cancers, we achieved a 66±6% sensitivity in recapturing known antimetabolite targets, strongly supported thioredoxin as a pan-cancer target, and linked top-ranked targets to poor 5-year survival in breast and liver cancers. Extending beyond cancer, MATADOR nominated metabolites with proinflammatory effects as potential antimetabolite targets in Alzheimer's and Parkinson's diseases, aligning well with their known pathological mechanisms. Finally, applying MATADOR on personalized metabolic networks, machine learning models trained on metabolic gene expression demonstrated the ability to leverage gene expression to personalize antimetabolite targets. The proposed approach may expedite prioritization and personalization of antimetabolite targets during pre-clinical studies across diseases with systemic metabolic alterations.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Placental metabolomics for assessment of pregnancy complications: a systematic review.","authors":"Grace V Mercer, Lindsay S Cahill","doi":"10.1093/molecular-omics/aaiag015","DOIUrl":"10.1093/molecular-omics/aaiag015","url":null,"abstract":"<p><p>Metabolomics is a powerful tool for understanding biological function at the cellular level. While this omics technology has found application in areas ranging from toxicology to cancer, its potential for the study of human pregnancy complications is in its infancy. Proper placental function is critical for a healthy pregnancy and assessing placental metabolism offers a direct window into placental physiology. This review aims to analyse placental metabolomics in pregnancy complications in humans. A systematic search was completed using PubMed and Google Scholar, and the Covidence software was used for the screening process. Following title and abstract screening and full-text review, 32 articles remained and were included in this review. Multiple articles performed metabolomics using mass spectrometry and/or nuclear magnetic resonance to study pre-eclampsia, gestational hypertension, foetal growth restriction, maternal obesity, gestational diabetes mellitus, intrahepatic cholestasis of pregnancy, and preterm birth. A subset of pregnancy complications was investigated in only a single study each (human immunodeficiency virus in pregnancy, neural tube defects, chromosomal disorders, chronic hypoxia, and recurrent pregnancy loss). Univariate analyses identified potential biomarkers of pregnancy complications. Multivariate analyses demonstrated that the placental global metabolic profiles could separate healthy and complicated pregnancies and identified metabolic pathways (e.g. amino acid metabolism, lipid and phospholipid metabolism) to explain pathogenesis and the mechanisms of disease. We highlight knowledge gaps and future directions, emphasizing the need for data replication and inclusion of variables such as biological sex in the field of placental metabolomics.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13140791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147675129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platelet proteomics on less than a drop of previously frozen, non-citrate plasma.","authors":"Dylan Nicholas Tabang, Kathrin Korff, Kaitlyn Brannon, Julia Tilburg, Komal Mandal, Arthur Viodé, Patrick Zalm, Arjun Ravishankar, Jin-Gyu Cheong, Oliver Schott, Ramy Arnaout, Steven Z Josefowicz, Joseph E Italiano, Hanno Steen, Kinga K Smolen","doi":"10.1093/molecular-omics/aaiag017","DOIUrl":"https://doi.org/10.1093/molecular-omics/aaiag017","url":null,"abstract":"<p><p>Platelets are blood components not regularly analysed with proteomics due to the conventional wisdom that plasma for platelet research must be citrate-treated and freshly sampled to minimize artifactual changes. Information from platelets is complementary to plasma, specifically regarding immunophenotyping of patients with challenged immune systems due to infection or inflammation. We sought to develop a sample-sparing, high-throughput-compatible platelet proteomics workflow applicable to previously frozen, non-citrate platelet-rich plasma, in contrast to the field's standard, and test its efficacy by applying it to a COVID-19 cohort. We examined centrifugation of whole blood and platelet-rich plasma and volume requirements of plasma for platelet analysis. Platelet and platelet-poor plasma samples were analysed from a cohort of 79 patients, consisting of COVID-19 negative non-ICU and ICU controls and patients with COVID-19 over time. Conventional platelet count was successfully performed using flow cytometry on previously frozen plasma, showing minimal platelet aggregation and cell debris, demonstrating viability of previously frozen plasma for platelet proteomic analysis. Protein counts in platelets mostly mirrored trends in platelet count, except in severe COVID-19 patients within three days of admission to the ICU. Proteins dysregulated in this group compared to controls were enriched in terms related to platelet activation, phagosome, and efferocytosis. This agrees with prior reports using conventional platelet proteomics methods. Such similar findings suggest that the method developed here can utilize non-citrate, previously frozen plasma down to 0.5 μL per sample. This will enable platelet proteomics studies on already collected, banked plasma samples more accessible and increase biomolecular information gained.