Next-generation proteomics: towards an integrative view of proteome dynamics

Gain-of-function PPM1D mutations attenuate ischemic stroke

Identification of genetic aberrations in stroke, the second leading cause of death worldwide, is of paramount importance for understanding the disease pathogenesis and generating new therapies. Whole-genome sequencing from 10,241 ischemic stroke patients identified eight patients carrying gain-of-function mutations on coding variants in the protein phosphatase magnesium-dependent 1 δ (PPM1D) gene. Patients carrying PPM1D mutations exhibit better stroke-related clinical phenotypes, including improvements in peripheral inflammation, fibrinogen, low-density lipoprotein, cholesterol and plateletcrit level. Experimental brain ischemia in Ppm1d-deficient (Ppm1d-/-) mice resulted in enlarged lesions and pronounced neurological impairments. Spatial transcriptomics revealed a distinct Ppm1d-associated gene expression pattern, indicating disrupted endothelial homeostasis during ischemic brain injury. Proteomic analysis demonstrated that differentially expressed proteins in primary brain endothelial cells from Ppm1d-/- mice were significantly enriched in the peroxisome proliferator-activated receptors (PPARs)-mediated metabolic signaling. Mechanistically, Ppm1d deficiency promoted aberrant fatty acid β-oxidation and increased oxidative stress, which impaired endothelial cell function through the PPARα pathway. A small molecule, T2755, was identified to engage Trp427 and stabilize PPM1D, thereby mitigating ischemic brain injury in mice. Collectively, we find that PPM1D protects against ischemic brain injury and validates its pharmacological stabilizer T2755 as a promising therapy for ischemic stroke. Gain-of-function PPM1D mutations attenuate ischemic cerebral injury. Whole-genome sequencing data of 10,241 ischemic stroke patients from the Third Chinese National Stroke Registry (CNSR-III) identified eight patients with gain-of-function mutations in the protein phosphatase magnesium-dependent 1 δ (PPM1D) gene (17q23.2). These mutation carriers displayed improved peripheral inflammation, decreased fibrinogen, low-density lipoprotein, cholesterol and plateletcrit level. Ppm1d-deficient (Ppm1d-/-) mice exhibited exacerbated stroke outcomes, characterized by enlarged infarct volumes, disrupted cerebrovascular architecture, and enhanced neuro-inflammation. Mechanistically, Ppm1d deficiency induced the disturbance of endothelial fatty acid metabolism involving the PPARα pathway. Through integrated computational modeling, virtual screening, and in vitro validation, T2755 was identified as a small molecule PPM1D stabilizer. Pharmacological PPM1D stabilization with T2755 significantly attenuated ischemic brain injury in murine models.

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models

This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reproducible models for peptide property predictions. Key limitations of the current models, including difficulties in handling diverse post-translational modifications and instrument variability, highlight the need for large-scale, harmonized datasets, and standardized evaluation metrics for benchmarking.

Proteomic and Metabolomic Analysis of the Neuroprotective Effects of Lycium Ruthenicum Polyphenols in Diabetic Peripheral Neuropathy Mice

Lycium ruthenicum polyphenols (LRP) have been proven to be anti-inflammatory, antioxidant, and neuroprotective phytochemicals. This study applies proteomics and metabolomics to LRP-treated db/db mice to explore its potential effects mechanism. The experiments were divided into three groups: normal control db/m group, diabetic peripheral neuropathy (DPN) db/db group, and LRP-treated db/db group. We examined physiological and biochemical indicators, behavioral indicators, and histopathology. As for the mechanism, we used TMT-based quantification proteomics and LC-MS/MS-based metabolomics for sciatic nerve and serum. After 8 weeks of treatment, the fasting blood glucose level, mechanical withdrawal threshold, and thermal hyperalgesia were significantly improved. Pathological examination showed a significant alleviation in sciatic nerve histomorphology in the LRP group. Proteomics and metabolomics showed that the interventional effects of LRP were enriched mainly in oxidative phosphorylation, cardiac muscle contraction, and serum metabolites were enriched mainly in amino acid metabolism. LRP improves neurological function by improving mitochondrial functions, promoting neuronal development, and ameliorating dysregulation of amino acid metabolism. These results provide theoretical evidence for LRP as a potential functional food ingredient for the prevention and treatment of DPN.

Advances in cancer genomics and precision oncology

BACKGROUND

Next-generation sequencing has revolutionized genome science over the last two decades. Indeed, the wealth of sequence information on our genome has deepened our understanding on cancer. Cancer is a genetic disease caused by genetic or epigenetic alternations that affect the expression of genes that control cell functions, particularly cell growth and division. Utilization of next-generation sequencing in cancer gene panels has enabled the identification of actionable gene alterations in cancer patients to guide personalized precision medicine.

OBJECTIVE

The aim is to provide information that can identify actionable gene alterations, enabling personalized precision medicine for cancer patients.

