Mass spectrometry and protein analysis

Single-Frequency Ion Parking in a Digital 3D Quadrupole Ion Trap

Single-frequency ion parking, a useful technique in electrospray mass spectrometry (ESI-MS), involves gas-phase charge-reduction ion/ion reactions in an electrodynamic ion trap in conjunction with the application of a supplementary oscillatory voltage to selectively inhibit the reaction rate of an ion of interest. The ion parking process provides a means for limiting the extent of charge reduction in a controlled fashion and allows for ions distributed over a range of charge states to be concentrated into fewer charge states (a single charge state under optimal conditions). As charge reduction inherently leads to an increase in the mass-to-charge (m/z) ratio of the ions, it is important that the means for storing and analyzing ions be able to accommodate ions of high m/z ratios. The so-called 'digital ion trap' (DIT), which uses a digital waveform as the trapping RF, has been demonstrated to be well-suited for the analysis of high m/z ions by taking advantage of its ability to manipulate the waveform frequency. In this study, the feasibility of ion parking in a 3D quadrupole ion trap operated as a DIT using a slow-amplitude single-frequency sine-wave for selective inhibition of an ion/ion reaction is demonstrated. A recently described model that describes ion parking has been adjusted for the DIT case and is used to interpret experimental data for proteins ranging in mass from 8600 Da to 467,000 Da.

Capillary zone electrophoresis-tandem mass spectrometry for in-depth proteomics analysis via data-independent acquisition

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study.

Conformational and Structural Characterization of Knotted Proteins

Knotted proteins are fascinating natural biomolecules whose backbones entangle themselves in a knot. Their particular knotted configurations provide them with a wide range of topological features. However, their folding/unfolding mechanisms, stability, and function are poorly understood. In the present work, native trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) was used for characterizing structural features of two model knotted proteins: a Gordian 52 knot ubiquitin C-terminal hydrolase (UCH) and a Stevedore 61 knot (α-haloacid dehalogenase, DehI). Experimental results showed structural transitions of UCH and DehI as a function of solution composition (0-50% MeOH) and temperature (T ∼20-95 °C). An increase in the protein charge states and collision cross sections (∼2750-8750 Å2 and ∼3250-15,385 Å2 for UCH and DehI, respectively) with the solution organic content (OC) and temperature suggested a three-step unfolding pathway with at least four structural transitions. Results also showed that the integrity of the UCH knot core was more resistant to thermal unfolding when compared to DehI; however, both knot cores can be disrupted with the increase in the solution OC. Additional enzymatic digestion experiments using carboxypeptidase Y combined with molecular dynamics simulations showed that the knot core was preserved between Glu20 and Glu188 and Arg89 and His304 residues for UCH and DehI, respectively, where disruption of the knot core led to structural collapse followed by unfolding events. This work highlights the potential of solution OC and temperature studies combined with native TIMS-MS for the comprehensive characterization of knotted proteins to gain a better understanding of their structural transitions.

Digital Ion Trap Isolation and Mass Analysis of Macromolecular Analytes with Multiply Charged Ion Attachment

Multiply charged ions produced by electrospray ionization (ESI) of heterogeneous mixtures of macromolecular analytes under native conditions are typically confined to relatively narrow ranges of mass-to-charge (m/z) ratio, often with extensive overlap. This scenario makes charge and mass assignments extremely challenging, particularly when individual charge states are unresolved. An ion/ion reaction strategy involving multiply charged ion attachment (MIA) to the mixture components in a narrow range of m/z can facilitate charge and mass assignment. In MIA operation, multiply charged reagent ions are attached to the analyte ions of opposite polarity to provide large m/z displacements resulting from both large changes in mass and charge. However, charge reduction of the high m/z ions initially generated under native ESI conditions requires the ability to isolate high m/z ions and to analyze even higher m/z product ions. Digital ion trap (DIT) operation offers means for both high m/z ion isolation and high m/z mass analysis, in addition to providing conditions for the reaction of oppositely charged ions. The feasibility of conducting MIA experiments in a DIT that takes advantage of high m/z ion operation is demonstrated here using a tandem 2D-3D DIT instrument. Proof-of-concept MIA experiments with cations derived from β-galactosidase using the 20- charge state of human serum immunoglobulin G (IgG, ∼149 kDa) as the reagent anion are described. MIA experiments involving mixtures of ions derived from the E. coli. ribosome are also described. For example, three components in a mixture of 70S particles (>2.2 MDa) were resolved and assigned with masses and charges following an MIA experiment involving the 20- charge state of human serum IgG.

Application of urine proteomics in the diagnosis and treatment effectiveness monitoring of early-stage Mycosis Fungoides

BACKGROUND

Mycosis fungoides (MF) is the most common type of cutaneous T cell lymphoma. As the early clinical manifestations of MF are non-specific (e.g., erythema or plaques), it is often misdiagnosed as inflammatory skin conditions (e.g., atopic dermatitis, psoriasis, and pityriasis rosea), resulting in delayed treatment. As there are no effective biological markers for the early detection and management of MF, the aim of the present study was to perform a proteomic analysis of urine samples (as a non-invasive protein source) to identify reliable MF biomarkers.

METHODS

Thirteen patients with early-stage MF were administered a subcutaneous injection of interferon α-2a in combination with phototherapy for 6 months. The urine proteome of patients with early-stage MF before and after treatment was compared against that of healthy controls by liquid chromatography-tandem mass spectrometry. The differentially expressed proteins were subjected to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Clusters of Orthologous Groups analyses. For validation, the levels of the selected proteins were evaluated by enzyme-linked immunosorbent assay (ELISA).

