Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Carbon nanoparticles induce DNA repair and PARP inhibitor resistance associated with nanozyme activity in cancer cells

Background

Quantum nanodots especially carbon nanoparticles (CNPs) have been widely studied in biomedicine in imaging, and drug delivery, but anti-cancer mechanisms remain elusive.

Methods

Here, we investigated a type of cell death induced by food (beet, soybean) derived CNPs in cancer cells and tested whether CNPs induced DNA damage and resistant to anti-cancer agent PARP inhibitor (PARPi) could be overcome by quantum calculations, TEM, AFM, FT-IR, soft agar assay, and cytotoxicity assay.

Results

At high doses, CNPs derived from beet lead to a pop-like apoptosis (Carbopoptosis) in cancer cells. Quantum mechanical calculations confirmed CNPs binding with phosphate groups as well as DNA bases. At low doses, CNPs develop PARPi drug resistance through interactions between CNPs and PARPi. A synergistic drug effect was achieved with the combination of phosphatase inhibitor (PPi), PARPi, and CNPs. This is corroborated by the fact that sulfur modulated CNPs which exhibit super high phosphatase nanozyme activity abrogated the CNPs induced colony formation in anchorage-independent cancer cell growth.

Conclusion

Thus, our data suggest the CNPs intrinsic nanozyme activity of phosphatase may crosstalk with drug resistance, which can be reversed upon modulations.

Octreotide may improve pharyngocutaneous fistula healing through downregulation of cystatins: A pilot study

Background

Pharyngocutaneous fistula (PCF) and salivary leaks are well known complications of head and neck surgery. The medical management of PCF has included the use of octreotide without a well-defined understanding of its therapeutic mechanism. We hypothesized that octreotide induces alterations in the saliva proteome and that these alterations may provide insight into the mechanism of action underlying improved PCF healing. We undertook an exploratory pilot study in healthy controls that involved collecting saliva before and after a subcutaneous injection of octreotide and performing proteomic analysis to determine the effects of octreotide.

Methods

Four healthy adult participants provided saliva samples before and after subcutaneous injection of octreotide. A mass-spectrometry based workflow optimized for the quantitative proteomic analysis of biofluids was then employed to analyze changes in salivary protein abundance after octreotide administration.

Results

There were 3076 human, 332 Streptococcus mitis, 102 G. haemolyans, and 42 Granulicatella adiacens protein groups quantified in saliva samples. A paired statistical analysis was performed using the generalized linear model (glm) function in edgeR. There were and ~300 proteins that had a p < .05 between the pre- and post-octreotide groups ~50 proteins with an FDR-corrected p < .05 between pre- and post-groups. These results were visualized using a volcano plot after filtering on proteins quantified by 2 more or unique precursors. Both human and bacterial proteins were among the proteins altered by octreotide treatment. Notably, four isoforms of the human cystatins, belonging to a family of cysteine proteases, that had significantly lower abundance after treatment.

Conclusion

This pilot study demonstrated octreotide-induced downregulation of cystatins. By downregulation of cystatins in the saliva, there is decreased inhibition of cysteine proteases such as Cathepsin S. This results in increased cysteine protease activity that has been linked to enhanced angiogenic response, cell proliferation and migration that have resulted in improved wound healing. These insights provide first steps at furthering our understanding of octreotide's effects on saliva and reports of improved PCF healing.

Mitochondrial dysfunction compromises ciliary homeostasis in astrocytes

Astrocytes, often considered as secondary responders to neurodegeneration, are emerging as primary drivers of brain disease. Here we show that mitochondrial DNA depletion in astrocytes affects their primary cilium, the signaling organelle of a cell. The progressive oxidative phosphorylation deficiency in astrocytes induces FOXJ1 and RFX transcription factors, known as master regulators of motile ciliogenesis. Consequently, a robust gene expression program involving motile cilia components and multiciliated cell differentiation factors are induced. While the affected astrocytes still retain a single cilium, these organelles elongate and become remarkably distorted. The data suggest that chronic activation of the mitochondrial integrated stress response (ISRmt) in astrocytes drives anabolic metabolism and promotes ciliary elongation. Collectively, our evidence indicates that an active signaling axis involving mitochondria and primary cilia exists and that ciliary signaling is part of ISRmt in astrocytes. We propose that metabolic ciliopathy is a novel pathomechanism for mitochondria-related neurodegenerative diseases.

Functional impairment of cortical AMPA receptors in schizophrenia

Clinical and preclinical studies suggest that some of the behavioral alterations observed in schizophrenia (SZ) may be mechanistically linked to synaptic dysfunction of glutamatergic signaling. Recent genetic and proteomic studies suggest alterations of cortical glutamate receptors of the AMPA-type (AMPARs), which are the predominant ligand-gated ionic channels of fast transmission at excitatory synapses. The impact of gene and protein alterations on the electrophysiological activity of AMPARs is not known in SZ. In this proof of principle work, using human postmortem brain synaptic membranes isolated from the dorsolateral prefrontal cortex (DLPFC), we combined electrophysiological analysis from microtransplanted synaptic membranes (MSM) with transcriptomic (RNA-Seq) and label-free proteomics data in 10 control and 10 subjects diagnosed with SZ. We observed in SZ a reduction in the amplitude of AMPARs currents elicited by kainate, an agonist of AMPARs that blocks the desensitization of the receptor. This reduction was not associated with protein abundance but with a reduction in kainate's potency to activate AMPARs. Electrophysiologically-anchored dataset analysis (EDA) was used to identify synaptosomal proteins that linearly correlate with the amplitude of the AMPARs responses, gene ontology functional annotations were then used to determine protein-protein interactions. Protein modules associated with positive AMPARs current increases were downregulated in SZ, while protein modules that were upregulated in SZ were associated with decreased AMPARs currents. Our results indicate that transcriptomic and proteomic alterations, frequently observed in the DLPFC in SZ, converge at the synaptic level producing a functional electrophysiological impairment of AMPARs.