RESULTS & DISCUSSION

Equipped with next-generation sequencing techniques, international collaboration programs on cancer genomics have identified numerous mutations, gene fusions, microsatellite variations, copy number variations, and epigenetics changes that promote the transformation of normal cells into tumors. Cancer classification has traditionally been based on cell type or tissue-of-origin and the morphological characteristics of the cancer. However, interactive genomic analyses have currently reclassified cancers based on systemic molecular-based taxonomy. Although all cancer-causing genes and mechanisms have yet to be completely understood or identified, personalized or precision medicine is now currently possible for some forms of cancer. Unlike the "one-size-fits-all" approach of traditional medicine, precision medicine allows for customized or personalized treatment based on genomic information.

CONCLUSION

Despite the availability of numerous cancer gene panels, technological innovation in genomics and expansion of knowledge on the cancer genome will allow precision oncology to manage even more types of cancers.

Impact of urbanization on the house sparrow (Passer domesticus): Serum proteome and pathogen prevalence

The house sparrow (Passer domesticus) is a globally distributed species found in rural, urban and other humanised environments. In Europe, sparrow populations have significantly declined in recent decades, especially in urbanised areas. In the present study, we analysed the impact of urbanization on sparrow body condition, pathogen prevalence, and serum proteome changes. Sparrows were captured in four locations with two different urbanization status (rural/urban). Biometric data, blood samples and oral and cloacal swabs were collected. Rural sparrows exhibited significantly better body condition compared to urban sparrows, with no notable differences between sexes. Haemoparasite prevalence was higher in rural sparrows 70.16 % (87/124) than in urban sparrows 50 % (27/54). No avian influenza virus (AIV) or West Nile virus (WNV) genetic material was found, although one urban sparrow (0.58 %) had antibodies to AIV. Serum proteomics revealed that rural sparrows showed an up-regulation of proteins involved in the metabolism, in contrast to proteins of the immune system and the coagulation system, which were found to be over-represented in urban sparrows. Thus, we documented a worse body condition and immune system activation in urban sparrows in contrast to a more active metabolism and a higher prevalence of avian malaria in rural sparrows, and at least occasional exposure to AIV in urban habitats. This information suggests exposure to urban environments may alter the host-pathogen relationship. Urbanization in combination with exposure to AIV, could modulate their role in viral spread and transmission.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Proteogenomic characterisation of primary oral cancer unveils extracellular matrix remodelling and immunosuppressive microenvironment linked to lymph node metastasis

Oral squamous cell carcinoma (OSCC) is an increasingly prevalent malignancy worldwide. This study aims to understand molecular alterations associated with lymph node metastasis of OSCC in order to improve treatment strategies. We analysed a cohort of 46 patients with primary OSCC, including 10 with lymph node metastasis and 36 without. Using a comprehensive multi-omics approach - encompassing genomic, transcriptomic, proteomic, epigenetic, single-cell, and spatial analyses - we integrated data to delineate the molecular landscape of OSCC in the context of lymph node metastasis. Our genomic analysis identified significant mutations in key genes within the MAPK, TGF-β and WNT signalling pathways, which are essential for tumour development. The proteogenomic analysis highlighted pathways critical for lymph node dissemination and factors contributing to an immunosuppressive tumour microenvironment. Elevated levels of POSTN were found to reorganise the extracellular matrix (ECM), interact with TGF-β, disrupt cell cycle regulation and suppress the immune response by reducing VCAM1 activity. Integrated analyses of single-cell and spatial transcriptome data revealed that cancer-associated fibroblasts (CAFs) secrete TGF-β1/2, promoting cancer cell metastasis through epithelial-mesenchymal transition (EMT). Our integrated multi-omics analysis provides a detailed understanding of molecular mechanisms driving lymph node metastasis of OSCC. These insights could lead to more precise diagnostics and targeted treatments. This article is protected by copyright. All rights reserved.

SERPINA3, FGA, AGP1, ITIH3 and SAA1 as novel biomarkers for eosinophilic granulomatosis with polyangiitis diagnosis and activity assessment

OBJECTIVE

The objective of this study was to identify novel biomarkers for diagnosis and prediction of active eosinophilic granulomatosis with polyangiitis (EGPA) through data-independent acquisition (DIA) analysis.

METHODS

Plasma samples from 11 EGPA patients and 10 healthy controls (HCs) were analysed through DIA to identify potential biomarkers. The results were validated in 32 EGPA patients, 24 disease controls (DCs), and 20 HCs using ELISA. The receiver operating characteristic (ROC) curve was used to assess the diagnostic value of candidate biomarkers.

RESULTS

Thirty-five differentially expressed proteins (DEPs) (24 upregulated and 11 downregulated) were screened between the EGPA and HC groups. Five proteins, including serine proteinase inhibitor A3 (SERPINA3), alpha-fibrinogen (FGA), alpha-1 acid glycoprotein 1(AGP1), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), and serum amyloid A1 (SAA1), were significantly upregulated in EGPA compared with HCs. Apart from SAA1, all proteins were also higher in EGPA patients compared with DCs. Furthermore, a panel of SERPINA3 and SAA1 exhibited potential diagnostic value for EGPA, with an area under the curve (AUC) of 0.953, while a panel of SERPINA3, FGA, AGP1 and ITIH3 showed good discriminative power for differentiating EGPA from DCs, with an AUC of 0.926. Moreover, SERPINA3, FGA and AGP levels were significantly higher in active EGPA and correlated well with disease activity. A combination of SERPINA3 and AGP1 exhibited an excellent AUC of 0.918 for disease activity assessment.