RESULTS

We identified 41 differentially expressed proteins (11 overexpressed and 30 underexpressed) between untreated MF patients and healthy control subjects. The proteins were mainly enriched in focal adhesion, endocytosis, and the PI3K-Akt, phospholipase D, MAPK, and calcium signaling pathways. The ELISA results confirmed that the urine levels of Serpin B5, epidermal growth factor (EGF), and Ras homologous gene family member A (RhoA) of untreated MF patients were significantly lower than those of healthy controls. After 6 months of treatment, however, there was no significant difference in the urine levels of Serpin B5, EGF, and RhoA between MF patients and healthy control subjects. The area under the receiver operating characteristic curve values for Serpin B5, EGF, and RhoA were 0.817, 0.900, and 0.933, respectively.

CONCLUSIONS

This study showed that urine proteomics represents a valuable tool for the study of MF, as well as identified potential new biomarkers (Serpin B5, EGF, and RhoA), which could be used in its diagnosis and management.

A systematic review of predictive, diagnostic, and prognostic biomarkers for detecting reproductive diseases in cattle using traditional and omics approaches

Reproductive diseases and illnesses pose significant challenges in cattle farming, affecting fertility, milk production, and overall herd health. In recent years, the integration of various omics approaches, including transcriptomics, proteomics, metagenomics, miRNAomics, and metabolomics, has revolutionized the study of these conditions. This systematic review summarised the findings from studies that investigated reproductive disease biomarkers in both male and female cattle. After extracting 6137 studies according to exclusion and inclusion criteria, a total of 60 studies were included in this review. All studies identified were associated with female cattle and none were related to reproductive diseases in bulls. The analysis highlights specific biomarkers, metabolic pathways, and microbial compositions associated with bovine reproductive disease conditions, providing valuable insights into the underlying molecular mechanisms of disease. Pro-inflammatory cytokines such as IL-1β, IL-8, IL-4, IL-6, TNFα and acute-phase response proteins such as SAA and HP have been identified as promising biomarkers for bovine reproductive diseases. However, further research is needed to validate these markers clinically and to explore potential strategies for improving cow reproductive health. The role of bulls as carriers of venereal diseases has been underestimated in the current literature and therefore needs more attention to understand their impact on infectious reproductive diseases of female cattle.

Acoustically targeted measurement of transgene expression in the brain

Gene expression is a critical component of brain physiology, but monitoring this expression in the living brain represents a major challenge. Here, we introduce a new paradigm called recovery of markers through insonation (REMIS) for noninvasive measurement of gene expression in the brain with cell type, spatial, and temporal specificity. Our approach relies on engineered protein markers that are produced in neurons but exit into the brain's interstitium. When ultrasound is applied to targeted brain regions, it opens the blood-brain barrier and releases these markers into the bloodstream. Once in blood, the markers can be readily detected using biochemical techniques. REMIS can noninvasively confirm gene delivery and measure endogenous signaling in specific brain sites through a simple insonation and a subsequent blood test. REMIS is reliable and demonstrated consistent improvement in recovery of markers from the brain into the blood. Overall, this work establishes a noninvasive, spatially specific method of monitoring gene delivery and endogenous signaling in the brain.

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components

Fragment correlation mass spectrometry correlates ion pairs generated from the same fragmentation pathway, achieved by covariance mapping of tandem mass spectra generated with an unmodified linear ion trap without preseparation. We enable the identification of different precursors at different charge states in a complex mixture from a large isolation window, empowering an analytical approach for data-independent acquisition. The method resolves and matches isobaric fragments, internal ions, and disulfide bond fragments. We suggest that this method represents a major advance for analyzing structures of biopolymers in mixtures.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Protein analysis by desorption electrospray ionization mass spectrometry

This review presents progress made in the ambient analysis of proteins, in particular by desorption electrospray ionization-mass spectrometry (DESI-MS). Related ambient ionization techniques are discussed in comparison to DESI-MS only to illustrate the larger context of protein analysis by ambient ionization mass spectrometry. The review describes early and current approaches for the analysis of undigested proteins, native proteins, tryptic digests, and indirect protein determination through reporter molecules. Applications to mass spectrometry imaging for protein spatial distributions, the identification of posttranslational modifications, determination of binding stoichiometries, and enzymatic transformations are discussed. The analytical capabilities of other ambient ionization techniques such as LESA and nano-DESI currently exceed those of DESI-MS for in situ surface sampling of intact proteins from tissues. This review shows, however, that despite its many limitations, DESI-MS is making valuable contributions to protein analysis. The challenges in sensitivity, spatial resolution, and mass range are surmountable obstacles and further development and improvements to DESI-MS is justified.

Mobility capillary electrophoresis-native mass spectrometry reveals the dynamic conformational equilibrium of calmodulin and its complexes

Benefitting from the rapid evolution of artificial intelligence and structural biology, an expanding collection of high-resolution protein structures has greatly improved our understanding of protein functions. Yet, proteins are inherently flexible, and these static structures can only offer limited snapshots of their true dynamic nature. The conformational and functional changes of calmodulin (CaM) induced by Ca2+ binding have always been a focus of research. In this study, the conformational dynamics of CaM and its complexes were investigated using a mobility capillary electrophoresis (MCE) and native mass spectrometry (native MS) based method. By analyzing the ellipsoidal geometries of CaM in the solution phase at different Ca2+ concentrations, it is interesting to discover that CaM molecules, whether bound to Ca2+ or not, possess both closed and open conformations. Moreover, each individual CaM molecule actively "jumps" (equilibrium exchange) between these two distinct conformations on a timescale ranging from milli- to micro-seconds. The binding of Ca2+ ions did not affect the structural dynamics of CaM, while the binding of a peptide ligand would stabilize CaM, leading to the observation of a single, compact conformation of the resulting protein complex. A target recognition mechanism was also proposed based on these new findings, suggesting that CaM's interaction with targets may favor a conformational selection model. This enriches our understanding of the binding principles between CaM and its numerous targets.

