MS-based glycomics and glycoproteomics methods enabling isomeric characterization.

Glycosylation is one of the most significant and abundant posttranslational modifications in mammalian cells. It mediates a wide range of biofunctions, including cell adhesion, cell communication, immune cell trafficking, and protein stability. Also, aberrant glycosylation has been associated with various diseases such as diabetes, Alzheimer's disease, inflammation, immune deficiencies, congenital disorders, and cancers. The alterations in the distributions of glycan and glycopeptide isomers are involved in the development and progression of several human diseases. However, the microheterogeneity of glycosylation brings a great challenge to glycomic and glycoproteomic analysis, including the characterization of isomers. Over several decades, different methods and approaches have been developed to facilitate the characterization of glycan and glycopeptide isomers. Mass spectrometry (MS) has been a powerful tool utilized for glycomic and glycoproteomic isomeric analysis due to its high sensitivity and rich structural information using different fragmentation techniques. However, a comprehensive characterization of glycan and glycopeptide isomers remains a challenge when utilizing MS alone. Therefore, various separation methods, including liquid chromatography, capillary electrophoresis, and ion mobility, were developed to resolve glycan and glycopeptide isomers before MS. These separation techniques were coupled to MS for a better identification and quantitation of glycan and glycopeptide isomers. Additionally, bioinformatic tools are essential for the automated processing of glycan and glycopeptide isomeric data to facilitate isomeric studies in biological cohorts. Here in this review, we discuss commonly employed MS-based techniques, separation hyphenated MS methods, and software, facilitating the separation, identification, and quantitation of glycan and glycopeptide isomers.

A Reciprocal Best-hit Approach to Characterize Isomeric N-Glycans Using Tandem Mass Spectrometry.

Glycosylation is a post-translational modification involved in many important biological functions. The aberrant alteration of glycan structure is implicit with malfunction of cells and possess potential significance in medical diagnosis of complex diseases such as cancer. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been commonly applied to the analysis of complex glycomic samples. However, the characterization of isomeric glycans from their MS/MS spectra in complex biological samples remains challenging. In this paper, we present a novel reciprocal best-hit glycan-spectrum matching (RB-GSM) approach toward characterizing N-glycans. In this method, the MS/MS spectra in the input data set are evaluated against all glycans with the matched precursor mass using customized scoring functions, where a glycan-spectrum matching (GSM) is considered to be true if it is a reciprocal best-hit, that is, it receives the highest score among not only the GSMs between the respective spectrum and all matched glycans, but also the GSMs between the respective glycan and all matched MS/MS spectra in the input data set. We evaluated this RB-GSM approach on N-glycan identification using MS/MS spectra acquired from glycan standards as well as those released from the model glycoprotein fetuin, immunoglobulin G, and human serum samples, which showed the RB-GSM is capable of distinguishing isomeric glycans.

Targeted N-Glycan Analysis with Parallel Reaction Monitoring Using a Quadrupole-Orbitrap Hybrid Mass Spectrometer.

Targeted mass spectrometric analysis is widely employed across various omics fields. The approach has been successfully employed for the structural analysis of proteins, glycans, lipids, and small molecules. Selected reaction monitoring and multiple reaction monitoring (MRM) have been a method of choice for targeted structural studies of biomolecules. However, innovations in instrument designs have led to the development of parallel reaction monitoring (PRM). PRM detects all product ions simultaneously rather than optimizing/preselecting the target glycan transitions, simplifying the analytical workflow. By reducing background interference, increasing selectivity/specificity, and improving data quality, PRM allows reliable quantification of target glycans in complex matrices. PRM can also improve sensitivity for detecting low-abundance target glycans and reduce low-level limit of quantification values with an improved S/N ratio. PRM's advantages are attributed to the development of sensitive and highly selective mass analyzers, orbitrap, and time of flight. In this study, we developed a sensitive PRM method for the quantitative analysis of permethylated N-glycans, an important class of disease biomarkers, using a quadrupole-orbitrap hybrid mass spectrometer. Pooled human cerebrospinal fluid was used for the study as a source of permethylated N-glycans. The method illustrates the fragmentation of N-glycans at different collision energies as well as the optimization of collision energy. The method also detects low-abundance N-glycans more efficiently than MRM. This study is the first attempt to develop a sensitive PRM-based method to analyze permethylated N-glycans.