CONCLUSION

SERPINA3, FGA, AGP1, ITIH3 and SAA1 were identified as potential biomarkers for EGPA diagnosis and disease activity assessment. Among them, as a single biomarker, SERPINA3 had the best diagnostic performance.

The membrane may be a key factor influencing browning: a mini review on browning mechanisms of fresh-cut fruit and vegetables from a multi-omics perspective

Fresh-cut fruit and vegetables are susceptible to browning during storage and subsequent consumption. The cell membrane acts as a vital structural barrier, compartmentalizing various substances within living organisms. The fresh-cutting process induces mechanical injuries, disrupting these membranes and resulting in the leakage of cellular contents. This facilitates direct contact between substances and enzymes that mediate browning reactions. This mini review explores the potential roles of cell membranes in the browning of fresh-cut fruit and vegetables from a multi-omics perspective, aiming to provide novel insights into the underlying mechanisms of browning in fresh-cut fruit and vegetables. Considering potential roles of cell membranes in blocking the browning of fresh-cut fruit and vegetables, future studies should focus on elucidating the precise mechanisms by which membranes regulate browning reactions, aiming to provide directions for the development of more effective intervention strategies.

Multi-omics in Allergic Rhinitis: Mechanism Dissection and Precision Medicine

Allergic rhinitis (AR) is a common chronic inflammatory airway disease caused by inhaled allergens, and its prevalence has increased in recent decades. AR not only causes nasal leakage, itchy nose, nasal congestion, sneezing, and allergic conjunctivitis but also induces asthma, as well as sleep disorders, anxiety, depression, memory loss, and other phenomena that seriously affect the patient's ability to study and work, lower their quality of life, and burden society. The current methods used to diagnose and treat AR are still far from ideal. Multi-omics technology can be used to comprehensively and systematically analyze the differentially expressed DNA, RNA, proteins, and metabolites and their biological functions in patients with AR. These capabilities allow for an in-depth understanding of the intrinsic pathogenic mechanism of AR, the ability to explore key cells and molecules that drive its progression, and to design personalized treatment for AR. This article summarizes the progress made in studying AR by use of genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in order to illustrate the important role of multi-omics technologies in facilitating the precise diagnosis and treatment of AR.

pLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification. We evaluated peptide embeddings generated from BEPLER, CPCProt, SeqVec, various ESM variants (ESM, ESM-2 with expanded feature set, ESM-1b, and ESM-1v), ProtT5-XL UniRef50, ProtT5-XL BFD, and ProtBERT. We developed pLM4CCPs, a novel deep learning architecture using convolutional neural networks (CNNs) as the classifier for binary classification of CPPs. pLM4CCPs demonstrated superior performance over existing state-of-the-art CPP prediction models, achieving improvements in accuracy (ACC) by 4.9-5.5%, Matthews correlation coefficient (MCC) by 9.3-10.2%, and sensitivity (Sn) by 14.1-19.6%. Among all the tested models, ESM-1280 and ProtT5-XL BFD demonstrated the highest overall performance on the kelm data set. ESM-1280 achieved an ACC of 0.896, an MCC of 0.796, a Sn of 0.844, and a specificity (Sp) of 0.978. ProtT5-XL BFD exhibited superior performance with an ACC of 0.901, an MCC of 0.802, an Sn of 0.885, and an Sp of 0.917. pLM4CCPs combine predictions from multiple models to provide a consensus on whether a given peptide sequence is classified as a CPP or non-CPP. This approach will enhance prediction reliability by leveraging the strengths of each individual model. A user-friendly web server for bioactivity predictions, along with data sets, is available at https://ry2acnp6ep.us-east-1.awsapprunner.com. The source code and protocol for adapting pLM4CPPs can be accessed on GitHub at https://github.com/drkumarnandan/pLM4CPPs. This platform aims to advance CPP prediction and peptide functionality modeling, aiding researchers in exploring peptide functionality effectively.

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

Deciphering the safeguarding role of cysteine residues in p53 against H2O2-induced oxidation using high-resolution native mass spectrometry

The transcription factor p53 is exquisitely sensitive and selective to a broad variety of cellular environments. Several studies have reported that oxidative stress weakens the p53-DNA binding affinity for certain promoters depending on the oxidation mechanism. Despite this body of work, the precise mechanisms by which the physiologically relevant DNA-p53 tetramer complex senses cellular stresses caused by H2O2 are still unknown. Here, we employed native mass spectrometry (MS) and ion mobility (IM)-MS coupled to chemical labelling and H2O2-induced oxidation to examine the mechanism of redox regulation of the p53-p21 complex. Our approach has found that two reactive cysteines in p53 protect against H2O2-induced oxidation by forming reversible sulfenates.