Changes in the urinary proteome before and after quadrivalent influenza vaccine and COVID-19 vaccination

The proteome of urine samples from quadrivalent influenza vaccine cohort were analyzed with self-contrasted method. Significantly changed urine protein at 24 hours after vaccination was enriched in immune-related pathways, although each person’s specific pathways varied. We speculate that this may be because different people have different immunological backgrounds associated with influenza. Then, urine samples were collected from several uninfected SARS-CoV-2 young people before and after the first, second, and third doses of the COVID-19 vaccine. The differential proteins compared between after the second dose (24h) and before the second dose were enriched in pathways involving in multicellular organismal process, regulated exocytosis and immune-related pathways, indicating no first exposure to antigen. Surprisingly, the pathways enriched by the differential urinary protein before and after the first dose were similar to those before and after the second dose. It is inferred that although the volunteers were not infected with SARS-CoV-2, they might have been exposed to other coimmunogenic coronaviruses. Two to four hours after the third vaccination, the differentially expressed protein were also enriched in multicellular organismal process, regulated exocytosis and immune-related pathways, indicating that the immune response has been triggered in a short time after vaccination. Multicellular organismal process and regulated exocytosis after vaccination may be a new indicator to evaluate the immune effect of vaccines. Urinary proteome is a terrific window to monitor the changes in human immune function.

Urinary Proteome Analysis of Global Cerebral Ischemia-Reperfusion Injury Rat Model via Data-Independent Acquisition and Parallel Reaction Monitoring Proteomics

Cerebral ischemia–reperfusion (I/R) injury is the leading cause of death in severe hypotension caused by cardiac arrest, drowning, and excessive blood loss. Urine can sensitively reflect pathophysiological changes in the brain even at an early stage. In this study, a rat model of global cerebral I/R injury was established via Pulsinelli’s four-vessel occlusion (4-VO) method. Overall, 164 urinary proteins significantly changed in the 4-VO rat urine samples compared to the control samples by data-independent acquisition (DIA) proteomics technique (1.5-fold change, p < 0.05). Gene Ontology annotation showed that the acute-phase response, the ERK1 and ERK2 cascade, endopeptidase activity, blood coagulation, and angiogenesis were overrepresented. After parallel reaction monitoring (PRM) validation, 15 differential proteins having human orthologs were verified as the potential urinary markers associated with cerebral I/R injury. Of these potential biomarkers, 8 proteins were reported to be closely associated with cerebral I/R injury. Nine differential proteins changed even when there were no clinical manifestations or histopathological cerebral damage, including FGG, COMP, TFF2, HG2A, KNG1, CATZ, PTGDS, PRVA, and HEPC. These 9 proteins are potential biomarkers for early screening of cerebral I/R injury to prevent the development of cerebral injury. KNG1, CATZ, PTGDS, PRVA, and HEPC showed an overall trend of upregulation or downregulation at 12 and 48 h after I/R injury, reflecting the progression of cerebral I/R injury. These 5 proteins may serve as potential biomarkers for prognostic evaluation of cerebral I/R injury. These findings provide important clues to inform the monitoring of cerebral I/R injury and further the current understanding of its molecular biological mechanisms.

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

BACKGROUND

Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets.

RESULTS

As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods.

CONCLUSIONS

Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided worse results than those obtained with unnormalized data.

Proteome Analysis of Urinary Biomarkers in a Bovine IRBP-Induced Uveitis Rat Model via Data-Independent Acquisition and Parallel Reaction Monitoring Proteomics

Uveitis, a group of intraocular inflammatory diseases, is one of the major causes of severe visual impairment among the working-age population. This study aimed to screen potential urinary biomarkers for uveitis based on proteome analysis. An experimental autoimmune uveitis (EAU) rat model induced by bovine interphotoreceptor retinoid-binding protein (IRBP) was used to mimic uveitis. In discovery phase, a total of 704 urinary proteins were identified via data-independent acquisition (DIA) proteomic technique, of which 76 were significantly changed (34, 36, and 37 on days 5, 8, and 12, respectively, after bovine IRBP immunization). Gene Ontology annotation of the differential proteins indicates that acute-phase response, innate immune response, neutrophil aggregation, and chronic inflammatory response were significantly enriched. Protein-protein interaction network indicates that these differential urinary proteins were biologically connected in EAU, as a group. In validation phase, 17 proteins having human orthologs were verified as the potential markers associated with uveitis by parallel reaction monitoring (PRM) targeted quantitative analysis. Twelve differential proteins changed even when there were no clinical manifestations or histopathological ocular damage. These 12 proteins are potential biomarkers for early diagnosis of uveitis to prevent the development of visual impairment. Five differential proteins changed at three time-points and showed progressive changes as the uveitis progressed, and another five differential proteins changed only on day 12 when EAU severity peaked. These 10 proteins may serve as potential biomarkers for prognostic evaluation of uveitis. Our findings revealed that the urinary proteome could sensitively reflect dynamic pathophysiological changes in EAU, and represent the first step towards the application of urinary protein biomarkers for uveitis.

Urinary Proteomics Analysis of Active Vitiligo Patients: Biomarkers for Steroid Treatment Efficacy Prediction and Monitoring

Vitiligo is a common acquired skin disorder caused by immune-mediated destruction of epidermal melanocytes. Systemic glucocorticoids (GCs) have been used to prevent the progression of active vitiligo, with 8.2–56.2% of patients insensitive to this therapy. Currently, there is a lack of biomarkers that can accurately predict and evaluate treatment responses. The goal of this study was to identify candidate urinary protein biomarkers to predict the efficacy of GCs treatment in active vitiligo patients and monitor the disease. Fifty-eight non-segmental vitiligo patients were enrolled, and 116 urine samples were collected before and after GCs treatment. Patients were classified into a treatment-effective group (n = 42) and a treatment-resistant group (n = 16). Each group was divided equally into age- and sex-matched experimental and validation groups, and proteomic analyses were performed. Differentially expressed proteins were identified, and Ingenuity Pathway Analysis was conducted for the functional annotation of these proteins. Receiver operating characteristic curves were used to evaluate the diagnostic value. A total of 245 and 341 differentially expressed proteins between the treatment-resistant and treatment-effective groups were found before and after GCs treatment, respectively. Bioinformatic analysis revealed that the urinary proteome reflected the efficacy of GCs in active vitiligo patients. Eighty and fifty-four candidate biomarkers for treatment response prediction and treatment response evaluation were validated, respectively. By ELISA analysis, retinol binding protein-1 and torsin 1A interacting protein 1 were validated to have the potential to predict the efficacy of GCs with AUC value of 1 and 0.875, respectively. Retinol binding protein-1, torsin 1A interacting protein 1 and protein disulfide-isomerase A4 were validated to have the potential to reflect positive treatment effect to GCs treatment in active vitiligo with AUC value of 0.861, 1 and 0.868, respectively. This report is the first to identify urine biomarkers for GCs treatment efficacy prediction in vitiligo patients. These findings might contribute to the application of GCs in treating active vitiligo patients.