Long term online desalting analysis of MS/LC-MS using thermal assisted recrystallization ionization

Mass spectrometric analysis of non-volatile salts containing samples remains challenging due to salt-induced ion suppression and contamination. This challenge is even more pronounced for a liquid chromatography-mass spectrometry analysis, where the accumulation of salts in the transmission system poses an ongoing problem. In this study, a novel thermal assisted recrystallization ionization mass spectrometry (TARI-MS) device was developed to achieve efficient on-line desalting and prolonged analysis of saline samples. The core component of this device was a heated plate positioned between the electrospray unit and the MS inlet. The desalting mechanism was demonstrated as the spontaneous separation of target molecules from salts during the "crystallization" process. After optimization, the angle between the nebulizer and the heated plate was 45°; the distance between the front end of the heated plate and the MS inlet was 2 mm; the distance between the front edge of the heated plate and the center of the sample spray projected onto the heating plate was 3 mm; the distance between the emitter of nebulizer and the heated plate was 3 mm. TARI-MS realized direct analysis of eight drugs dissolved in eight commonly used non-volatile salts solutions (up to 0.5 mol/L). The high sensitivity, repeatability, linearity, accuracy, and intra- and inter-day precision of TARI-MS confirm its reliability as a robust tool for the analysis of saline samples. Furthermore, TARI-MS allowed continuous analysis of salty eluates of LC for up to nearly 1 h without maintenance and verified the feasibility of LC-MS analysis through detecting a five-drug mixture and a crude aripiprazole product. Finally, six impurities in the crude aripiprazole product were successfully detected by LC-TARI-MS. The established method holds promise for applications across academic and pharmaceutical domains.

Single-Molecule Electrical Profiling of Peptides and Proteins

In recent decades, there has been a significant increase in the application of single-molecule electrical analysis platforms in studying proteins and peptides. These advanced analysis methods have the potential for deep investigation of enzymatic working mechanisms and accurate monitoring of dynamic changes in protein configurations, which are often challenging to achieve in ensemble measurements. In this work, the prominent research progress in peptide and protein-related studies are surveyed using electronic devices with single-molecule/single-event sensitivity, including single-molecule junctions, single-molecule field-effect transistors, and nanopores. In particular, the successful commercial application of nanopores in DNA sequencing has made it one of the most promising techniques in protein sequencing at the single-molecule level. From single peptides to protein complexes, the correlation between their electrical characteristics, structures, and biological functions is gradually being established. This enables to distinguish different molecular configurations of these biomacromolecules through real-time electrical monitoring of their life activities, significantly improving the understanding of the mechanisms underlying various life processes.

Drug hypersensitivity reactions: review of the state of the science for prediction and diagnosis

Drug hypersensitivity reactions (DHRs) are a type of adverse drug reaction that can occur with different classes of drugs and affect multiple organ systems and patient populations. DHRs can be classified as allergic or non-allergic based on the cellular mechanisms involved. Whereas nonallergic reactions rely mainly on the innate immune system, allergic reactions involve the generation of an adaptive immune response. Consequently, drug allergies are DHRs for which an immunological mechanism, with antibody and/or T cell, is demonstrated. Despite decades of research, methods to predict the potential for a new chemical entity to cause DHRs or to correctly attribute DHRs to a specific mechanism and a specific molecule are not well-established. This review will focus on allergic reactions induced by systemically administered low-molecular weight drugs with an emphasis on drug- and patient-specific factors that could influence the development of DHRs. Strategies for predicting and diagnosing DHRs, including potential tools based on the current state of the science, will also be discussed.

Fluorescence lifetime imaging microscopy of flexible and rigid dyes probes the biophysical properties of synthetic and biological membranes

Sensing of the biophysical properties of membranes using molecular reporters has recently regained widespread attention. This was elicited by the development of new probes of exquisite optical properties and increased performance, combined with developments in fluorescence detection. Here, we report on fluorescence lifetime imaging of various rigid and flexible fluorescent dyes to probe the biophysical properties of synthetic and biological membranes at steady state as well as upon the action of external membrane-modifying agents. We tested the solvatochromic dyes Nile red and 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine-N-(7-nitro-2-1,3-benzoxadiazol-4-yl) (ammonium salt) (NBD), the viscosity sensor Bodipy C12, the flipper dye FliptR, as well as the dyes 3,3'-dioctadecyloxacarbocyanine perchlorate (DiO), Bodipy C16, lissamine-rhodamine, and Atto647, which are dyes with no previous reported environmental sensitivity. The performance of the fluorescent probes, many of which are commercially available, was benchmarked with well-known environmental reporters, with Nile red and Bodipy C12 being specific reporters of medium hydration and viscosity, respectively. We show that some widely used ordinary dyes with no previous report of sensing capabilities can exhibit competing performance compared to highly sensitive commercially available or custom-based solvatochromic dyes, molecular rotors, or flipper in a wide range of biophysics experiments. Compared to other methods, fluorescence lifetime imaging is a minimally invasive and nondestructive method with optical resolution. It enables biophysical mapping at steady state or assessment of the changes induced by membrane-active molecules at subcellular level in both synthetic and biological membranes when intensity measurements fail to do so. The results have important consequences for the specific choice of the sensor and take into consideration factors such as probe sensitivity, response to environmental changes, ease and speed of data analysis, and the probe's intracellular distribution, as well as potential side effects induced by labeling and imaging.