Desalting strategies for native mass spectrometry

In native mass spectrometry (MS) salts are indispensable for preserving the native structures of biomolecules, but detrimental to mass sensitivity, resolution, and accuracy. Such a conflict makes desalting in native MS more challenging, distinctive, and sample-dependent than in peptide-centric MS. This review first briefly introduces the charged residue mechanism whereby native-like gaseous protein ions are released from electrospray droplets, revealing a higher degree of salt adduction than denatured proteins. Subsequently, this review summarizes and explores the existing strategies, underlying mechanisms and future perspectives of desalting in native MS. These strategies mainly focus on buffer exchange into volatile salts (offline and online approaches), addition of solution additives (e.g., anion, supercharging reagent, solution phase chelator and amino acid), use of submicron electrospray emitters (down to 60 nm), and other potential approaches (e.g., induced and electrophoretic nanoelectrospray ionization). The strategies of online buffer exchange and using nanoscale electrospray emitters are highlighted. This review would not only be a valuable addition to the field of sample preparation in MS, but would also serve as a beginner's guide to desalting in native MS.

Advances in the Degradation of Polycyclic Aromatic Hydrocarbons by Yeasts: A Review

Polycyclic aromatic hydrocarbons (PAHs) are toxic organic compounds produced during the incomplete combustion of organic materials and are commonly found in the environment due to anthropogenic activities such as industrial and vehicular emissions as well as natural sources, mainly volcanic eruptions and forest fires. PAHs are well known for their bioaccumulative capacity and environmental persistence, raising concerns due to their adverse effects on human health, including their carcinogenic potential. In recent years, bioremediation has emerged as a promising, effective, and sustainable solution for the degradation of PAHs in contaminated environments. In this context, yeasts have proven to be key microorganisms in the degradation of these compounds, owing to their ability to metabolize them through a series of enzymatic pathways. This review explores the advancements in yeast-mediated degradation of PAHs, with a particular focus on the role of enzymes such as cytochrome P450 (CYPs), epoxide hydrolases (EHs), and glutathione S-transferases (GSTs), which facilitate the breakdown of these compounds. The review also discusses the applications of genetic engineering to enhance the efficiency of yeasts in PAH degradation and the use of omics technologies to predict the catabolic potential of these organisms. Additionally, it examines studies addressing the degradation of benzo[a]pyrene (BaP) by yeasts such as Debaryomyces hansenii, and the potential future implications of omics sciences for developing new bioremediation.

Proteomic and ubiquitinome analysis reveal that microgravity affects glucose metabolism of mouse hearts by remodeling non-degradative ubiquitination

Long-term exposure to a microgravity environment leads to structural and functional changes in hearts of astronauts. Although several studies have reported mechanisms of cardiac damage under microgravity conditions, comprehensive research on changes at the protein level in these hearts is still lacking. In this study, proteomic analysis of microgravity-exposed hearts identified 156 differentially expressed proteins, and ubiquitinomic analysis of these hearts identified 169 proteins with differential ubiquitination modifications. Integrated ubiquitinomic and proteomic analysis revealed that differential proteomic changes caused by transcription affect the immune response in microgravity-exposed hearts. Additionally, changes in ubiquitination modifications under microgravity conditions excessively activated certain kinases, such as hexokinase and phosphofructokinase, leading to cardiac metabolic disorders. These findings provide new insights into the mechanisms of cardiac damage under microgravity conditions.

Plasma proteomics profile-based comparison of torso versus brain injury: A prospective cohort study

BACKGROUND

Trauma-related deaths and posttraumatic sequelae are a global health concern, necessitating a deeper understanding of the pathophysiology to advance trauma therapy. Proteomics offers insights into identifying and analyzing plasma proteins associated with trauma and inflammatory conditions; however, current proteomic methods have limitations in accurately measuring low-abundance plasma proteins. This study compared plasma proteomics profiles of patients from different acute trauma subgroups to identify new therapeutic targets and devise better strategies for personalized medicine.

METHODS

This prospective observational single-center cohort study was conducted between August 2020 and September 2021 in the intensive care unit of Osaka University Hospital in Japan. Enrolling 59 consecutive patients with blunt trauma, we meticulously analyzed plasma proteomics profiles in participants with torso or head trauma, comparing them with those of controls (mild trauma). Using the Olink Explore 3072 instrument (Olink Proteomics AB, Uppsala, Sweden), we identified five endotypes (α-ε) via unsupervised hierarchical clustering.

RESULTS

The median time from injury to blood collection was 47 minutes [interquartile range, 36-64 minutes]. The torso trauma subgroup exhibited 26 unique proteins with significantly altered expression, while the head trauma subgroup showed 68 unique proteins with no overlap between the two. The identified endotypes included α (torso trauma, n = 8), β (young patients with brain injury, n = 5), γ (severe brain injury postsurgery, n = 8), δ (torso or brain trauma with mild hyperfibrinolysis, n = 18), and ε (minor trauma, n = 20). Patients with torso trauma showed changes in blood pressure, smooth muscle adaptation, hypermetabolism, and hypoxemia. Patients with traumatic brain injury had dysregulated blood coagulation and altered nerves regeneration and differentiation.