Direct Comparison of N-Glycans and Their Isomers Derived from Spike Glycoprotein 1 of MERS-CoV, SARS-CoV-1, and SARS-CoV-2.

The emergence of COVID-19 pandemic has engaged the scientific community around the globe in the rapid development of effective therapeutics and vaccines. Owing to its crucial role in the invasion of the host cell, spike (S) glycoprotein is one of the major targets in these studies. The S1 subunit of the S protein (S1 protein) accommodates the receptor-binding domain, which enables the initial binding of the virus to the host cell. Being a heavily glycosylated protein, numerous studies have investigated its glycan composition. However, none of the studies have explored the isomeric glycan distribution of this protein. Furthermore, this isomeric glycan distribution has never been compared to that in S1 proteins of other coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, which were responsible for past epidemics. This study explores the uncharted territory of the isomeric glycan distribution in the coronaviruses' S1 protein using liquid chromatography coupled to tandem mass spectrometry. We believe that our data would facilitate future investigations to study the role of isomeric glycans in coronavirus viral pathogenesis.

Mesoporous Graphitized Carbon Column for Efficient Isomeric Separation of Permethylated Glycans.

Post-translational modifications are vital aspects of functional proteins. Therefore, it is critical to understand their roles in biological processes. Glycosylation is particularly challenging to study among these modifications due to the heterogeneity displayed by the glycans in terms of their isomers. Thus, researchers continue to strive for the development of efficient liquid chromatography techniques for isomeric separation of glycans. Porous graphitized carbon (PGC) nano column has been one of the most widely used columns for this purpose, but poor stability and lack of reproducibility led to its discontinuation. In our endeavor to find an alternative stationary phase for isomeric glycan separation, we tested the mesoporous graphitized carbon (MGC) material. Unprecedentedly, satisfactory results were obtained with a column only 1 cm long, which was tested on permethylated N-glycans derived from model glycoproteins as well as biological samples. The column was found to be reproducible across months as well as across different column preparations. Additionally, to decrease the dead volume and attain a better resolution, MGC was utilized to pack a 1 cm length of a pulled capillary nanospray emitter and again demonstrated efficient isomeric separation. Thus, MGC proved to be a suitable stationary phase to obtain efficient isomeric separation of permethylated N-glycans with 1 cm-long packing length, in both capillary columns and packed nanospray emitters.

Using micro pillar array columns (µPAC) for the analysis of permethylated glycans.

Glycosylation is a complex and common post-translational modification of proteins. To study glycosylation, liquid chromatography-mass spectrometry (LC-MS) is often used to profile and structurally characterize the glycans in biological systems. While bed packed reverse phase columns are frequently utilized for the separation of permethylated glycans, the use of newly commercialized micro array pillar nanoLC columns (µPAC) have not been demonstrated previously. Owing to its advantages such as low back pressure, reproducibility, and durability, we have investigated the viability of the µPAC for the analysis of permethylated glycans. In this work, we demonstrate the online purification ability of µPAC trapping column compared against PepMap trapping column. We also found that the 50 cm µPAC can be used for the analysis of both permethylated N- and O-glycans. The use of 50 cm µPAC was compared against the previous method. The use of 200 cm µPAC was also investigated for the permethylated glycan analysis. 200 cm µPAC demonstrated efficient separation of oligomannose glycan isomers as well as other complex glycans.

Glucose unit index (GUI) of permethylated glycans for effective identification of glycans and glycan isomers.