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Genome sequence-based species classification of Enterobacter cloacae complex: a study among clinical isolates

Accurate species-level identification of Enterobacter cloacae complex (ECC) is crucial for related research. The classification of ECC is based on strain-to-strain phylogenetic congruence, as well as genomic features including average nucleotide identity (ANI) and digitalized DNA-DNA hybridization (dDDH). ANI and dDDH derived from whole-genome sequencing have emerged as a reliable metric for assessing genetic relatedness between genomes and are increasingly recognized as a standard for species delimitation. Up to now, there are two different classification methods for ECC. The first one categorizes E. hormaechei, a species within ECC, into five subspecies (E. hormaechei subsp. steigerwaltii, subsp. oharae, subsp. xiangfangensis, subsp. hoffmannii, and subsp. hormaechei). The second classifies E. hormaechei as three species: E. hormaechei, "E. xiangfangensis," "E. hoffmanii." While the former is well-accepted in the academic area, the latter may have a greater ability to distinguish different species of ECC. To assess the suitability of these identification criteria for clinical ECC isolates, we conducted a comprehensive analysis involving phylogenetic analysis, ANI and dDDH value alignment, virulence gene identification, and capsule typing on 256 clinical ECC strains isolated from the bloodstream. Our findings indicated that the method of categorizing E. hormaechei into five subspecies has better correlation and consistency with the molecular characteristics of clinical ECC isolates, as evidenced by phylogenetic analysis, virulence genes, and capsule typing. Therefore, the subspecies-based classification method appears more suitable for taxonomic assignments of clinical ECC isolates.

IMPORTANCE

Standardizing taxonomy of the Enterobacter cloacae complex (ECC) is necessary for data integration across diverse studies. The study utilized whole-genome data to accurately identify 256 clinical ECC isolated from bloodstream infections using average nucleotide identity (ANI), digitalized DNA-DNA hybridization (dDDH), and phylogenetic analysis. Through comprehensive assessments including phylogenetic analysis, ANI and dDDH comparisons, virulence gene, and capsule typing of the 256 clinical isolates, it was concluded that the classification method based on subspecies exhibited better correlation and consistency with the molecular characteristics of clinical ECC isolates. In summary, this research contributes to the precise identification of clinical ECC at the species level and expands our understanding of ECC.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Proteomic analysis of sperm from fertile stallions and subfertile stallions due to impaired acrosomal exocytosis

Thoroughbred stallions that carry a double-homozygous genotype A/A-A/A for SNPs rs397316122 and rs69101140 in exon 5 of the FKBP6 gene (chr13; EquCab3.0) are uniquely subfertile due to impaired acrosomal exocytosis (IAE). In this study, the sperm proteome in frozen/thawed semen from subfertile Thoroughbred stallions was studied and compared to that of frozen/thawed sperm from fertile Thoroughbred stallions. A total of 2,220 proteins was identified, of which 140 proteins were found to be differentially abundant in sperm from the subfertile stallions compared to that of fertile stallions (83 less and 57 more abundant). Proteins of differential abundance in sperm from the subfertile stallions were mainly overrepresented in the "metabolism" and the "metabolism of lipids" pathways. One of these proteins, arylsulfatase F (ARSF), was studied by immunofluorescence. A lower proportion of sperm displaying ARSF signal at the acrosome region was observed in sperm from subfertile Thoroughbred stallions. In addition, heterologous zona pellucida binding assays revealed that sperm from subfertile Thoroughbred stallions bound at a lower proportion to zonae pellucidae than sperm from fertile Thoroughbred stallions. In conclusion, a group of differential abundance proteins, including some of acrosome origin, were identified in sperm from subfertile stallions with acrosome dysfunction.

Wearable sampling of proteins from human exhaled aerosols for nano-liquid chromatography-tandem mass spectrometry analysis: A pilot study

RATIONALE

Human exhaled breath usually contains unique proteins that may provide clues to characterize individual physiological activities and many diseases. However, the concentration of exhaled proteins in exhaled breath is extremely low and usually does not reach the detection limits of all online breath mass spectrometry instruments. Therefore, developing a new breath sampler for collecting and characterizing exhaled proteins is important.

METHODS

In this study, a new mask-based wearable sampler was developed by fixing metal materials into the inner surface of the KN95 mask. Human exhaled proteins could be directly adsorbed onto the metal material while wearing the mask. After sampling, the collected proteins were eluted, digested, and identified using nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS).

RESULTS

The adsorption of exhaled proteins was evaluated, showing that modified gold foil is an effective material for collecting exhaled proteins. Various endogenous proteins were successfully identified from exhaled breath, many of which can be potential biomarkers for disease diagnosis.

CONCLUSIONS

By coupling the newly developed mask sampler with nano-LC-MS/MS, human exhaled proteins were successfully collected and identified. Our results show that the mask sampler is wearable, simple, and convenient, and the method is noninvasive for investigating disease diagnosis and human health.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

P62 promotes FSH-induced antral follicle formation by directing degradation of ubiquitinated WT1

In females, the pathophysiological mechanism of poor ovarian response (POR) is not fully understood. Considering the expression level of p62 was significantly reduced in the granulosa cells (GCs) of POR patients, this study focused on identifying the role of the selective autophagy receptor p62 in conducting the effect of follicle-stimulating hormone (FSH) on antral follicles (AFs) formation in female mice. The results showed that p62 in GCs was FSH responsive and that its level increased to a peak and then decreased time-dependently either in ovaries or in GCs after gonadotropin induction in vivo. GC-specific deletion of p62 resulted in subfertility, a significantly reduced number of AFs and irregular estrous cycles, which were same as pathophysiological symptom of POR. By conducting mass spectrum analysis, we found the ubiquitination of proteins was decreased, and autophagic flux was blocked in GCs. Specifically, the level of nonubiquitinated Wilms tumor 1 homolog (WT1), a transcription factor and negative controller of GC differentiation, increased steadily. Co-IP results showed that p62 deletion increased the level of ubiquitin-specific peptidase 5 (USP5), which blocked the ubiquitination of WT1. Furthermore, a joint analysis of RNA-seq and the spatial transcriptome sequencing data showed the expression of steroid metabolic genes and FSH receptors pivotal for GCs differentiation decreased unanimously. Accordingly, the accumulation of WT1 in GCs deficient of p62 decreased steroid hormone levels and reduced FSH responsiveness, while the availability of p62 in GCs simultaneously ensured the degradation of WT1 through the ubiquitin‒proteasome system and autophagolysosomal system. Therefore, p62 in GCs participates in GC differentiation and AF formation in FSH induction by dynamically controlling the degradation of WT1. The findings of the study contributes to further study the pathology of POR.