Proteomics reveals gender differences in physiological characteristics of the gonads and tube feet of the sea cucumber, Apostichopus japonicus

The sea cucumber Apostichopus japonicus is an important aquaculture species in China because of its high nutritional and medicinal values. Gender, as a factor affecting the physiology of organisms, is always considered when improving the breeding efficiency of economically important animals. In the present study, protein expression profiles of the gonads and tube feet of male and female A. japonicus were investigated using a comparative proteomics approach. A set of 7499 proteins were identified, which covered a broad range of functions based on function annotations. A significant difference in protein expression profiles was observed between the gonads and tube feet of A. japonicus; gonads showed more apparent gender differences than tube feet. Moreover, the findings revealed that male A. japonicus had more specific functions and most of these functions were associated with energy consumption. Further analyses suggested that the regulation of ERK activity and the capacity of tyrosine production and virus immunity might be more powerful in male and female A. japonicus, respectively. Some candidate proteins were also recognized as potential targets for gender identification of A. japonicus. Overall, our study provides new insights into the understanding of molecular mechanisms underlying gender-based physiological differences in A. japonicus. SIGNIFICANCE: The current study aimed to reveal gender differences in the physiological characteristics of gonads and tube feet of the sea cucumber A. japonicus. To the best of our knowledge, this is the first proteomics study to analyze the differences in the protein expression profiles of external organs between male and female A. japonicus. The analysis revealed gender differences in the protein expression profiles of both gonads and tube feet of A. japonicus, and the gender differences in gonads were quite apparent. Moreover, according to the recognition of differentially expressed proteins and the enrichment analyses based on Kyoto Encyclopedia of Genes and Genomes, a draft view of how the physiological functions of A. japonicus were affected by gender was obtained. Male A. japonicus could have more specific functions related to energy consumption than females. The regulation of ERK activity and virus immunity might be more robust in male and female A. japonicus, respectively. Some candidate proteins were also recognized as potential targets for gender identification of A. japonicus. The findings presented here will improve the understanding of researchers about the molecular mechanisms underlying gender-based differences in A. japonicus and contribute to the meticulous breeding of A. japonicus.

The deterioration of calcified cartilage integrity reflects the severity of osteoarthritis-A structural, molecular, and biochemical analysis

The calcified cartilage zone (CCZ) is a thin interlayer between the hyaline articular cartilage and the subchondral bone and plays an important role in maintaining the joint homeostasis by providing biological and mechanical support from unmineralized cartilage to the underlying mineralized subchondral bone. The hallmark of CCZ characteristics in osteoarthritis (OA) is less well known. The aim of our study is to evaluate the structural, molecular, and biochemical composition of CCZ in tissues affected by primary knee OA and its relationship with disease severity. We collected osteochondral tissue samples stratified according to disease severity, from 16 knee OA patients who underwent knee replacement surgery. We also used meniscectomy-induced rat samples to confirm the pathophysiologic changes of human samples. We defined the characteristics of the calcified cartilage layer using a combination of morphological, biochemical, proteomic analyses on laser micro-dissected tissue. Our results demonstrated that the Calcium/Phosphate ratio is unchanged during the OA progression, but the calcium-binding protein and cadherin binding protein, as well as carbohydrate metabolism-related proteins, undergo significant changes. These changes were further accompanied by thinning of the CCZ, loss of collagen and proteoglycan content, the occurrence of the endochondral ossification, neovasculature, loss of the elastic module, loss of the collagen direction, and increase of the tortuosity indicating an altered structural and mechanical properties of the CCZ in OA. In conclusion, our results suggest that the calcified cartilage changes can reflect the disease progression.

Comparison of Microflow and Analytical Flow Liquid Chromatography Coupled to Mass Spectrometry Global Metabolomics Methods Using a Urea Cycle Disorder Mouse Model

Microscale-based separations are increasingly being applied in the field of metabolomics for the analysis of small-molecule metabolites. These methods have the potential to provide improved sensitivity, less solvent waste, and reduced sample-size requirements. Ion-pair free microflow-based global metabolomics methods, which we recently reported, were further compared to analytical flow ion-pairing reagent containing methods using a sample set from a urea cycle disorder (UCD) mouse model. Mouse urine and brain homogenate samples representing healthy, diseased, and disease-treated animals were analyzed by both methods. Data processing was performed using univariate and multivariate techniques followed by analyte trend analysis. The microflow methods performed comparably to the analytical flow ion-pairing methods with the ability to separate the three sample groups when analyzed by partial least-squares analysis. The number of detected metabolic features present after each data processing step was similar between the microflow-based methods and the ion-pairing methods in the negative ionization mode. The observed analyte trend and coverage of known UCD biomarkers were the same for both evaluated approaches. The 12.5-fold reduction in sample injection volume required for the microflow-based separations highlights the potential of this method to support studies with sample-size limitations.

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics─currently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.