Retention time is the most common and widely used criterion to report the separation of glycans using Liquid Chromatography (LC), but it varies widely across different columns, instruments and laboratories. This variation is problematic when inter-laboratory data is compared. Furthermore, it influences reproducibility and hampers efficient data interpretation. In our endeavor to overcome this variance, we propose the use of the Glucose Unit Index (GUI) on C18 and PGC column-based separation of reduced and permethylated glycans. GUI has previously been utilized for retention time normalization of native and labeled glycans. We evaluated this method with reduced and permethylated glycans derived from model glycoproteins fetuin and ribonuclease B (RNase B), and then implemented it to human blood serum to generate C18 and PGC column-based isomeric glycan libraries. GUI values for glycan compositions were calculated with respect to the glucose units derived from dextrin, which was employed as an elution standard. The GUI values were validated on three different LC systems (UltiMate 3000 Nano UHPLC systems) in two laboratories to ensure the reliability and reproducibility of the method. Applicability on real samples was demonstrated using human breast cancer cell lines. A total of 116 permethylated N-glycans separated on a C18 column and 134 glycans separated on a PGC column were compiled in a library. Overall, the established GUI method and the demonstration of reproducible inter- and intra-laboratory GUI values would aid the future development of automated glycan and isomeric glycan identification methods.

Advances in mass spectrometry-based glycomics.

The diversification of the chemical properties and biological functions of proteins is attained through posttranslational modifications, such as glycosylation. Glycans, which are covalently attached to proteins, play a vital role in cell activities. The microheterogeneity and complexity of glycan structures associated with proteins make comprehensive glycomic analysis challenging. However, recent advancements in mass spectrometry (MS), separation techniques, and sample preparation methods have primarily facilitated structural elucidation and quantitation of glycans. This review focuses on describing recent advances in MS-based techniques used for glycomic analysis (2012-2018), including ionization, tandem MS, and separation techniques coupled with MS. Progress in glycomics workflow involving glycan release, purification, derivatization, and separation will also be highlighted here. Additionally, the recent development of quantitative glycomics through comparative and multiplex approaches will also be described.

Exploring the structural, mechanical, magneto-electronic and thermophysical properties of f electron based XNpO3 perovskites (X = Na, Cs, Ca, Ra).

Here, we present systematic investigation of the structural and mechanical stability, electronic profile and thermophysical properties of f-electron based XNPO3 (X = Na, Cs, Ca, Ra) perovskites by first principles calculations. The structural optimization, tolerance factor criteria depicts the cubic structural stability of these alloys. Further, the stability of these materials is also determined by the cohesive and formation energy calculations along with mechanical stability criteria. The electronic structure is explored by calculating band structure and density of states which reveal the well-known half-metallic nature of the materials. Further, we have calculated different thermodynamic parameters including specific heat capacity, thermal expansion, Gruneisen parameter and their variation with temperature and pressure. The thermoelectric effectiveness of these materials is predicted in terms of Seebeck coefficient, electrical conductivity and power factor. All-inclusive we can say that calculated properties of these half-metallic materials extend their route in spintronics, thermoelectric and radioisotope generators device applications.

In silico Screening of Food and Drug Administration-approved Compounds against Trehalose 2-sulfotransferase (Rv0295c) in Mycobacterium tuberculosis: Insights from Molecular Docking and Dynamics Simulations.

BACKGROUND: Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations. METHODS: The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand-receptor complex. RESULTS: Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (-12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories. CONCLUSION: Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

Electrodeposition of PdPt Nanoparticles on Edges and S-Vacancies in Exfoliated MoS2 Nanosheets for Enhanced Hydrogen Evolution Activity.