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the Diabetic Transition: A Lipidomic Approach for Biomarker Discovery in an Indian Cohort.","authors":"Sneha Singh, Rekha Nagpal, Ashutosh Kumar","doi":"10.1093/molecular-omics/aaiag016","DOIUrl":"https://doi.org/10.1093/molecular-omics/aaiag016","url":null,"abstract":"<p><p>Type 2 Diabetes Mellitus (T2DM) is a significant public health burden in India, where the disease presents with unique clinical characteristics, yet data from this high-risk population remain scarce. This study aimed to conduct a lipidomic analysis to identify candidate lipid species associated with different glycemic stages using a refined untargeted lipidomics approach. A cross-sectional study of 95 individuals from Mumbai was stratified into healthy, prediabetic, newly diagnosed diabetic, and advanced diabetic groups based on glycated hemoglobin (HbA1c) levels. An untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was applied to pooled serum samples to construct a comprehensive lipid library and enable comparative profiling. A stringent filtering pipeline was implemented to ensure robust lipid identification. Our analysis identified a core lipidome of 69 species shared across all cohorts, alongside an increase in lipidomic diversity in diabetic states, with the advanced diabetic group exhibiting the highest number of unique species. Ceramides (Cer) were detected across the glycemic spectrum and exhibited a distinct remodelling pattern. Partial least squares discriminant analysis (PLS-DA) identified 20 candidate lipid species with variable importance in projection (VIP) scores > 1.0, prominently including Cer, phosphatidylinositol (PI), and hexosylceramides (HexCer). Additional lipid classes of interest included sphingomyelins (SM), triacylglycerols (TG), acylcarnitines (ACar), and fatty acids (FA). These hypothesis-generating findings provide a prioritized framework for future large-scale quantitative validation studies aimed at early prediction and risk stratification of T2DM in the Indian population.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147691072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of putative kidney-derived proteins in plasma using nanoparticle enrichment.","authors":"Daniela A Key Planas, Sumaiya Nazli, Angelica M Riojas, Arisbeth Camarillo Reyes, Avinash Jadhav, Kip Zimmerman, Laura A Cox, Michael Olivier","doi":"10.1093/molecular-omics/aaiag008","DOIUrl":"10.1093/molecular-omics/aaiag008","url":null,"abstract":"<p><p>Many diseases, including diabetes and hypertension, can lead to kidney damage, often resulting in long-term health complications and organ failure. Both animal and human studies have documented a causal relationship between high dietary sodium intake and elevated blood pressure. We explored a proteomics analysis of plasma samples to detect early kidney damage in response to a high-sodium (HS) diet through the quantification of putative kidney-derived proteins in plasma for early disease detection. Plasma samples from seven female baboons fed an HS diet for 6 weeks were collected before and after the HS diet challenge. Plasma samples were analysed using a novel nanoparticle enrichment protocol. Using Seer's Proteograph XT workflow, we identified 2294 plasma proteins across all the samples. Of these, 2139 proteins were annotated with gene IDs, including 453 proteins previously identified in kidney lysate. In the Human Protein Atlas, 1969 of these proteins were annotated as expressed in the kidney, and 37 were classified as elevated. Ninety-seven proteins were only detected in plasma samples collected after the 6-week HS diet. We also identified 35 nominally significant differentially abundant proteins (P < .05), with 84% of these detected at higher abundance after HS exposure. Additional proteins were identified in two animals that demonstrated an increase in blood pressure in response to the HS diet. Our analysis reports an increase in putative kidney-derived proteins in plasma after HS diet exposure, and demonstrates the suitability of the Seer's Proteograph technology to identify potential biomarkers for kidney tissue damage.