Dynamic urine proteome changes in a rat model of simvastatin-induced skeletal muscle injury

Statin-associated muscle symptoms (SAMS) are the main side effects of statins. Currently, there are no effective biomarkers for accurate clinical diagnosis. Urine is not subject to homeostatic control and therefore accumulates early changes, making it an ideal biomarker source. We therefore examined urine proteome changes associated with SAMS. Here, we established a SAMS rat model by intragastric intubation with simvastatin (80 mg/kg). Biochemical analyses and hematoxylin and eosin staining were used to evaluate the degree of muscle injury. The urine proteome on days 3, 6, 9 and 14 was profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Differential proteins on day 14 of SAMS were mainly associated with glycolysis/gluconeogenesis, pyruvate metabolism, metabolism of reactive oxygen species and apoptosis, which were associated with the pathological mechanism of SAMS. Among the 14 differential proteins on day 3, Fibrinogen gamma chain (FIBG), Osteopontin (OSTP) and C-reactive protein (CRP) were associated with muscle damage, while EH domain-containing protein 1(EHD1), Cubilin (CUBN) and Fibronectin (FINC) were associated with the pathogenic mechanisms of SAMS. Our preliminary results indicated that the urine proteome can reflect early changes in the SAMS rat model, providing the potential for monitoring drug side effects in future clinical research. SIGNIFICANCE: This study demonstrate that the early muscle damage caused by simvastatin can be reflected in urinary proteins. The urine proteome also has the potential to reflect the pharmacology and toxicology of drugs in future clinical research.

A cross-species spatiotemporal proteomic analysis identifies UBE3A-dependent signaling pathways and targets

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of neuronal E3 ligase UBE3A. Restoring UBE3A levels is a potential disease-modifying therapy for AS and has recently entered clinical trials. There is paucity of data regarding the molecular changes downstream of UBE3A hampering elucidation of disease therapeutics and biomarkers. Notably, UBE3A plays an important role in the nucleus but its targets have yet to be elucidated. Using proteomics, we assessed changes during postnatal cortical development in an AS mouse model. Pathway analysis revealed dysregulation of proteasomal and tRNA synthetase pathways at all postnatal brain developmental stages, while synaptic proteins were altered in adults. We confirmed pathway alterations in an adult AS rat model across multiple brain regions and highlighted region-specific differences. UBE3A reinstatement in AS model mice resulted in near complete and partial rescue of the proteome alterations in adolescence and adults, respectively, supporting the notion that restoration of UBE3A expression provides a promising therapeutic option. We show that the nuclear enriched transketolase (TKT), one of the most abundantly altered proteins, is a novel direct UBE3A substrate and is elevated in the neuronal nucleus of rat brains and human iPSC-derived neurons. Taken together, our study provides a comprehensive map of UBE3A-driven proteome remodeling in AS across development and species, and corroborates an early UBE3A reinstatement as a viable therapeutic option. To support future disease and biomarker research, we present an accessible large-scale multi-species proteomic resource for the AS community ( https://www.angelman-proteome-project.org/ ).

Atlas of RNA editing events affecting protein expression in aged and Alzheimer's disease human brain tissue

RNA editing is a feature of RNA maturation resulting in the formation of transcripts whose sequence differs from the genome template. Brain RNA editing may be altered in Alzheimer's disease (AD). Here, we analyzed data from 1,865 brain samples covering 9 brain regions from 1,074 unrelated subjects on a transcriptome-wide scale to identify inter-regional differences in RNA editing. We expand the list of known brain editing events by identifying 58,761 previously unreported events. We note that only a small proportion of these editing events are found at the protein level in our proteome-wide validation effort. We also identified the occurrence of editing events associated with AD dementia, neuropathological measures and longitudinal cognitive decline in: SYT11, MCUR1, SOD2, ORAI2, HSDL2, PFKP, and GPRC5B. Thus, we present an extended reference set of brain RNA editing events, identify a subset that are found to be expressed at the protein level, and extend the narrative of transcriptomic perturbation in AD to RNA editing.

Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma

BACKGROUND

Scleroderma is a serious chronic autoimmune disease in which a patient's disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients.

METHODS

We use a Bayesian mixed model approach to simultaneously characterize each individual's future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model.

RESULTS

The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual's risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient's visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR).

CONCLUSIONS

This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.

Cromolyn platform suppresses fibrosis and inflammation, promotes microglial phagocytosis and neurite outgrowth

Neurodegenerative diseases are characterized by chronic neuroinflammation and may perpetuate ongoing fibrotic reactions within the central nervous system. Unfortunately, there is no therapeutic available that treats neurodegenerative inflammation and its sequelae. Here we utilize cromolyn, a mast cell inhibitor with anti-inflammatory capabilities, and its fluorinated analogue F-cromolyn to study fibrosis-related protein regulation and secretion downstream of neuroinflammation and their ability to promote microglial phagocytosis and neurite outgrowth. In this report, RNA-seq analysis shows that administration of the pro-inflammatory cytokine TNF-α to HMC3 human microglia results in a robust upregulation of fibrosis-associated genes. Subsequent treatment with cromolyn and F-cromolyn resulted in reduced secretion of collagen XVIII, fibronectin, and tenascin-c. Additionally, we show that cromolyn and F-cromolyn reduce pro-inflammatory proteins PLP1, PELP1, HSP90, IL-2, GRO-α, Eotaxin, and VEGF-Α, while promoting secretion of anti-inflammatory IL-4 in HMC3 microglia. Furthermore, cromolyn and F-cromolyn augment neurite outgrowth in PC12 neuronal cells in concert with nerve growth factor. Treatment also differentially altered secretion of neurogenesis-related proteins TTL, PROX1, Rab35, and CSDE1 in HMC3 microglia. Finally, iPSC-derived human microglia more readily phagocytose Aβ42 with cromolyn and F-cromolyn relative to controls. We propose the cromolyn platform targets multiple proteins upstream of PI3K/Akt/mTOR, NF-κB, and GSK-3β signaling pathways to affect cytokine, chemokine, and fibrosis-related protein expression.