Deposition of metal nanoparticles onto the molybdenum disulfide (MoS2) nanosheets is an efficient method to tune the electronic structure of the MoS2 and maximize its catalytic performance towards the hydrogen evolution reaction (HER). Herein, we report the electrodeposition of Pd and Pt nanoparticles onto desulfurized MoS2 nanosheets (MoS2-x) to achieve an improved HER activity in an acidic electrolyte. The initial MoS2 powder was exfoliated and isolated through centrifugation, followed by electrochemical desulfurization to create defect sites. Subsequently, Pt and Pd nanoparticles were electrodeposited onto the S-vacancies of MoS2-x nanosheets. The resulting PdPt nanoparticles, with a diameter of 3.3 ±1.7 nm, were distributed across the surfaces of the nanosheets. A preferential deposition was evident at the edges of the nanosheets, particularly when Pd was deposited first followed by Pt. Owing to this preferential deposition of Pd and Pt and the synergistic interaction of MoS2-x with Pd and Pt, the prepared catalyst exhibited a low overpotential of 30 mV at 10 mA cm-2, which is 2.7× lower than the MoS2-x alone. The prepared catalyst exhibited a 1.7× increase in the mass activity at 20 mV overpotential, relative to that of a commercial Pt/C nanocatalyst, showcasing its promising potential as an alternative catalyst.

Exploring the Cell Wall and Secretory Proteins of Mycobacterium leprae as Biomarkers.

The bacterial cell wall is composed of a wide variety of intricate proteins in addition to lipids, glycolipids, and polymers. Given the diversity of cell wall proteins among bacterial species, they are a feasible target for biomarker identification and characterization in clinical research and diagnosis of the disease. The slow growth rate of Mycobacterium leprae poses a major hurdle in the accurate diagnosis of leprosy before the onset of peripheral neuropathy. The use of biomarker- based diagnostic methods can help in preventing the spread and manifestation of leprosy. Despite many advances in research methods and techniques, there remains a knowledge gap regarding the cell wall proteomes of M. leprae that can be used as biomarkers. The cell wall and secretory proteins of M. leprae are the major focus of this review article. This article enfolds the characteristics and functions of M. leprae cell wall proteins and gives an insight into those cell wall proteins that are yet to be established as biomarkers. Tools and techniques used in cell wall extraction and biomarker identification can also be explored in this article.

Comparative Proteomic Analysis of Capsule Proteins in Aminoglycoside-Resistant and Sensitive Mycobacterium tuberculosis Clinical Isolates: Unraveling Potential Drug Targets.

BACKGROUND: Tuberculosis (TB), a global infectious threat, has seen a concerning rise in aminoglycoside-resistant Mycobacterium tuberculosis (M.tb) strains. The potential role of capsule proteins remains largely unexplored. This layer acts as the primary barrier for tubercle bacilli, attempting to infiltrate host cells and subsequent disease development. METHODS: The study aims to bridge this gap by investigating the differentially expressed capsule proteins in aminoglycoside-resistant M.tb clinical isolates compared with drug-sensitive isolates employing two-dimensional gel electrophoresis, mass spectrometry, and bioinformatic approaches. RESULTS: We identified eight proteins that exhibited significant upregulation in aminoglycoside-resistant isolates. Protein Rv3029c and Rv2110c were associated with intermediary metabolism and respiration; Rv2462c with cell wall and cell processes; Rv3804c with lipid metabolism; Rv2416c and Rv2623 with virulence and detoxification/adaptation; Rv0020c with regulatory functions; and Rv0639 with information pathways. Notably, the Group-based Prediction System for Prokaryotic Ubiquitin-like Protein (GPS-PUP) algorithm identified potential pupylation sites within all proteins except Rv3804c. Interactome analysis using the STRING 12.0 database revealed potential interactive partners for these proteins, suggesting their involvement in aminoglycoside resistance. Molecular docking studies revealed suitable binding between amikacin and kanamycin drugs with Rv2462c, Rv3804c, and Rv2623 proteins. CONCLUSION: As a result, our findings illustrate the multifaceted nature of aminoglycoside resistance in M.tb and the importance of understanding how capsule proteins play a role in counteracting drug efficacy. Identifying the role of these proteins in drug resistance is crucial for developing more effective treatments and diagnostics for TB.

Understanding the computational insights of spin-polarised density functional theory into the newly half-metallic f electron-based actinide perovskites SrMO3 (M = Pa, Np, Cm, Bk).