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13017961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prostate cancer: metabolic remodelling in expressed prostatic secretions reveals cellular structural changes measured by mpMRI.","authors":"Anuj Kumar, Ashish Ranjan, Aniket Anand, Avinash D Gautam, Hira Lal, Kavindra Nath, Uday P Singh, Ashish Gupta","doi":"10.1093/molecular-omics/aaiag013","DOIUrl":"10.1093/molecular-omics/aaiag013","url":null,"abstract":"<p><p>Early and accurate differentiation of prostate cancer (PC) from benign prostatic hyperplasia (BPH) remains challenging; metabolomics enables biomarker discovery by capturing disease-specific metabolic changes. The study includes 64 expressed prostatic secretion from 31 PC cases and 33 BPH cases. Nuclear magnetic resonance spectroscopy was used for metabolomics. Multivariate analyses, including principal component analysis, orthogonal partial least squares discriminant analysis, and artificial neural network modelling, were performed to identify discriminative metabolites. Diagnostic performance was assessed using receiver operating characteristic curve analysis. Clinical correlations with prostate-specific antigen (PSA) levels, multiparametric MRI (mpMRI), and Gleason score (GS) were executed. Citrate, glutamate, myo-inositol, and cis-aconitate were identified as key metabolites distinguishing PC from BPH, showing significant correlations with PSA, mpMRI-derived Prostate Imaging Reporting and Data System scores and apparent diffusion coefficient values as well as histopathology-based GS. The identified metabolic signature demonstrates strong potential as a noninvasive tool to support early PC detection and clinical decision-making, showing correlation with PSA, mpMRI indices and GS to enhance diagnostic accuracy before structural changes become evident.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147593416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urinary metabolomics identifies lactate as a biomarker for bladder cancer detection and progression.","authors":"Ângela Carapito, Tiago Vieira de Sousa, Ana Teixeira-Marques, Rui Henrique, Carmen Jerónimo, Ana Cecília A Roque, Félix Carvalho, Joana Pinto, Paula Guedes de Pinho","doi":"10.1093/molecular-omics/aaiag004","DOIUrl":"10.1093/molecular-omics/aaiag004","url":null,"abstract":"<p><p>Bladder cancer (BC) is the ninth most prevalent malignancy worldwide. It remains a significant clinical burden due to high recurrence rates and the need for reliable, non-invasive diagnostic tools. Metabolomics is a powerful strategy for non-invasive cancer detection, with urine representing an ideal biofluid for biomarker discovery, given its direct contact with the urinary tract and its rich diversity of metabolites. This study aimed to identify urinary metabolites showing significant differences in urinary levels between BC patients and controls, and to evaluate their potential for diagnosis and disease monitoring. Beyond identifying metabolites differentiating BC patients from controls, we also assessed whether urinary metabolic patterns could distinguish BC subtypes [non-muscle invasive BC (NMIBC) versus muscle-invasive BC (MIBC)]. Following chemical derivatization, urinary samples were analysed by gas chromatography-mass spectrometry, and the resulting datasets were evaluated using univariate and multivariate statistical approaches. Among the 32 metabolites identified (e.g. amino acids, organic acids, alcohols, and sugar-derivatives), lactate was identified as significantly upregulated in BC versus controls, particularly in MIBC cases. Receiver operating characteristic analysis demonstrated a good performance for overall BC detection and in discriminating between MIBC and NMIBC cases. These results, independent of smoking status and sex, position lactate as a promising non-invasive biomarker for invasive BC.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Native PGC-LC-MS profiling reveals distinct O-acetylation patterns of sialylated N-glycans across mammalian sera.","authors":"Christopher Ashwood","doi":"10.1093/molecular-omics/aaiag010","DOIUrl":"10.1093/molecular-omics/aaiag010","url":null,"abstract":"<p><p>O-acetylation of sialic acids represents an additional layer of structural diversity and biological complexity, occurring at various hydroxyl positions (commonly C-7, C-8, or C-9) of the sialic acid residue. This modification modulates the recognition of sialylated glycans by lectins, antibodies, and viral proteins, and contributes to viral tropism and host susceptibility, particularly in influenza and coronaviruses that bind O-acetylated sialylated receptors. However, current LC-MS glycomics workflows commonly employ reduction or permethylation, which, while improving chromatographic stability and ionization, result in the loss of labile O-acetyl groups, obscuring their biological relevance. Native glycan analysis, in contrast, preserves the complete structural integrity of glycans, enabling accurate detection of labile modifications. Using a native released glycan workflow limited to pH ≤8, O-acetylated N-glycans were detected in mouse and rat sera that were previously undetectable under basic derivatization conditions. Beam-type collision-induced dissociation generated the most informative fragmentation spectra, with diagnostic ions confirming O-acetylated NeuGc and NeuAc residues. Chromatographic profiling revealed later elution and broadened peak shapes for O-acetylated species, consistent with increased hydrophobicity and microheterogeneity. A checkpoint-based identification workflow incorporating isotopic, chromatographic, and MS2 criteria reduced false positives, retaining only 3%-5% of putative O-acetylated glycans as confident identifications. Quantitative comparison across species revealed extensive O-acetylation in rat (53.4%) and moderate modification in mouse (8.8%), but none detectable in human serum. These findings establish a robust analytical framework for native detection and characterization of O-acetylated N-glycans, revealing species-specific regulation of this labile modification.