Looking deeper into ocular surface health: an introduction to clinical tear proteomics analysis

Ocular surface diseases are becoming more prevalent worldwide. Reasons for this include the ongoing population ageing and increasing use of digital displays, although ophthalmologists have a wide selection of tools, which can be implemented in the evaluation of the ocular surface health, methods, which enable the in-depth study of biological functions are gaining more interest. These new approaches are needed, since the individual responses to ocular surface diseases and treatments can vary from person to person, and the correlations between clinical signs and symptoms are often low. Modern mass spectrometry (MS) methods can produce information on hundreds of tear proteins, which in turn can provide valuable information on the biological effects occurring on the ocular surface. In this review article, we will provide an overview of the different aspects, which are part of a successful tear proteomics study design and equip readers with a better understanding of the methods most suited for their MS-based tear proteomics study in the field of ophthalmology and ocular surface.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Proteomic signatures of myeloid derived suppressor cells from liver and lung metastases reveal functional divergence and potential therapeutic targets

Myeloid-derived suppressor cells (MDSCs) promote immunosuppressive activities in the tumor microenvironment (TME), resulting in increased tumor burden and diminishing the anti-tumor response of immunotherapies. While primary and metastatic tumors are typically the focal points of therapeutic development, the immune cells of the TME are differentially programmed by the tissue of the metastatic site. In particular, MDSCs are programmed uniquely within different organs in the context of tumor progression. Given that MDSC plasticity is shaped by the surrounding environment, the proteomes of MDSCs from different metastatic sites are hypothesized to be unique. A bottom-up proteomics approach using sequential window acquisition of all theoretical mass spectra (SWATH-MS) was used to quantify the proteome of CD11b⁺ cells derived from murine liver metastases (LM) and lung metastases (LuM). A comparative proteomics workflow was employed to compare MDSC proteins from LuM (LuM-MDSC) and LM (LM-MDSC) while also elucidating common signaling pathways, protein function, and possible drug-protein interactions. SWATH-MS identified 2516 proteins from 200 µg of sample. Of the 2516 proteins, 2367 have matching transcriptomic data. Upregulated proteins from lung and liver-derived murine CD11b⁺ cells with matching mRNA transcriptomic data were categorized based on target knowledge and level of drug development. Comparative proteomic analysis demonstrates that liver and lung tumor-derived MDSCs have distinct proteomes that may be subject to pharmacologic manipulation.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Nonlinear effects of environmental salinity on the gill transcriptome versus proteome of Oreochromis niloticus modulate epithelial cell turnover

A data-independent acquisition (DIA) assay library for targeted quantitation of thousands of Oreochromis niloticus gill proteins using a label- and gel-free workflow was generated and used to compare protein and mRNA abundances. This approach generated complimentary rather than redundant data for 1899 unique genes in gills of tilapia acclimated to freshwater and brackish water. Functional enrichment analyses identified mitochondrial energy metabolism, serine protease and immunity-related functions, and cytoskeleton/ extracellular matrix organization as major processes controlled by salinity in O. niloticus gills. Non-linearity in salinity-dependent transcriptome versus proteome regulation was revealed for specific functional groups of genes. The relationship was more linear for other molecular functions/ cellular processes, suggesting that the salinity-dependent regulation of O. niloticus gill function relies on post-transcriptional mechanisms for some functions/ processes more than others. This integrative systems biology approach can be adopted for other tissues and organisms to study cellular dynamics for many changing ecological contexts.

Compensating Complete Loss of Signal Recognition Particle During Co-translational Protein Targeting by the Translation Speed and Accuracy

Signal recognition particle (SRP) is critical for delivering co-translational proteins to the bacterial inner membrane. Previously, we identified SRP suppressors in Escherichia coli that inhibit translation initiation and elongation, which provided insights into the mechanism of bypassing the requirement of SRP. Suppressor mutations tended to be located in regions that govern protein translation under evolutionary pressure. To test this hypothesis, we re-executed the suppressor screening of SRP. Here, we isolated a novel SRP suppressor mutation located in the Shine–Dalgarno sequence of the S10 operon, which partially offset the targeting defects of SRP-dependent proteins. We found that the suppressor mutation decreased the protein translation rate, which extended the time window of protein targeting. This increased the possibility of the correct localization of inner membrane proteins. Furthermore, the fidelity of translation was decreased in suppressor cells, suggesting that the quality control of translation was inactivated to provide an advantage in tolerating toxicity caused by the loss of SRP. Our results demonstrated that the inefficient protein targeting due to SRP deletion can be rescued through modulating translational speed and accuracy.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data

MOTIVATION

Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.

RESULTS

To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples.

AVAILABILITYAND IMPLEMENTATION

https://github.com/mwgrassgreen/RobNorm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses

RATIONALE

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data.

METHODS

We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide the selection of appropriate normalization approaches for two different FT-ICR-MS data sets. Furthermore, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses.

RESULTS

The normalization approach identified by SPANS differed for the two data sets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that total sum scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches.

CONCLUSIONS

Although there is no single, best normalization method for all data sets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. The number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Genetic inactivation of RIP1 kinase activity in rats protects against ischemic brain injury

RIP1 kinase-mediated inflammatory and cell death pathways have been implicated in the pathology of acute and chronic disorders of the nervous system. Here, we describe a novel animal model of RIP1 kinase deficiency, generated by knock-in of the kinase-inactivating RIP1(D138N) mutation in rats. Homozygous RIP1 kinase-dead (KD) rats had normal development, reproduction and did not show any gross phenotypes at baseline. However, cells derived from RIP1 KD rats displayed resistance to necroptotic cell death. In addition, RIP1 KD rats were resistant to TNF-induced systemic shock. We studied the utility of RIP1 KD rats for neurological disorders by testing the efficacy of the genetic inactivation in the transient middle cerebral artery occlusion/reperfusion model of brain injury. RIP1 KD rats were protected in this model in a battery of behavioral, imaging, and histopathological endpoints. In addition, RIP1 KD rats had reduced inflammation and accumulation of neuronal injury biomarkers. Unbiased proteomics in the plasma identified additional changes that were ameliorated by RIP1 genetic inactivation. Together these data highlight the utility of the RIP1 KD rats for target validation and biomarker studies for neurological disorders.