Here, we investigated the structural, mechanical, electronic, magnetic, thermodynamic and thermoelectric properties of Strontium based simple perovskites SrMO3 (M = Pa, Np, Cm, Bk) by using density functional theory. First and foremost, the ground state stability of these perovskites was initially evaluated by optimizing their total ground state energies in distinct ferromagnetic and non-magnetic configurations. The structural stability in terms of their ground state energies defines that these alloys stabilize in ferromagnetic rather than competing non-magnetic phase. From the understandings of mechanical parameters these alloys are characterized to be ductile in nature. After that, two approximation schemes namely Generalized Gradient approximation and Tran-Blaha modified Becke-Johnson potential have been used to find their intimate electronic structures which displays the half-metallic nature of these alloys. Further, we have verified temperature and pressure effect on these alloys. Finally, the transport properties have been evaluated within the selected temperature range of 150-900 K. In view of this, the different transport parameters along with half-metallic nature advocate their possible applications in thermoelectric and spintronics devices.

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing.

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

Analysis of socioeconomic condition and bacillary index with respect to the development of Hansen's disease.

Background: Leprosy is a chronic granulomatous infection caused by Mycobacterium leprae or Mycobacterium lepromatosis and mainly affects the skin and peripheral nerves. Although treatable, its early intervention can significantly reduce the occurrence of disability. India accounts for more than half of new cases globally. This study was undertaken to better understand the clinical traits of newly diagnosed cases in a tertiary facility of Western Uttar Pradesh, and a few from Madhya Pradesh and Uttarakhand. Methods: The observational prospective study was carried out on all the newly diagnosed leprosy cases who visited the Outpatient Department of ICMR-National JALMA Institute for Leprosy and Other Mycobacterial Diseases, Agra, during October 2019-December 2022. After obtaining answers to a prestructured questionnaire with their consent, participants were enrolled in the study and underwent clinical examination and a slit-skin smear test. Results: A total of 56 cases were investigated, and among them, 20 (35.7%) and 36 (64.3%) women and men, respectively, had positive contact with persons affected by leprosy either within family, friends, or neighbors. It is observed that due to the delayed detection of leprosy cases, paucibacillary (PB) patients converted into multibacillary (MB) patients, and the number of MB cases is much higher compared to PB cases. Conclusion: Leprosy instances continue to spread frequently from sick to healthy people indicating continued transmission of leprosy in society. Multidrug therapy in the management of leprosy cases is effective; however, early diagnosis of PB cases is still a challenge and needs to be addressed on priority.

Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach.

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.

Development of an In-Source Peltier Heater for Pulled Capillary Nanospray Emitter Columns.

Column compartments in liquid chromatography (LC) systems house the LC columns. These compartments are responsible for maintaining a suitable column environment for achieving optimal chromatographic performance. However, the advancements in instrument and column designs demand newer technologies. It is a well-established concept that decreasing the dead volume of the column improves the column resolution, thereby providing enhanced chromatographic separation. One of the major contributors in the dead volume is the line connecting the column in the LC compartment to the ion source in the mass spectrometer. Using in-source emitter columns is one strategy to enhance the resolution. However, ion sources without temperature control are not suitable for columns that are used at high temperatures. In this work, we are introducing a nano electrospray ionization source with an integrated Peltier heater designed for pulled capillary nanospray emitter columns. Although the performance of the device is demonstrated by showing the isomeric separation of permethylated glycans using a mesoporous graphitized carbon packed pulled capillary emitter, it can easily be paired with any nanospray emitter column that requires temperature control.

LC-MS/MS Isomeric Profiling of N-Glycans Derived from Low-Abundant Serum Glycoproteins in Mild Cognitive Impairment Patients.

Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities, such as language or virtual/spatial comprehension. This cognitive decline is mostly observed with the aging of individuals. Recently, MCI has been considered as a prodromal phase of Alzheimer's disease (AD), with a 10-15% conversion rate. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of serum N-glycan expression could represent essential contributors to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using non-invasive procedures. Herein, we undertook an LC-MS/MS glycomics approach to determine and characterize potential N-glycan markers in depleted blood serum samples from MCI patients. For the first time, we profiled the isomeric glycome of the low abundant serum glycoproteins extracted from serum samples of control and MCI patients using an LC-MS/MS analytical strategy. Additionally, the MRM validation of the identified data showed five isomeric N-glycans with the ability to discriminate between healthy and MCI patients: the sialylated N-glycans GlcNAc5,Hex6,Neu5Ac3 and GlcNAc6,Hex7,Neu5Ac4 with single AUCs of 0.92 and 0.87, respectively, and a combined AUC of 0.96; and the sialylated-fucosylated N-glycans GlcNAc4,Hex5,Fuc,Neu5Ac, GlcNAc5,Hex6,Fuc,Neu5Ac2, and GlcNAc6,Hex7,Fuc,Neu5Ac3 with single AUCs of 0.94, 0.67, and 0.88, respectively, and a combined AUC of 0.98. According to the ingenuity pathway analysis (IPA) and in line with recent publications, the identified N-glycans may play an important role in neuroinflammation. It is a process that plays a fundamental role in neuroinflammation, an important process in the progression of neurodegenerative diseases.

Exploring serum glycome patterns after moderate to severe traumatic brain injury: A prospective pilot study.

Background: Glycans play essential functional roles in the nervous system and their pathobiological relevance has become increasingly recognized in numerous brain disorders, but not fully explored in traumatic brain injury (TBI). We investigated longitudinal glycome patterns in patients with moderate to severe TBI (Glasgow Coma Scale [GCS] score ≤12) to characterize glyco-biomarker signatures and their relation to clinical features and long-term outcome. Methods: This prospective single-center observational study included 51 adult patients with TBI (GCS ≤12) admitted to the neurosurgical unit of the University Hospital of Pecs, Pecs, Hungary, between June 2018 and April 2019. We used a high-throughput liquid chromatography-tandem mass spectrometry platform to assess serum levels of N-glycans up to 3 days after injury. Outcome was assessed using the Glasgow Outcome Scale-Extended (GOS-E) at 12 months post-injury. Multivariate statistical techniques, including principal component analysis and orthogonal partial least squares discriminant analysis, were used to analyze glycomics data and define highly influential structures driving class distinction. Receiver operating characteristic analyses were used to determine prognostic accuracy. Findings: We identified 94 N-glycans encompassing all typical structural types, including oligomannose, hybrid, and complex-type entities. Levels of high mannose, hybrid and sialylated structures were temporally altered (p<0·05). Four influential glycans were identified. Two brain-specific structures, HexNAc5Hex3DeoxyHex0NeuAc0 and HexNAc5Hex4DeoxyHex0NeuAc1, were substantially increased early after injury in patients with unfavorable outcome (GOS-E≤4) (area under the curve [AUC]=0·75 [95%CI 0·59-0·90] and AUC=0·71 [0·52-0·89], respectively). Serum levels of HexNAc7Hex7DeoxyHex1NeuAc2 and HexNAc8Hex6DeoxyHex0NeuAc0 were persistently increased in patients with favorable outcome, but undetectable in those with unfavorable outcome. Levels of HexNAc5Hex4DeoxyHex0NeuAc1 were acutely elevated in patients with mass lesions and in those requiring decompressive craniectomy. Interpretation: In spite of the exploratory nature of the study and the relatively small number of patients, our results provide to the best of our knowledge initial evidence supporting the utility of glycomics approaches for biomarker discovery and patient phenotyping in TBI. Further larger multicenter studies will be required to validate our findings and to determine their pathobiological value and potential applications in practice. Funding: This work was funded by the Italian Ministry of Health (grant number GR-2013-02354960), and also partially supported by a NIH grant (1R01GM112490-08).