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Need for speed: advances in the era of high throughput interaction proteomics.","authors":"Fabian Frommelt, Uliana Federico, Ruedi Aebersold, Andrea Fossati","doi":"10.1093/molecular-omics/aaiag012","DOIUrl":"10.1093/molecular-omics/aaiag012","url":null,"abstract":"<p><p>Protein-protein interactions are central to virtually all biological processes, forming intricate networks that operate in a highly regulated manner. These interactions are not permanent but rather continuously adapt to environmental changes, developmental cues, or disease-related stress. Understanding which protein interactions are present in a specific cellular state and how they adapt to specific stimuli is one of the long-standing goals of modern systems biology. Mass spectrometry (MS)-based proteomics has emerged as the primary tool for charting these networks. Over the past two decades, continuous advances in instrumentation, sample preparation, and data analysis have enabled researchers to explore the protein interaction landscape with increasing depth and accuracy. This has led to important discoveries in areas ranging from fundamental cell signalling to the identification of new therapeutic targets. We present the current state of MS-based protein interaction analysis, focusing on the three most widely utilized approaches: affinity purification, proximity labelling, and co-fractionation MS. For each, we discuss the fundamental approach, technical considerations, limitations, and highlight the potential integration with future technologies and datasets. Recent innovations such as short-gradient chromatography and faster data acquisition have further improved sensitivity and throughput. Together, these developments are bringing researchers closer to mapping the dynamic, context-dependent architecture of protein networks in unprecedented detail.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering urinary proteogenomic signatures associated with head and neck squamous cell carcinoma.","authors":"Oriana Barros, Joaquim Castro Silva, Eurico Monteiro, Susana Aveiro, Pedro Domingues, Pedro Valente Sousa, Carolina Castro, Catarina Rodrigues, António Barros, Francisco Amado, Saeid Ghavami, Vito G D'Agostino, Rita Ferreira, Rui Vitorino, Lúcio Lara Santos","doi":"10.1093/molecular-omics/aaiag002","DOIUrl":"10.1093/molecular-omics/aaiag002","url":null,"abstract":"<p><p>Head and neck squamous cell carcinoma (HNSCC) is a major clinical challenge due to its aggressive nature and poor prognosis in advanced stages. Late detection, often due to a delayed diagnosis, limits treatment success. With the aim of improving the early diagnosis of HNSCC, we analysed urine samples from 19 male HNSCC patients and 10 healthy male subjects and identified 1427 proteins by mass spectrometry (MS)-based proteomics. Of these, 351 proteins were consistently detected in all subjects and selected for quantitative comparisons, which highlighted potential prognostic markers such as RNASE1, LRG1, and CD44. Proteogenomic cross-referencing of MS-identified peptides with cancer variant databases suggested the presence of HNSCC-associated protein variants [e.g. GAA p.(Trp746Cys) and SIAE p.(Pro210Leu)] as potential indicators of advanced disease. Functional analyses linked the identified proteins to important tumour-related processes, including the epithelial-mesenchymal transition and neutrophil degranulation. These results support urine as a valuable body fluid for proteogenomic profiling, as it can be collected non-invasively, is available in large volumes, and enables the longitudinal monitoring of molecular changes over time, providing a convenient window into systemic and tumour-associated processes. This study provides a proof of concept that tumour-related protein variants originating from HNSCC can be detected in urine, supporting its potential as a source of biomarkers for early detection. However, given the small, male-only cohort, these findings should be regarded as preliminary and will require validation in larger, sex-balanced cohorts, including patients with benign or inflammatory head and neck conditions, to confirm disease specificity. Altogether, our data underscore the translational promise of urinary proteogenomics in HNSCC management.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}