Integration of Proteomics and Metabolomics Into the Design, Build, Test, Learn Cycle to Improve 3-Hydroxypropionic Acid Production in Aspergillus pseudoterreus

Biological engineering of microorganisms to produce value-added chemicals is a promising route to sustainable manufacturing. However, overproduction of metabolic intermediates at high titer, rate, and yield from inexpensive substrates is challenging in non-model systems where limited information is available regarding metabolic flux and its control in production conditions. Integrated multi-omic analyses of engineered strains offers an in-depth look at metabolites and proteins directly involved in growth and production of target and non-target bioproducts. Here we applied multi-omic analyses to overproduction of the polymer precursor 3-hydroxypropionic acid (3HP) in the filamentous fungus Aspergillus pseudoterreus. A synthetic pathway consisting of aspartate decarboxylase, beta-alanine pyruvate transaminase, and 3HP dehydrogenase was designed and built for A. pseudoterreus. Strains with single- and multi-copy integration events were isolated and multi-omics analysis consisting of intracellular and extracellular metabolomics and targeted and global proteomics was used to interrogate the strains in shake-flask and bioreactor conditions. Production of a variety of co-products (organic acids and glycerol) and oxidative degradation of 3HP were identified as metabolic pathways competing with 3HP production. Intracellular accumulation of nitrogen as 2,4-diaminobutanoate was identified as an off-target nitrogen sink that may also limit flux through the engineered 3HP pathway. Elimination of the high-expression oxidative 3HP degradation pathway by deletion of a putative malonate semialdehyde dehydrogenase improved the yield of 3HP by 3.4 × after 10 days in shake-flask culture. This is the first report of 3HP production in a filamentous fungus amenable to industrial scale biomanufacturing of organic acids at high titer and low pH.

Urine proteome changes in a chronic unpredictable mild stress (CUMS) mouse model of major depressive disorder

Major depressive disorder (MDD) is a prevalent complex psychiatric disorder, and there are no effective biomarkers for clinical diagnosis. Urine is not subjected to homeostatic control, allowing it to reflect the sensitive changes that occur in various diseases. In this study, we examined the urine proteome changes in a chronic unpredictable mild stress mouse model of MDD. Male C57BL/6 mice were subjected to chronic unpredictable mild stress for 5 weeks. The tail suspension test and sucrose consumption test were then applied to evaluate depression-like behaviors. The urine proteomes on day 0 and day 36 in the CUMS group were profiled by liquid chromatography coupled with tandem mass spectrometry (LCMS/MS). A total of 36 differential proteins were identified, 19 of which have been associated with the pathogenic mechanisms of MDD. There was an average of two differential proteins that were identified through 1,048,574 random combination statistical analyses, indicating that at least 95 % of the differential proteins were reliable. The differential proteins were mainly associated with blood coagulation, inflammatory responses and central nervous system development. Our preliminary results indicated that the urine proteome can reflect changes associated with MDD in the CUMS model, which provides potential clues for the diagnosis of MDD.

Discovery and Pharmacological Evaluation of STEAP4 as a Novel Target for HER2 Overexpressing Breast Cancer

Breast cancer (BC) is a highly heterogeneous disease encompassing multiple subtypes with different molecular and histopathological features, disease prognosis, and therapeutic responses. Among these, the Triple Negative BC form (TNBC) is an aggressive subtype with poor prognosis and therapeutic outcome. With respect to HER2 overexpressing BC, although advanced targeted therapies have improved the survival of patients, disease relapse and metastasis remains a challenge for therapeutic efficacy. In this study the aim was to identify key membrane-associated proteins which are overexpressed in these aggressive BC subtypes and can serve as potential biomarkers or drug targets. We leveraged on the development of a membrane enrichment protocol in combination with the global profiling GeLC-MS/MS technique, and compared the proteomic profiles of a HER2 overexpressing (HCC-1954) and a TNBC (MDA-MB-231) cell line with that of a benign control breast cell line (MCF-10A). An average of 2300 proteins were identified from each cell line, of which approximately 600 were membrane-associated proteins. Our global proteomic methodology in tandem with invigoration by Western blot and Immunofluorescence analysis, readily detected several previously-established BC receptors like HER2 and EPHA2, but importantly STEAP4 and CD97 emerged as novel potential candidate markers. This is the first time that the mitochondrial iron reductase STEAP4 protein up-regulation is linked to BC (HER2+ subtype), while for CD97, its role in BC has been previously described, but never before by a global proteomic technology in TNBC. STEAP4 was selected for further detailed evaluation by the employment of Immunohistochemical analysis of BC xenografts and clinical tissue microarray studies. Results showed that STEAP4 expression was evident only in malignant breast tissues whereas all the benign breast cases had no detectable levels. A functional role of STEAP4 intervention was established in HER2 overexpressing BC by pharmacological studies, where blockage of the STEAP4 pathway with an iron chelator (Deferiprone) in combination with the HER2 inhibitor Lapatinib led to a significant reduction in cell growth in vitro. Furthermore, siRNA mediated knockdown of STEAP4 also suppressed cell proliferation and enhanced the inhibition of Lapatinib in HER2 overexpressing BC, confirming its potential oncogenic role in BC. In conclusion, STEAP4 may represent a novel BC related biomarker and a potential pharmacological target for the treatment of HER2 overexpressing BC.

Proteomic analysis of urine reveals biomarkers for the diagnosis and phenotyping of abdominal-type Henoch-Schonlein purpura

BACKGROUND

Abdominal-type Henoch-Schonlein purpura (HSP) is a common refractory disease in children. Currently, no specific diagnostic biomarker is available for HSP.

METHODS

Children with abdominal type HSP were first diagnosed with three syndromes using Chinese traditional medicine. The urinary proteomes among the three syndromes of patients with abdominal type HSP and healthy controls were compared using two label-free proteomics quantifications, including data-dependent acquisition and data-independent acquisition.

RESULTS

For the comparison between patients with abdominal type HSP and healthy children, a total of 75 differential urinary proteins were identified by determining the overlap of the two experiments. The ingenuity pathway analysis (IPA) analysis showed that these differential proteins were correlated with the pathogenesis of abdominal type HSP. Of these, 37 proteins were distributed in 13 solid tissues as tissue-enriched proteins. Monitoring changes in these proteins might help us detect uncommon clinical manifestations of HSP. Patients with abdominal type HSP can be further distinguished into three syndromes based on the urine proteome. Finally, a panel of six urinary proteins (P25774, P09417, Q7Z5L0, P60900, P14550 and P09668) was constructed for both the diagnosis and phenotyping of abdominal type HSP.