CONCLUSION

This study identified unique plasma protein expression patterns in patients with torso trauma and traumatic brain injury, helping categorize five distinct endotypes. Our findings may offer new insights for clinicians, highlighting potential strategies for personalized medicine and improved trauma-related care.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

DCC, a potential target for controlling fear memory extinction and hippocampal LTP in male mice receiving single prolonged stress

Post-traumatic stress disorder (PTSD) is a severe stress-dependent psychiatric disorder characterized by impairment of fear memory extinction; however, biological markers to determine impaired fear memory extinction in PTSD remain unclear. In male mice with PTSD-like behaviors elicited by single prolonged stress (SPS), 19 differentially expressed proteins in the hippocampus were identified compared with controls. Among them, a biological macromolecular protein named deleted in colorectal cancer (DCC) was highly upregulated. Specific overexpression of DCC in the hippocampus induced similar impairment of long-term potentiation (LTP) and fear memory extinction as observed in SPS mice. The impairment of fear memory extinction in SPS mice was improved by inhibiting the function of hippocampal DCC using a neutralizing antibody. Mechanistic studies have shown that knocking down or inhibiting μ-calpain in hippocampal neurons increased DCC expression and induced impairment of fear memory extinction. Additionally, SPS-triggered impairment of hippocampal LTP and fear memory extinction could be rescued through activation of the Rac1-Pak1 signaling pathway. Our study provides evidence that calpain-mediated regulation of DCC controls hippocampal LTP and fear memory extinction in SPS mice, which likely through activation of the Rac1-Pak1 signaling pathway.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Dual-Probe Activity-Based Protein Profiling Reveals Site-Specific Differences in Protein Binding of EGFR-Directed Drugs

Comparative, dose-dependent analysis of interactions between small molecule drugs and their targets, as well as off-target interactions, in complex proteomes is crucial for selecting optimal drug candidates. The affinity of small molecules for targeted proteins is largely dictated by interactions between amino acid side chains and these drugs. Thus, studying drug-protein interactions at an amino acid resolution provides a comprehensive understanding of the drug selectivity and efficacy. In this study, we further refined the site-specific activity-based protein profiling strategy (ABPP), PhosID-ABPP, on a timsTOF HT mass spectrometer. This refinement enables dual dose-dependent competition of inhibitors within a single cellular proteome. Here, a comparative analysis of two activity-based probes (ABPs), developed to selectively target the epidermal growth factor receptor (EGFR), namely, PF-06672131 (PF131) and PF-6422899 (PF899), facilitated the simultaneous identification of ABP-specific binding sites at a proteome-wide scale within a cellular proteome. Dose-dependent probe-binding preferences for proteinaceous cysteines, even at low nanomolar ABP concentrations, could be revealed. Notably, in addition to the intrinsic affinity of the electrophilic probes for specific sites in targeted proteins, the observed labeling intensity is influenced by several other factors. These include the efficiency of cellular uptake, the stability of the probes, and their intracellular distribution. While both ABPs showed comparable labeling efficiency for EGFR, PF131 had a broader off-target reactivity profile. In contrast, PF899 exhibited a higher labeling efficiency for the ERBB2 receptor and bound to catalytic cysteines in several other enzymes, which is likely to disrupt their catalytic activity. Notably, PF131 effectively labeled ADP/ATP translocase proteins at a concentration of just 1 nm, and we found this affected ATP transport. Analysis of the effect of PF131 and its parent inhibitor Afatinib on murine translocase SLC25A4 (ANT1)-mediated ATP transport strongly indicated that PF131 (10 μM) partially blocked ATP transport. Afatinib was less efficient at inhibiting ATP transport by SLC25A4 than PF131, and the reduction of ATP transport by Afatinib was not significant. Follow-up analysis is required to evaluate the affinity of these inhibitors for ADP/ATP translocase SLC25A4 in more detail. Additionally, the analysis of different binding sites within the EGF receptor and the voltage-dependent anion channel 2 revealed secondary binding sites of both probes and provided insights into the binding poses of inhibitors on these proteins. Insights from the PhosID-ABPP analysis of these two ABPs serve as a valuable resource for understanding drug on- and off-target engagement in a dose- and site-specific manner.

Conjoint analysis of succinylome and phosphorylome reveals imbalanced HDAC phosphorylation-driven succinylayion dynamic contibutes to lung cancer

Cancerous genetic mutations result in a complex and comprehensive post-translational modification (PTM) dynamics, in which protein succinylation is well known for its ability to reprogram cell metabolism and is involved in the malignant evolution. Little is known about the regulatory interactions between succinylation and other PTMs in the PTM network. Here, we developed a conjoint analysis and systematic clustering method to explore the intermodification communications between succinylome and phosphorylome from eight lung cancer patients. We found that the intermodification coorperation in both parallel and series. Besides directly participating in metabolism pathways, some phosphosites out of mitochondria were identified as an upstream regulatory modification directing succinylome dynamics in cancer metabolism reprogramming. Phosphorylated activation of histone deacetylase (HDAC) in lung cancer resulted in the removal of acetylation and favored the occurrence of succinylation modification of mitochondrial proteins. These results suggest a tandem regulation between succinylation and phosphorylation in the PTM network and provide HDAC-related targets for intervening mitochondrial succinylation and cancer metabolism reprogramming.