Assessing the evolution of research topics in a biological field using plant science as an example

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

PPARα Agonism Enhances Immune Response to Radiotherapy While Dietary Oleic Acid Results in Counteraction

PURPOSE

Head and neck cancer (HNC) improvements are stagnant, even with advances in immunotherapy. Our previous clinical trial data show that altered fatty acid (FA) metabolism correlates with outcome. We hypothesized that pharmacologic and dietary modulation of FA catabolism will affect therapeutic efficacy.

EXPERIMENTAL DESIGN

We performed in vivo and in vitro experiments using PPARα agonism with fenofibrate (FF) or high oleic acid diets (OAD) with radiotherapy, generating metabolomic, proteomic, stable isotope tracing, extracellular flux analysis, and flow-cytometric data to investigate these alterations.

RESULTS

FF improved antitumor efficacy of high dose per fraction radiotherapy in HNC murine models, whereas the OAD reversed this effect. FF-treated mice on the control diet had evidence of increased FA catabolism. Stable isotope tracing showed less glycolytic utilization by ex vivo CD8+ T cells. Improved efficacy correlated with intratumoral alterations in eicosanoid metabolism and downregulated mTOR and CD36.

CONCLUSIONS

Metabolic intervention with increased FA catabolism improves the efficacy of HNC therapy and enhances antitumoral immune response.

Ortho C-H Bond Activations in an Atmospheric Microwave Plasma Ion Source

C-H bond ortho-substitution reaction has always been a significant and challenging topic in organic chemistry. We proposed a synthesis method based on microwave plasma torches. High-resolution mass spectrometry was used to monitor rapid reaction products. 2-Alkylbenzimidazole can be formed through the reaction of phenylnitrenium ion and nitriles on a millisecond scale. This reaction can achieve the one-step formation of benzimidazoles from benzene ring single-substituted compounds without the addition of external oxidants or catalysts. A similar C-H bond activation reaction can be accomplished with ketones. Meanwhile, the microwave plasma reactor was modified, and the resulting 2-methylbenzimidazole was successfully collected, indicating the device has good application potential in organic reactions such as C-H bond activation reaction.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Recent advancements in disulfide bridge characterization: Insights from mass spectrometry

RATIONALE

Disulfide bridges (DSB) play an important role in stabilizing three-dimensional structures of biopharmaceuticals, single purified proteins, and various cyclic peptide drugs that contain disulfide in their structures. Incorrect cross-linking known as DSB scrambling results in misfolded structures that can be inactive, immunogenic, and susceptible to aggregation. Very few articles have been published on the experimental annotation of DSBs in proteins and cyclic peptide drugs. Accurate characterization of the disulfide bond is essential for understanding protein confirmation.

METHODS

Characterizing DSBs using mass spectrometry (MS) involves the chemical and enzymatic digestion of samples to obtain smaller peptide fragments, in both reduced and nonreduced forms. Subsequently, these samples are analyzed using MS to locate the DSB, either through interpretation or by employing various software tools.

RESULTS

The main challenge in DSB analysis methods using sample preparation is to obtain a sample solution in which nonnative DSBs are not formed due to high pH, temperature, and presence of free sulfhydryl groups. Formation of nonnative DSBs can lead to erroneous annotation of disulfide bond. Sample preparation techniques, fragmentation methods for DSB analysis, and contemporary approaches for DSB mapping using this fragmentation were discussed.

CONCLUSIONS

This review presents the latest advancement in MS-based characterization; also a critical perspective is presented for further annotation of DSBs using MS, primarily for single purified proteins or peptides that are densely connected and rich in cysteine. Despite significant breakthroughs resulting from advancements in MS, the analysis of disulfide bonds is not straightforward; it necessitates expertise in sample preparation and interpretation.

Electrospray triple quadrupole mass spectrometry guides pathologists to suggest appropriate molecular testing in the identification of rare hemoglobin variants

Background

The presumptive diagnosis of hemoglobinopathies relies on routine tests such as Complete Blood Count (CBC), peripheral blood smear, Liquid Chromatography (LC), and Capillary Electrophoresis (CE), along with clinical findings. Pathologists suggest molecular sequencing of HBA and HBB genes to correlate blood picture with clinical findings in order to identify unknown rare haemoglobin (Hb) variants or variants that coelute with Hb. This paper presents a low-resolution mass spectrometry (MS)-based method for presumptive identification of variants that eluted in zone 12 of CE, followed by molecular sequencing of the HBB gene for a definitive diagnosis of hemoglobinopathies.

Methods

Eight patient samples with a variant peak in zone 12 of CE (Sebia) were analyzed using MS. The mass-to-charge ratio (m/z) observed was deconvoluted to determine the mass of Hb variants. The β variants were subsequently confirmed through molecular sequencing.

Results

Based on the intact mass of the variants, there were two samples of the α variant (α + 58 Da and α + 44 Da), and six samples of the β variant. Out of these six β variant samples, three were the β + 58 Da variant, and three were the β + 30 Da variant. By correlating the intact mass information with the CE pattern and considering the ethnicity of the patients, it was presumed that the α variants were HbJ Meerut (α + 58 Da, x-axis 102) and HbJ Paris-I (α + 44 Da, x-axis 80). Molecular analysis confirmed the identity of β variants as Hb Rambam/HbJ Cambridge, HbJ Bangkok (+58 Da), and Hb Hofu (+30 Da).

Conclusion

The mass information of Hb variants obtained using Electrospray triple quadrupole MS assists pathologists in recommending the appropriate molecular sequencing for identifying unknown variants.

Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis

The human gut microbiota is a complex and dynamic community of microorganisms, that influence metabolic, neurodevelopmental, and immune pathways. Microbial dysbiosis, characterized by changes in microbial diversity and relative abundances, is implicated in the development of various chronic neurological and neurodegenerative disorders. These disorders are marked by the accumulation of pathological protein aggregates, leading to the progressive loss of neurons and behavioural functions. Dysregulations in protein-protein interaction networks and signalling complexes, critical for normal brain function, are common in neurological disorders but challenging to unravel, particularly at the neuron and synapse-specific levels. To advance therapeutic strategies, a deeper understanding of neuropathogenesis, especially during the progressive disease phase, is needed. Biomarkers play a crucial role in identifying disease pathophysiology and monitoring disease progression. Proteomics, a powerful technology, shows promise in accelerating biomarker discovery and aiding in the development of novel treatments. In this chapter, we provide an in-depth overview of how proteomic techniques, utilizing various biofluid samples from patients with neurological conditions and diverse animal models, have contributed valuable insights into the pathogenesis of numerous neurological disorders. We also discuss the current state of research, potential challenges, and future directions in proteomic approaches to unravel neuro-pathological conditions.

Anthracene and tetraphenylsilane based conjugated porous polymer nanoparticles for sensitive detection of nitroaromatics in water

Conjugated porous polymers (CPPs) are a kind of promising sensing materials for the detection of nitroaromatic compounds, but their sensing applications in aqueous media are limited because of their poor dispersity or solubility in water. In this study, we prepared anthracene and tetraphenylsilane based CPPs named PSiAn by conventional Suzuki coupling and Suzuki-miniemulsion polymerization, respectively. The structure, morphology and porosity of the CPPs were characterized by Fourier Transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (1H NMR), transmission electron microscope (TEM) and N2 sorption isotherm, respectively. Both of the CPPs have porous structure which is beneficial for the adsorption and diffusion of the analytes within them. The particle size of PSiAn nanoparticles prepared by Suzuki-miniemulsion polymerization is 10-40 nm from the TEM image, which facilitates the dispersion in the aqueous phase. Combined with the porosity and nanoparticle morphology, PSiAn nanoparticles realized the efficient photoluminescence (PL) sensing of nitroaromatic explosives in aqueous phase. The limit of detection (LOD) and limit of quantitation (LOQ) of PSiAn nanoparticles for 2,4,6-trinitrophenol (TNP) detection in the pure aqueous phase are 0.33 μM and 1.11 μM, respectively. Meanwhile, the good selectivity and anti-interference in presence of other nitro-compounds were observed. Furthermore, the spike/recovery test for the TNP detection in real water samples by PL sensing based on PSiAn nanoparticles indicates the quantitative recovery of TNP from 100.74 % to 101.00 %. The electrochemical test, ultraviolet-visible absorption spectra, excitation and emission spectra, and time-resolved PL spectra were investigated to explore the PL sensing mechanism. As a result, it is found that the fluorescence inner filter effect might be the predominant quenching mechanism during the detection of nitrophenolic compounds such as TNP and 4-nitrophenol (4-NP).

Origin of functional de novo genes in humans from "hopeful monsters"

For a long time, it was believed that new genes arise only from modifications of preexisting genes, but the discovery of de novo protein-coding genes that originated from noncoding DNA regions demonstrates the existence of a "motherless" origination process for new genes. However, the features, distributions, expression profiles, and origin modes of these genes in humans seem to support the notion that their origin is not a purely "motherless" process; rather, these genes arise preferentially from genomic regions encoding preexisting precursors with gene-like features. In such a case, the gene loci are typically not brand new. In this short review, we will summarize the definition and features of human de novo genes and clarify their process of origination from ancestral non-coding genomic regions. In addition, we define the favored precursors, or "hopeful monsters," for the origin of de novo genes and present a discussion of the functional significance of these young genes in brain development and tumorigenesis in humans. This article is categorized under: RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

Chemical compositional analysis of jet fuels: Contributions of mass spectrometry in the 21st century

Jet fuels are complex mixtures composed of many individual compounds that influence crucial chemical and physical properties. Approximately over the last 20 years, mass spectrometry studies provided important and extensive qualitative and quantitative information of the compounds that make up jet fuels. This review presents these main findings, evaluates the analytical methods utilized, and summarizes the hydrocarbons, nitrogen-, oxygen- and sulfur-containing compounds characterized in the jet fuels. Potential areas where mass spectrometry may play important roles in the future will also be discussed.

Highly sensitive SERS platform for pathogen analysis by cyclic DNA nanostructure@AuNP tags and cascade primer exchange reaction

The capacity to identify small amounts of pathogens in real samples is extremely useful. Herein, we proposed a sensitive platform for detecting pathogens using cyclic DNA nanostructure@AuNP tags (CDNA) and a cascade primer exchange reaction (cPER). This platform employs wheat germ agglutinin-modified Fe3O4@Au magnetic nanoparticles (WMRs) to bind the E. coli O157:H7, and then triggers the cPER to generate branched DNA products for CDNA tag hybridization with high stability and amplified SERS signals. It can identify target pathogens as low as 1.91 CFU/mL and discriminate E. coli O157:H7 in complex samples such as water, milk, and serum, demonstrating comparable or greater sensitivity and accuracy than traditional qPCR. Moreover, the developed platform can detect low levels of E. coli O157:H7 in mouse serum, allowing the discrimination of mice with early-stage infection. Thus, this platform holds promise for food analysis and early infection diagnosis.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Low expression of TGF-β2 and matrilin2 in human aqueous humour with acute primary angle closure

Primary angle-closure glaucoma (PACG) is the leading cause of irreversible blindness in the world. Angle closure induced by pupil block and secondary iris synechia is the fundamental pathology of the PACG. The molecular mechanisms of angle closure have not yet been clearly illustrated. This study was designed to investigate the protein difference in the aqueous humour and explore new biomarker of the PACG. Aqueous humour (AH) was collected from patients with acute primary angle closure (APAC) and cataract (n = 10 in APAC group) and patients with cataract only (n = 10 in control group). Samples were pooled and measured using label-free proteome technology. Then, the differentially expressed proteins (DEPs) were verified by ELISA using independent AH samples (n = 20 each group). More than 400 proteins were revealed in both groups through proteomics. Comparing the two groups, there were 91DEPs. These proteins participate in biological activities such as inflammation, fibrosis, nerve growth and degeneration and metabolism. We found that the expression of transforming growth factor-β2 and matrilin2 was downregulated in the APAC group. The two proteins are related to inflammation and extracellular matrix formation, which might be involved in angle closure. This study characterized DEPs in AH of the APAC and found a downregulated protein matrilin2.