CONCLUSIONS

Urinary protein biomarkers for the diagnosis and phenotyping of abdominal type HSP were identified, which will contribute to the personalized treatment of patients with abdominal type HSP.

Ceramide synthase 2 deletion decreases the infectivity of HIV-1

The lipid composition of HIV-1 virions is enriched in sphingomyelin (SM), but the roles that SM or other sphingolipids (SLs) might play in the HIV-1 replication pathway have not been elucidated. In human cells, SL levels are regulated by ceramide synthase (CerS) enzymes that produce ceramides, which can be converted to SMs, hexosylceramides, and other SLs. In many cell types, CerS2, which catalyzes the synthesis of very long chain ceramides, is the major CerS. We have examined how CerS2 deficiency affects the assembly and infectivity of HIV-1. As expected, we observed that very long chain ceramide, hexosylceramide, and SM were reduced in CerS2 knockout cells. CerS2 deficiency did not affect HIV-1 assembly or the incorporation of the HIV-1 envelope (Env) protein into virus particles, but it reduced the infectivites of viruses produced in the CerS2-deficient cells. The reduced viral infection levels were dependent on HIV-1 Env, since HIV-1 particles that were pseudotyped with the vesicular stomatitis virus glycoprotein did not exhibit reductions in infectivity. Moreover, cell-cell fusion assays demonstrated that the functional defect of HIV-1 Env in CerS2-deficient cells was independent of other viral proteins. Overall, our results indicate that the altered lipid composition of CerS2-deficient cells specifically inhibit the HIV-1 Env receptor binding and/or fusion processes.

Untargeted Ultrahigh-Performance Liquid Chromatography-Hybrid Quadrupole-Orbitrap Mass Spectrometry (UHPLC-HRMS) Metabolomics Reveals Propolis Markers of Greek and Chinese Origin

Chemical composition of propolis depends on the plant source and thus on the geographic and climatic characteristics of the site of collection. The aim of this study was to investigate the chemical profile of Greek and Chinese propolis extracts from different regions and suggest similarities and differences between them. Untargeted ultrahigh-performance liquid chromatography coupled to hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-HRMS) method was developed and 22 and 23 propolis samples from Greece and China, respectively, were analyzed. The experimental data led to the observation that there is considerable variability in terms of quality of the distinctive propolis samples. Partial least squares - discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed and allowed the identification of significant features for sample discrimination, adding relevant information for the identification of class-determining metabolites. Chinese samples overexpressed compounds that are characteristic of the poplar type propolis, whereas Greek samples overexpress the latter and the diterpenes characteristic of the Mediterranean propolis type.

Signal Recognition Particle Suppressor Screening Reveals the Regulation of Membrane Protein Targeting by the Translation Rate

The signal recognition particle (SRP) is conserved in all living organisms, and it cotranslationally delivers proteins to the inner membrane or endoplasmic reticulum. Recently, SRP loss was found not to be lethal in either the eukaryote Saccharomyces cerevisiae or the prokaryote Streptococcus mutans In Escherichia coli, the role of SRP in mediating inner membrane protein (IMP) targeting has long been studied. However, the essentiality of SRP remains a controversial topic, partly hindered by the lack of strains in which SRP is completely absent. Here we show that the SRP was nonessential in E. coli by suppressor screening. We identified two classes of extragenic suppressors-two translation initiation factors and a ribosomal protein-all of which are involved in translation initiation. The translation rate and inner membrane proteomic analyses were combined to define the mechanism that compensates for the lack of SRP. The primary factor that contributes to the efficiency of IMP targeting is the extension of the time window for targeting by pausing the initiation of translation, which further reduces translation initiation and elongation rates. Furthermore, we found that easily predictable features in the nascent chain determine the specificity of protein targeting. Our results show why the loss of the SRP pathway does not lead to lethality. We report a new paradigm in which the time delay in translation initiation is beneficial during protein targeting in the absence of SRP.IMPORTANCE Inner membrane proteins (IMPs) are cotranslationally inserted into the inner membrane or endoplasmic reticulum by the signal recognition particle (SRP). Generally, the deletion of SRP can result in protein targeting defects in Escherichia coli Suppressor screening for loss of SRP reveals that pausing at the translation start site is likely to be critical in allowing IMP targeting and avoiding aggregation. In this work, we found for the first time that SRP is nonessential in E. coli The time delay in initiation is different from the previous mechanism that only slows down the elongation rate. It not only maximizes the opportunity for untranslated ribosomes to be near the inner membrane but also extends the time window for targeting translating ribosomes by decreasing the speed of translation. We anticipate that our work will be a starting point for a more delicate regulatory mechanism of protein targeting.

Label-free proteomics for discovering biomarker candidates of RAD140 administration to castrated horses

Selective androgen receptor (AR) modulators (SARMs) are potent anabolic agents with a high potential of misuse in horseracing and equestrian sports. In this study, we applied label-free proteomics to discover plasma protein biomarkers in geldings (castrated horses) after administration with a popular SARM named RAD140. Tryptic peptides were prepared from plasma samples and analyzed by nano-flow ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (nano-UHPLC-HRMS/MS) using data-independent acquisition (DIA) method. Orthogonal projection on latent structure-discriminant analysis (OPLS-DA) has led to the development of a predictive model that could discriminate RAD140-administered samples from control samples and could also correctly classify 18 out of 19 in-training horses as control samples. The model comprises 75 proteins with variable importance in projection (VIP) score above 1. Gene Ontology (GO) enrichment analysis and literature review have identified upregulation of AR-regulated clusterin, and proteins associated with inflammation (haptoglobin, cluster of differentiation 14 [CD14], and inter-alpha-trypsin inhibitor heavy chain 4 [ITIH4]) and erythropoiesis (glycosylphosphatidylinositol-specific phospholipase D1 [GPLD1]) after RAD140 administration. Their changes were confirmed by selected reaction monitoring (SRM) experiments. Similar effects have been reported by the use of androgens and other SARMs. This is the first reported study that describes the use of a proteomic biomarker approach to detect horses that have been administered with RAD140 by applying label-free proteomic profiling of plasma samples. These results support the concept of a biomarker-driven approach to enhance the doping control of RAD140 and potentially other SARMs in the future.