Exploring the Molecular Therapeutic Mechanisms of Gemcitabine through Quantitative Proteomics

Gemcitabine (GEM) is widely employed in the treatment of various cancers, including pancreatic cancer. Despite their clinical success, challenges related to GEM resistance and toxicity persist. Therefore, a deeper understanding of its intracellular mechanisms and potential targets is urgently needed. In this study, through mass spectrometry analysis in data-dependent acquisition mode, we carried out quantitative proteomics (three independent replications) and thermal proteome profiling (TPP, two independent replications) on MIA PaCa-2 cells to explore the effects of GEM. Our proteomic analysis revealed that GEM led to the upregulation of the cell cycle and DNA replication proteins. Notably, we observed the upregulation of S-phase kinase-associated protein 2 (SKP2), a cell cycle and chemoresistance regulator. Combining SKP2 inhibition with GEM showed synergistic effects, suggesting SKP2 as a potential target for enhancing the GEM sensitivity. Through TPP, we pinpointed four potential GEM binding targets implicated in tumor development, including in breast and liver cancers, underscoring GEM's broad-spectrum antitumor capabilities. These findings provide valuable insights into GEM's molecular mechanisms and offer potential targets for improving treatment efficacy.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

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.

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia

This study aimed to identify key proteomic analytes correlated with response to splenectomy in primary immune thrombocytopenia (ITP). Thirty-four patients were retrospectively collected in the training cohort and 26 were prospectively enrolled as validation cohort. Bone marrow biopsy samples of all participants were collected prior to the splenectomy. A total of 12 modules of proteins were identified by weighted gene co-expression network analysis (WGCNA) method in the developed cohort. The tan module positively correlated with megakaryocyte counts before splenectomy (r = 0.38, p = 0.027), and time to peak platelet level after splenectomy (r = 0.47, p = 0.005). The blue module significantly correlated with response to splenectomy (r = 0.37, p = 0.0031). KEGG pathways analysis found that the PI3K-Akt signalling pathway was predominantly enriched in the tan module, while ribosomal and spliceosome pathways were enriched in the blue module. Machine learning algorithm identified the optimal combination of biomarkers from the blue module in the training cohort, and importantly, cofilin-1 (CFL1) was independently confirmed in the validation cohort. The C-index of CFL1 was >0.7 in both cohorts. Our results highlight the use of bone marrow proteomics analysis for deriving key analytes that predict the response to splenectomy, warranting further exploration of plasma proteomics in this patient population.

Proteomic analyses on chicken breast meat with white striping myopathy

White striping (WS) is an emerging myopathy that results in significant economic losses as high as $1 billion (combined with losses derived from other breast myopathies including woody breast and spaghetti meat) to the global poultry industry. White striping is detected as the occurrence of white lines on raw poultry meat. The exact etiologies for WS are still unclear. Proteomic analyses of co-expressed WS and woody breast phenotypes previously demonstrated dysfunctions in carbohydrate metabolism, protein synthesis, and calcium buffering capabilities in muscle cells. In this study, we conducted shotgun proteomics on chicken breast fillets exhibiting only WS that were collected at approximately 6 h postmortem. After determining WS severity, protein extractions were conducted from severe WS meat with no woody breast (WB) condition (n = 5) and normal non-affected (no WS) control meat (n = 5). Shotgun proteomics was conducted by Orbitrap Lumos, tandem mass tag (TMT) analysis. As results, 148 differentially abundant proteins (|fold change|>1.4; p-value < 0.05) were identified in the WS meats compared with controls. The significant canonical pathways included BAG2 signaling pathway, glycogen degradation II, isoleucine degradation I, aldosterone signaling in epithelial cells, and valine degradation I. The potential upstream regulators include LIPE, UCP1, ATP5IF1, and DMD. The results of this study provide additional insights into the cellular mechanisms on the WS myopathy and meat quality.

Ultrasensitive and Multiplexed Protein Imaging with Clickable and Cleavable Fluorophores

Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction, and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity, or are technically demanding. To tackle these issues, here, we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through repeated cycles of target staining, fluorescence imaging, and fluorophore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ∼820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed that different subregions of the tissue are composed of cells from specific clusters.

DeKinomics pulse-chases kinase functions in living cells

Cellular context is crucial for understanding the complex and dynamic kinase functions in health and disease. Systematic dissection of kinase-mediated cellular processes requires rapid and precise stimulation ('pulse') of a kinase of interest, as well as global and in-depth characterization ('chase') of the perturbed proteome under living conditions. Here we developed an optogenetic 'pulse-chase' strategy, termed decaging kinase coupled proteomics (DeKinomics), for proteome-wide profiling of kinase-driven phosphorylation at second-timescale in living cells. We took advantage of the 'gain-of-function' feature of DeKinomics to identify direct kinase substrates and further portrayed the global phosphorylation of understudied receptor tyrosine kinases under native cellular settings. DeKinomics offered a general activation-based strategy to study kinase functions with high specificity and temporal resolution under living conditions.

Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers

Sensitive analytical techniques that are capable of detecting and quantifying disease-associated biomolecules are indispensable in our efforts to understand disease mechanisms and guide therapeutic intervention through early detection, accurate diagnosis, and effective monitoring of disease. Parkinson's Disease (PD), for example, is one of the most prominent neurodegenerative disorders in the world, but the diagnosis of PD has primarily been based on the observation of clinical symptoms. The protein α-synuclein (α-syn) has emerged as a promising biomarker candidate for PD, but a lack of analytical methods to measure complex disease-associated variants of α-syn has prevented its widespread use as a biomarker. Antibody-based methods such as immunoassays and mass spectrometry-based approaches have been used to measure a limited number of α-syn forms; however, these methods fail to differentiate variants of α-syn that display subtle differences in only the sequence and structure. In this work, we developed a cyclic ion mobility-mass spectrometry method that combines multiple stages of activation and timed ion selection to quantify α-syn variants using both mass- and structure-based measurements. This method can allow for the quantification of several α-syn variants present at physiological levels in biological fluid. Taken together, this approach can be used to galvanize future efforts aimed at understanding the underlying mechanisms of PD and serves as a starting point for the development of future protein-structure-based diagnostics and therapeutic interventions.

Extracellular vesicles in pancreatic cancer: a new era in precision medicine

Pancreatic cancer (PC) is a lethal disease that presents a considerable challenge to healthcare providers and patients, given its low survival rate. However, recent advancements in precision medicine and innovative technologies have transformed the management of this disease. Among these advancements, extracellular vesicles (EVs) have emerged as crucial players in cancer progression. In PC, EVs play a pivotal role by facilitating cell-cell communication, impeding immune response, promoting cancer cell proliferation and survival, and supporting angiogenesis and chemoresistance. Cancer-derived EVs have a distinct oncogenic composition supporting tumour development and progression. Hence, they are critical biomarker candidates for various cancers, including PC. Notably, EVs can be isolated from diverse biological fluids such as blood, urine, and saliva, making them an ideal minimally invasive diagnostic and monitoring tool for PC patients. Despite the promising findings in the field of EVs, clinical validation as biomarkers is lacking. Furthermore, EVs being biocompatible, can act as drug carriers, delivering therapeutic molecules directly to cancer cells while minimizing toxicity to healthy cells. Therefore, understanding the role of EVs as biomarkers and their potential as drug cargo vehicles may revolutionise early detection, prognostication, and treatment in cancer. This mini-review summarises the latest understanding of their role in intercellular communication, involvement as potential biomarkers and drug carriers.

The omics era: a nexus of untapped potential for Mendelian chromatinopathies

The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.

Evolution of techniques and tools for replication fork proteome and protein interaction studies

Understanding the complex network of protein-protein interactions (PPI) that govern cellular functions is essential for unraveling the molecular basis of biological processes and diseases. Mass spectrometry (MS) has emerged as a powerful tool for studying protein dynamics, enabling comprehensive analysis of protein function, structure, post-translational modifications, interactions, and localization. This article provides an overview of MS techniques and their applications in proteomics studies, with a focus on the replication fork proteome. The replication fork is a multi-protein assembly involved in DNA replication, and its proper functioning is crucial for maintaining genomic integrity. By combining quantitative MS labeling techniques with various data acquisition methods, researchers have made significant strides in elucidating the complex processes and molecular mechanisms at the replication fork. Overall, MS has revolutionized our understanding of protein dynamics, offering valuable insights into cellular processes and potential targets for therapeutic interventions.

Multiomics analysis revealed the mechanism of the anti-diabetic effect of Salecan

Salecan, a natural β-glucan compromising nine residues connected by β-(1 → 3)/α-(1 → 3) glycosidic bonds, is one of the newly approved food ingredients. Salecan has multiple health-improving effects, yet its mechanism against Type 2 diabetes mellitus (T2DM) remains poorly understood. In this study, the hypoglycemic effect and underlying mechanism of Salecan intervention on STZ-induced diabetic model mice were investigated. After 8 weeks of gavage, Salecan attenuated insulin resistance and repaired pancreatic β cells in a dose-dependent manner. In addition, Salecan supplement remodel the structure of the gut microbiota and altered the level of intestinal metabolites. Serum metabolites, especially unsaturated fatty acids, were also affected significantly. In addition, tight junction proteins in the colon and autophagy-related proteins in the pancreas were upregulated. Multiomics analysis indicated that Lactobacillus johnsonii, Muribaculaceae, and Lachnoclostridium were highly associated with fatty acid esters of hydroxy fatty acids (FAHFA) levels in the colon, accordingly enhancing arachidonic acid and linoleic acid in serum, and promoting GLP-1 release in the intestine and insulin secretion in the pancreas, thus relieving insulin resistance and exhibiting hypoglycemic effects. These findings provide a novel understanding of the anti-diabetic effect of Salecan in mice from a molecular perspective, paving the way for the wide use of Salecan.

Allergy-associated biomarkers in early life identified by Omics techniques

The prevalence and severity of allergic diseases have increased over the last 30 years. Understanding the mechanisms responsible for these diseases is a major challenge in current allergology, as it is crucial for the transition towards precision medicine, which encompasses predictive, preventive, and personalized strategies. The urge to identify predictive biomarkers of allergy at early stages of life is crucial, especially in the context of major allergic diseases such as food allergy and atopic dermatitis. Identifying these biomarkers could enhance our understanding of the immature immune responses, improve allergy handling at early ages and pave the way for preventive and therapeutic approaches. This minireview aims to explore the relevance of three biomarker categories (proteome, microbiome, and metabolome) in early life. First, levels of some proteins emerge as potential indicators of mucosal health and metabolic status in certain allergic diseases. Second, bacterial taxonomy provides insight into the composition of the microbiota through high-throughput sequencing methods. Finally, metabolites, representing the end products of bacterial and host metabolic activity, serve as early indicators of changes in microbiota and host metabolism. This information could help to develop an extensive identification of biomarkers in AD and FA and their potential in translational personalized medicine in early life.

Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Reverse stable isotope labelling with Raman spectroscopy for microbial proteomics

Global proteome changes in microbes affect the survival and overall production of commercially relevant metabolites through different bioprocesses. The existing methods to monitor proteome level changes are destructive in nature. Stable isotope probing (SIP) coupled with Raman spectroscopy is a relatively new approach for proteome analysis. However, applying this approach for monitoring changes in a large culture volume is not cost-effective. In this study, for the first time we are presenting a novel method of combining reverse SIP using 13 C-glucose and Deuterium to monitor the proteome changes through Raman spectroscopy. The findings of the study revealed visible changes (blue shifts) in proteome related peaks that can be used for monitoring proteome dynamics, that is, synthesis of nascent amino acids and its turnover with time in a non-destructive, cost-effective, and label-free manner.

Phosphorylation Dynamics in a flg22-Induced, G Protein-Dependent Network Reveals the AtRGS1 Phosphatase

The microbe-associated molecular pattern flg22 is recognized in a flagellin-sensitive 2-dependent manner in root tip cells. Here, we show a rapid and massive change in protein abundance and phosphorylation state of the Arabidopsis root cell proteome in WT and a mutant deficient in heterotrimeric G-protein-coupled signaling. flg22-induced changes fall on proteins comprising a subset of this proteome, the heterotrimeric G protein interactome, and on highly-populated hubs of the immunity network. Approximately 95% of the phosphorylation changes in the heterotrimeric G-protein interactome depend, at least partially, on a functional G protein complex. One member of this interactome is ATBα, a substrate-recognition subunit of a protein phosphatase 2A complex and an interactor to Arabidopsis thaliana Regulator of G Signaling 1 protein (AtRGS1), a flg22-phosphorylated, 7-transmembrane spanning modulator of the nucleotide-binding state of the core G-protein complex. A null mutation of ATBα strongly increases basal endocytosis of AtRGS1. AtRGS1 steady-state protein level is lower in the atbα mutant in a proteasome-dependent manner. We propose that phosphorylation-dependent endocytosis of AtRGS1 is part of the mechanism to degrade AtRGS1, thus sustaining activation of the heterotrimeric G protein complex required for the regulation of system dynamics in innate immunity. The PP2A(ATBα) complex is a critical regulator of this signaling pathway.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction

MOTIVATION

Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances.

RESULTS

To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements.

AVAILABILITY AND IMPLEMENTATION

The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Multiply-mesoporous hydrophilic titanium dioxide nanohybrid for the highly-performed enrichment of N-glycopeptides from human serum

N-glycopeptide is considered as one of significant biomarkers which provide guidance for the diagnosis and drug design of diseases. However, the direct analysis of N-glycopeptides is nearly impracticable mainly owing to their extremely low abundance and grave signal suppression from other interfering substances in the bio-samples. In this research, a multiply-mesoporous hydrophilic TiO2 nanohybrid (mM-TiO2@Cys) was synthesized by immobilizing Cys on a TiO2 substrate with hierarchical mesopores to achieve the highly-performed enrichment of N-glycopeptides. With the advantages of superior hydrophilicity and multiply-mesoporous structure, the obtained material exhibited an excellent selectivity (IgG digests and BSA digests at the molar ratio of 1/500), a high sensitivity (1 fmol μL-1 for IgG digests) and a good size-exclusion ability (IgG digests, IgG and BSA at the molar ratio of 1/500/500) in the enrichment of N-glycopeptides from IgG digests. As a result, 281 N-glycopeptides corresponded with 109 glycoproteins were identified from 2 μL serum digests of the patients with nasopharyngeal carcinoma, and 181 N-glycopeptides corresponded with 78 glycoproteins were identified from 2 μL serum digests of the healthy volunteers, revealing the potential application value of mM-TiO2@Cys in glycoproteomics.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

Ameliorating the effects of multiple stresses on agronomic traits in crops: modern biotechnological and omics approaches

While global climate change poses a significant environmental threat to agriculture, the increasing population is another big challenge to food security. To address this, developing crop varieties with increased productivity and tolerance to biotic and abiotic stresses is crucial. Breeders must identify traits to ensure higher and consistent yields under inconsistent environmental challenges, possess resilience against emerging biotic and abiotic stresses and satisfy customer demands for safer and more nutritious meals. With the advent of omics-based technologies, molecular tools are now integrated with breeding to understand the molecular genetics of genotype-based traits and develop better climate-smart crops. The rapid development of omics technologies offers an opportunity to generate novel datasets for crop species. Identifying genes and pathways responsible for significant agronomic traits has been made possible by integrating omics data with genetic and phenotypic information. This paper discusses the importance and use of omics-based strategies, including genomics, transcriptomics, proteomics and phenomics, for agricultural and horticultural crop improvement, which aligns with developing better adaptability in these crop species to the changing climate conditions.