Chemoproteomics, A Broad Avenue to Target Deconvolution

As a vital project of forward chemical genetic research, target deconvolution aims to identify the molecular targets of an active hit compound. Chemoproteomics, either with chemical probe-facilitated target enrichment or probe-free, provides a straightforward and effective approach to profile the target landscape and unravel the mechanisms of action. Canonical methods rely on chemical probes to enable target engagement, enrichment, and identification, whereas click chemistry and photoaffinity labeling techniques improve the efficiency, sensitivity, and spatial accuracy of target recognition. In comparison, recently developed probe-free methods detect protein-ligand interactions without the need to modify the ligand molecule. This review provides a comprehensive overview of different approaches and recent advancements for target identification and highlights the significance of chemoproteomics in investigating biological processes and advancing drug discovery processes.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Discovery of β-sitosterol's effects on molecular changes in rat diabetic wounds and its impact on angiogenesis and macrophages

Diabetes care, particularly for diabetic foot ulcers (DFUs)-related complications, increases treatment costs substantially. Failure to provide timely and appropriate treatment for severe DFUs significantly increases amputation risk. Neovascularization and macrophage polarization play an important role in diabetic wound healing during different stages of the wound repair process. Therefore, a new treatment method that promotes neovascularization and macrophage polarization may accelerate diabetic wound healing. β-sitosterol possesses anti-inflammatory, lipid-lowering, and antidiabetic properties. However, its therapeutic potential in diabetic wound healing remains underexplored. This study evaluated the healing effects of β-sitosterol on diabetic ulcer wounds in rats. We found that β-sitosterol can promote angiogenesis, alternatively activated macrophages (M2 macrophage) proliferation, and collagen synthesis in diabetic wounds. Transcriptomics analysis and proteomics analysis revealed that MAPK, mTOR and VEGF signaling pathways were enriched in β-sitosterol-treated wounds. Molecular docking revealed Ndufb5 maybe the target of β-sitosterol-treated wounds. Our findings confirm the significant diabetic wound healing effects of β-sitosterol in a rat model. β-sitosterol treatment to diabetic wounds accelerates wound healing through promoting M2 macrophage proliferation and angiogenesis. Interestingly, we also found that the process of M2 macrophage proliferation accompanies angiogenesis. Thus, β-sitosterol may be a promising therapeutic approach to enhance diabetic wound healing and reduce amputation in diabetes.

Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Probing Protein Complexes Composition, Stoichiometry, and Interactions by Peptide-Based Mass Spectrometry

The characterization of a protein complex by mass spectrometry can be conducted at different levels. Initial steps regard the qualitative composition of the complex and subunit identification. After that, quantitative information such as stoichiometric ratios and copy numbers for each subunit in a complex or super-complex is acquired. Peptide-based LC-MS/MS offers a wide number of methods and protocols for the characterization of protein complexes. This chapter concentrates on the applications of peptide-based LC-MS/MS for the qualitative, quantitative, and structural characterization of protein complexes focusing on subunit identification, determination of stoichiometric ratio and number of subunits per complex as well as on cross-linking mass spectrometry and hydrogen/deuterium exchange as methods for the structural investigation of the biological assemblies.

Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies

Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of gene expression depends on being able to identify promoters, because they are the most important component of gene expression. Agrobacterium tumefaciens (A. tumefaciens) strain C58 was the subject of this study with the goal of creating a machine learning-based model to predict promoters. In this study, nucleotide density (ND), k-mer, and one-hot were used to encode the promoter sequence. Support vector machine (SVM) on fivefold cross-validation with incremental feature selection (IFS) was used to optimize the generated features. These improved characteristics were then used to distinguish promoter sequences by feeding them into the random forest (RF) classifier. Tenfold cross-validation (CV) analysis revealed that the projected model has the ability to produce an accuracy of 84.22%.

M-protein detection by mass spectrometry for minimal residual disease in multiple myeloma

Multiple myeloma (MM) is characterized by excessive production of monoclonal immunoglobulins (M proteins). Routine screening methods for M proteins to assess prognosis are unable to detect low levels of M proteins produced by residual tumor cells, ie, minimal residual disease (MRD). Assessment of MRD can be conducted by examining residual tumor cells in bone marrow or circulating M proteins. Advances in mass spectrometry have enabled reliable and highly sensitive detection of low abundance serum biomarkers making it a viable and significantly less invasive approach. Mass spectrometry can achieve dynamic monitoring of MRD and identify therapeutic monoclonal antibodies as well as oligoclonal proteins. In this review we summarize mass spectrometry methods in M protein detection and their applications of MRD detection in MM.

Advanced Imaging Techniques for the Characterization of Subcellular Organelle Structure in Pancreatic Islet β Cells

Type 2 diabetes (T2D) affects more than 32.3 million individuals in the United States, creating an economic burden of nearly $966 billion in 2021. T2D results from a combination of insulin resistance and inadequate insulin secretion from the pancreatic β cell. However, genetic and physiologic data indicate that defects in β cell function are the chief determinant of whether an individual with insulin resistance will progress to a diagnosis of T2D. The subcellular organelles of the insulin secretory pathway, including the endoplasmic reticulum, Golgi apparatus, and secretory granules, play a critical role in maintaining the heavy biosynthetic burden of insulin production, processing, and secretion. In addition, the mitochondria enable the process of insulin release by integrating the metabolism of nutrients into energy output. Advanced imaging techniques are needed to determine how changes in the structure and composition of these organelles contribute to the loss of insulin secretory capacity in the β cell during T2D. Several microscopy techniques, including electron microscopy, fluorescence microscopy, and soft X-ray tomography, have been utilized to investigate the structure-function relationship within the β cell. In this overview article, we will detail the methodology, strengths, and weaknesses of each approach. © 2024 American Physiological Society. Compr Physiol 14:5243-5267, 2024.

Interfacial Electromigration for Analysis of Biofluid Lipids in Small Volumes

Lipids are important biomarkers within the field of disease diagnostics and can serve as indicators of disease progression and predictors of treatment effectiveness. Although lipids can provide important insight into how diseases initiate and progress, mass spectrometric methods for lipid characterization and profiling are limited due to lipid structural diversity, particularly the presence of various lipid isomers. Moreover, the difficulty of handling small-volume samples exacerbates the intricacies of biological analyses. In this work, we have developed a strategy that electromigrates a thin film of a small-volume biological sample directly to the air-liquid interface formed at the tip of a theta capillary. Importantly, we seamlessly integrated in situ biological lipid extraction with accelerated chemical derivatization, enabled by the air-liquid interface, and conducted isomeric structural characterization within a unified platform utilizing theta capillary nanoelectrospray ionization mass spectrometry, all tailored for small-volume sample analysis. We applied this unified platform to the analysis of lipids from small-volume human plasma and Alzheimer's disease mouse serum samples. Accelerated electro-epoxidation of unsaturated lipids at the interface allowed us to characterize lipid double-bond positional isomers. The unique application of electromigration of a thin film to the air-liquid interface in combination with accelerated interfacial reactions holds great potential in small-volume sample analysis for disease diagnosis and prevention.

Data-independent acquisition (DIA) mass spectrometry reveals related proteins involved in the occurrence of early intestinal-type gastric cancer

Identifying proteins associated with the onset of early intestinal-type gastric cancer (EIGC) can yield valuable insights into the pathogenesis of this specific subtype of gastric cancer. Data-independent acquisition mass spectroscopy (DIA-MS) was utilized to identify the differential protein between 10 cases of EIGC and atrophic gastritis with intestinal metaplasia (NGC). The expressions of IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were verified by immunohistochemistry (IHC) in 20 EIGC samples, 17 gastric low-grade intraepithelial neoplasia (LGIN) samples, and 21 healthy controls. The prognostic values of the five genes were validated in the transcriptome data by survival analysis. A total of 4,028 proteins were identified using DIA-MS and a total of 177 differential proteins were screened with log2(fold change) > 1.5. Among them, 113 proteins were significantly up-regulated, and 64 proteins were significantly down-regulated in EIGC tissues. IHC results showed that proteins IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were highly expressed in the cytoplasm of EIGC and LGIN, which was consistent with the results of DIA-MS. Among them, p62/SQSTM1 may undergo nuclear-cytoplasmic transfer. The five protein-coding genes were associated with intestinal-type gastric cancer survival and exhibited differential expression across various disease stages. The study successfully identified differentially expressed proteins between EIGC and NGC, providing potential biomarkers and valuable insights into the mechanism underlying intestinal-type gastric cancer.

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and searching against such libraries has been shown to improve the sensitivity in peptide identification over conventional sequence database searching. However, current prediction models lack support for longer peptides, and thus far, predicted library searching has only been demonstrated for backbone ion-only spectrum prediction methods. Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. We demonstrated the superiority of using full-spectrum libraries over backbone ion-only prediction approaches in spectral library searching. Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. We also show that a hybrid library combining predicted and experimental spectra can lead to 20% more confident identifications over experimental library searching or sequence database searching.

Ursolic acid derivative UAOS-Na treats experimental autoimmune encephalomyelitis by immunoregulation and protecting myelin

Introduction

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS). Ursolic acid (UA) can be used in the MS treatment with anti-inflammatory and neuroprotective activities. However, UA is insoluble in water, which may affect its medication effectiveness. In our previous study, UAOS-Na, a water-soluble derivative of UA was obtained. In this study, we evaluated the pharmacological effects and explored its underlying mechanism of UAOS-Na on experimental autoimmune encephalomyelitis (EAE).

Methods

Firstly, the pharmacodynamics of UAOS-Na was investigated in EAE and Cuprizone-induced mice. And then the possible mechanisms were investigated by TMT proteomics and verified by in vitro and in vivo experiments.

Results

UAOS-Na (30 mg/kg/d) delayed the onset time of EAE from 11.78 days post immunization (dpi) to 14.33 dpi, reduced the incidence from 90.0% to 42.9%. UAOS-Na (60 mg/kg/d) reduced the serum levels of IFN-γ, IL-17A, TNF-α and IL-6, reduced the mononuclear cell infiltration of spinal cord, and inhibited the overexpression of key transcription factors T-bet and ROR-γt of EAE mouse spinal cord. In addition, UAOS-Na attenuated demyelination and astrogliosis in the CNS of EAE and cuprizone-induced mice. Mechanistically, proteomics showed that 96 differential expression proteins (DEPs) were enriched and 94 were upregulated in EAE mice compared with normal group. After UAOS-Na treatment, 16 DEPs were enriched and 15 were downregulated, and these DEPs were markedly enriched in antigen processing and presentation (APP) signaling pathway. Moreover, UAOS-Na downregulated the protein levels of Tapbp and H2-T23 in MHC-I antigen presentation pathway and reduced the proliferation of splenic CD8 T cells, thereby inhibiting the CNS infiltration of CD8 T cells.

Conclusion

Our findings demonstrated that UAOS-Na has both myelin protective and anti-inflammatory effects. And it could reduce the inflammation of MS by downregulating the expression of Tapbp and H2-T23 in the MHC-I antigen presentation pathway.