Mining Protein Expression Databases Using Network Meta-Analysis

Public databases featuring original, raw data from "Omics" experiments enable researchers to perform meta-analyses by combining either the raw data or the summarized results of several independent studies. In proteomics, high-throughput protein expression data is measured by diverse techniques such as mass spectrometry, 2-D gel electrophoresis or protein arrays yielding data of different scales. Therefore, direct data merging can be problematic, and combining the summarized data of the individual studies can be advantageous. A special form of meta-analysis is network meta-analysis, where studies with different settings of experimental groups can be combined. However, all studies must be linked by one experimental group that has to appear in each study. Usually that is the control group. Then, a study network is formed and indirect statistical inferences can also be made between study groups that appear not in each of the studies.In this chapter, we describe the working principle of and available software for network meta-analysis. The applicability to high-throughput protein expression data is demonstrated in an example from breast cancer research. We also describe the special challenges when applying this method.

High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.

Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling

The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is, however, still difficult to comprehensively access. The high complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC-MS/MS) or a highly sensitive targeted immunoassay such as the proximity extension assay (PEA). To address relevant differences and important shared characteristics, we tested the performance of LC-MS/MS in the data-dependent and data-independent acquisition modes and Olink PEA to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC-MS/MS approaches applied, mainly including high-abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/mL concentrations. Then, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation, and gender-based differential expression. Our work highlights the limitations and the advantages of both targeted and untargeted approaches and proves their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by a combination of platforms-a promising approach for future biomarker and mechanistic studies.

Activity-Based Protein Profiling of Chitin Catabolism

The microbial catabolism of chitin, an abundant and ubiquitous environmental organic polymer, is a fundamental cog in terrestrial and aquatic carbon and nitrogen cycles. Despite the importance of this critical bio-geochemical function, there is a limited understanding of the synergy between the various hydrolytic and accessory enzymes involved in chitin catabolism. To address this deficit, we synthesized activity-based probes (ABPs) designed to target active chitinolytic enzymes by modifying the chitin subunits N-acetyl glucosamine and chitotriose. The ABPs were used to determine the active complement of chitinolytic enzymes produced over time by the soil bacterium Cellvibrio japonicus treated with various C substrates. We demonstrate the utility of these ABPs in determining the synergy between various enzymes involved in chitin catabolism. The strategy can be used to gain molecular-level insights that can be used to better understand microbial roles in soil bio-geochemical cycling in the face of a changing climate.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

Blood plasma is one of the most widely used samples for cancer biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we have described an optimized, integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (iTRAQ and TMT) plasma proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only ~2 days to complete, including the time for data acquisition (excluding database searching). This protocol is quick, flexible, and eliminates the need for a separate immunoassay-based validation workflow in blood cancer biomarker investigations. We anticipate that this plasma proteomics workflow will help to accelerate the cancer biomarker discovery program and provide a valuable resource to the cancer research community.

Induction of ER Stress by an AAV5 BDD FVIII Construct Is Dependent on the Strength of the Hepatic-Specific Promoter

Adeno-associated virus 5 (AAV5)-human factor VIII-SQ (hFVIII-SQ; valoctocogene roxaparvovec) is an AAV-mediated product under evaluation for treatment of severe hemophilia A, which contains a B-domain-deleted hFVIII (hFVIII-SQ) transgene and a hybrid liver-specific promotor (HLP). To increase FVIII-SQ expression and reduce the vector dose required, a stronger promoter may be considered. However, because FVIII-SQ is a protein known to be difficult to fold and secrete, this could potentially induce endoplasmic reticulum (ER) stress. We evaluated the effect of two AAV5-hFVIII-SQ vectors with different liver-specific promoter strength (HLP << 100ATGB) on hepatic ER stress in mice. Five weeks after receiving vehicle or vector, the percentage of transduced hepatocytes and levels of liver hFVIII-SQ DNA and RNA increased dose dependently for both vectors. At lower doses, plasma hFVIII-SQ protein levels were higher for 100ATGB. This difference was attenuated at the highest dose. For 100ATGB, liver hFVIII-SQ protein accumulated dose dependently, with increased expression of ER stress markers at the highest dose, suggesting hepatocytes reached or exceeded their capacity to fold/secrete hFVIII-SQ. These data suggest that weaker promoters may require relatively higher doses to distribute expression load across a greater number of hepatocytes, whereas relatively stronger promoters may produce comparable levels of FVIII in fewer hepatocytes, with potential for ER stress.

A global lipid map defines a network essential for Zika virus replication

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.

Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is an unexplained chronic, debilitating illness characterized by fatigue, sleep disturbances, cognitive dysfunction, orthostatic intolerance and gastrointestinal problems. Using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), we analyzed the plasma proteomes of 39 ME/CFS patients and 41 healthy controls. Logistic regression models, with both linear and quadratic terms of the protein levels as independent variables, revealed a significant association between ME/CFS and the immunoglobulin heavy variable (IGHV) region 3-23/30. Stratifying the ME/CFS group based on self-reported irritable bowel syndrome (sr-IBS) status revealed a significant quadratic effect of immunoglobulin lambda constant region 7 on its association with ME/CFS with sr-IBS whilst IGHV3-23/30 and immunoglobulin kappa variable region 3-11 were significantly associated with ME/CFS without sr-IBS. In addition, we were able to predict ME/CFS status with a high degree of accuracy (AUC = 0.774-0.838) using a panel of proteins selected by 3 different machine learning algorithms: Lasso, Random Forests, and XGBoost. These algorithms also identified proteomic profiles that predicted the status of ME/CFS patients with sr-IBS (AUC = 0.806-0.846) and ME/CFS without sr-IBS (AUC = 0.754-0.780). Our findings are consistent with a significant association of ME/CFS with immune dysregulation and highlight the potential use of the plasma proteome as a source of biomarkers for disease.

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography - ElectroSpray Ionization - Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization - Furier Transform Ion Cyclotron Resonance - Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the 'Probabilistic Quotient' method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements.