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

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Targeting glutamine metabolism improves sarcoma response to radiation therapy in vivo

Diverse tumor metabolic phenotypes are influenced by the environment and genetic lesions. Whether these phenotypes extend to rhabdomyosarcoma (RMS) and how they might be leveraged to design new therapeutic approaches remains an open question. Thus, we utilized a Pax7Cre-ER-T2/+; NrasLSL-G12D/+; p53fl/fl (P7NP) murine model of sarcoma with mutations that most frequently occur in human embryonal RMS. To study metabolism, we infuse 13C-labeled glucose or glutamine into mice with sarcomas and show that sarcomas consume more glucose and glutamine than healthy muscle tissue. However, we reveal a marked shift from glucose consumption to glutamine metabolism after radiation therapy (RT). In addition, we show that inhibiting glutamine, either through genetic deletion of glutaminase (Gls1) or through pharmacological inhibition of glutaminase, leads to significant radiosensitization in vivo. This causes a significant increase in overall survival for mice with Gls1-deficient compared to Gls1-proficient sarcomas. Finally, Gls1-deficient sarcomas post-RT elevate levels of proteins involved in natural killer cell and interferon alpha/gamma responses, suggesting a possible role of innate immunity in the radiosensitization of Gls1-deficient sarcomas. Thus, our results indicate that glutamine contributes to radiation response in a mouse model of RMS.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

A multi-omic survey of black cottonwood tissues highlights coordinated transcriptomic and metabolomic mechanisms for plant adaptation to phosphorus deficiency

Introduction

Phosphorus (P) deficiency in plants creates a variety of metabolic perturbations that decrease photosynthesis and growth. Phosphorus deficiency is especially challenging for the production of bioenergy feedstock plantation species, such as poplars (Populus spp.), where fertilization may not be practically or economically feasible. While the phenotypic effects of P deficiency are well known, the molecular mechanisms underlying whole-plant and tissue-specific responses to P deficiency, and in particular the responses of commercially valuable hardwoods, are less studied.

Methods

We used a multi-tissue and multi-omics approach using transcriptomic, proteomic, and metabolomic analyses of the leaves and roots of black cottonwood (Populus trichocarpa) seedlings grown under P-deficient (5 µM P) and replete (100 µM P) conditions to assess this knowledge gap and to identify potential gene targets for selection for P efficiency.

Results

In comparison to seedlings grown at 100 µM P, P-deficient seedlings exhibited reduced dry biomass, altered chlorophyll fluorescence, and reduced tissue P concentrations. In line with these observations, growth, C metabolism, and photosynthesis pathways were downregulated in the transcriptome of the P-deficient plants. Additionally, we found evidence of strong lipid remodeling in the leaves. Metabolomic data showed that the roots of P-deficient plants had a greater relative abundance of phosphate ion, which may reflect extensive degradation of P-rich metabolites in plants exposed to long-term P-deficiency. With the notable exception of the KEGG pathway for Starch and Sucrose Metabolism (map00500), the responses of the transcriptome and the metabolome to P deficiency were consistent with one another. No significant changes in the proteome were detected in response to P deficiency.

Discussion and conclusion

Collectively, our multi-omic and multi-tissue approach enabled the identification of important metabolic and regulatory pathways regulated across tissues at the molecular level that will be important avenues to further evaluate for P efficiency. These included stress-mediating systems associated with reactive oxygen species maintenance, lipid remodeling within tissues, and systems involved in P scavenging from the rhizosphere.

Alteration of LARGE1 abundance in patients and a mouse model of 5q-associated spinal muscular atrophy

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by recessive pathogenic variants affecting the survival of motor neuron (SMN1) gene (localized on 5q). In consequence, cells lack expression of the corresponding protein. This pathophysiological condition is clinically associated with motor neuron (MN) degeneration leading to severe muscular atrophy. Additionally, vulnerability of other cellular populations and tissues including skeletal muscle has been demonstrated. Although the therapeutic options for SMA have considerably changed, treatment responses may differ thus underlining the persistent need for validated biomarkers. To address this need and to identify novel marker proteins for SMA, we performed unbiased proteomic profiling on cerebrospinal fluid derived (CSF) from genetically proven SMA type 1-3 cases and afterwards performed ELISA studies on CSF and serum samples to validate the potential of a novel biomarker candidates in both body fluids. To further decipher the pathophysiological impact of this biomarker, immunofluorescence studies were carried out on spinal cord and skeletal muscle derived from a 5q-SMA mouse model. Proteomics revealed increase of LARGE1 in CSF derived from adult patients showing a clinical response upon treatment with nusinersen. Moreover, LARGE1 levels were validated in CSF samples of further SMA patients (type 1-3) by ELISA. These studies also unveiled a distinguishment between groups in improvement of motor skills: adult patients do present with lowered level per se at baseline visit while no elevation upon treatment in the pediatric cohort can be observed. ELISA-based studies of serum samples showed no changes in the pediatric cohort but unraveled elevated level in adult patients responding to future intervention with nusinersen, while non-responders did not show a significant increase. Additional immunofluorescence studies of LARGE1 in MN and skeletal muscle of a SMA type 3 mouse model revealed an increase of LARGE1 during disease progression. Our combined data unraveled LARGE1 as a protein dysregulated in serum and CSF of SMA-patients (and in MN and skeletal muscle of SMA mice) holding the potential to serve as a disease marker for SMA and enabling to differentiate between patients responding and non-responding to therapy with nusinersen.

Nontargeted Plasma Proteomic Analysis of Renal Disease and Pulmonary Hypertension in Patients with Sickle Cell Disease

Sickle cell disease (SCD) is characterized by red blood cell sickling, vaso-occlusion, hemolytic anemia, damage to multiple organ systems, and, as a result, shortened life expectancy. Sickle cell disease nephropathy (SCDN) and pulmonary hypertension (pHTN) are common and frequently co-occurring complications of SCD; both are associated with markedly accelerated mortality. To identify candidate circulating biomarkers of SCDN and pHTN, we used mass spectrometry to quantify the relative abundance of >1000 proteins in plasma samples from 189 adults with SCD from the Outcome Modifying Genes in SCD (OMG-SCD) cohort (ProteomeXchange identifier PXD048716). Forty-four proteins were differentially abundant in SCDN, most significantly cystatin-C and collagen α-1(XVIII) chain (COIA1), and 55 proteins were dysregulated in patients with SCDN and pHTN, most significantly insulin-like growth factor-binding protein 6 (IBP6). Network analysis identified a module of 133 coregulated proteins significantly associated with SCDN, that was enriched for extracellular matrix proteins, insulin-like growth factor binding proteins, cell adhesion proteins, EGF-like calcium binding proteins, and several cadherin family members. Collectively, these data provide a comprehensive understanding of plasma protein changes in SCDN and pHTN which validate numerous studies of chronic kidney disease and suggest shared profiles of protein disruption in kidney dysfunction and pHTN among SCD patients.

Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells

Tiragolumab, an anti-TIGIT antibody with an active IgG1κ Fc, demonstrated improved outcomes in the phase 2 CITYSCAPE trial (ClinicalTrials.gov: NCT03563716 ) when combined with atezolizumab (anti-PD-L1) versus atezolizumab alone1. However, there remains little consensus on the mechanism(s) of response with this combination2. Here we find that a high baseline of intratumoural macrophages and regulatory T cells is associated with better outcomes in patients treated with atezolizumab plus tiragolumab but not with atezolizumab alone. Serum sample analysis revealed that macrophage activation is associated with a clinical benefit in patients who received the combination treatment. In mouse tumour models, tiragolumab surrogate antibodies inflamed tumour-associated macrophages, monocytes and dendritic cells through Fcγ receptors (FcγR), in turn driving anti-tumour CD8+ T cells from an exhausted effector-like state to a more memory-like state. These results reveal a mechanism of action through which TIGIT checkpoint inhibitors can remodel immunosuppressive tumour microenvironments, and suggest that FcγR engagement is an important consideration in anti-TIGIT antibody development.

Engineering the carbon and redox metabolism of Paenibacillus polymyxa for efficient isobutanol production

Paenibacillus polymyxa is a non-pathogenic, Gram-positive bacterium endowed with a rich and versatile metabolism. However interesting, this bacterium has been seldom used for bioproduction thus far. In this study, we engineered P. polymyxa for isobutanol production, a relevant bulk chemical and next-generation biofuel. A CRISPR-Cas9-based genome editing tool facilitated the chromosomal integration of a synthetic operon to establish isobutanol production. The 2,3-butanediol biosynthesis pathway, leading to the main fermentation product of P. polymyxa, was eliminated. A mutant strain harbouring the synthetic isobutanol operon (kdcA from Lactococcus lactis, and the native ilvC, ilvD and adh genes) produced 1 g L-1 isobutanol under microaerobic conditions. Improving NADPH regeneration by overexpression of the malic enzyme subsequently increased the product titre by 50%. Network-wide proteomics provided insights into responses of P. polymyxa to isobutanol and revealed a significant metabolic shift caused by alcohol production. Glucose-6-phosphate 1-dehydrogenase, the key enzyme in the pentose phosphate pathway, was identified as a bottleneck that hindered efficient NADPH regeneration through this pathway. Furthermore, we conducted culture optimization towards cultivating P. polymyxa in a synthetic minimal medium. We identified biotin (B7), pantothenate (B5) and folate (B9) to be mutual essential vitamins for P. polymyxa. Our rational metabolic engineering of P. polymyxa for the production of a heterologous chemical sheds light on the metabolism of this bacterium towards further biotechnological exploitation.

MYC targeting by OMO-103 in solid tumors: a phase 1 trial

Among the 'most wanted' targets in cancer therapy is the oncogene MYC, which coordinates key transcriptional programs in tumor development and maintenance. It has, however, long been considered undruggable. OMO-103 is a MYC inhibitor consisting of a 91-amino acid miniprotein. Here we present results from a phase 1 study of OMO-103 in advanced solid tumors, established to examine safety and tolerability as primary outcomes and pharmacokinetics, recommended phase 2 dose and preliminary signs of activity as secondary ones. A classical 3 + 3 design was used for dose escalation of weekly intravenous, single-agent OMO-103 administration in 21-day cycles, encompassing six dose levels (DLs). A total of 22 patients were enrolled, with treatment maintained until disease progression. The most common adverse events were grade 1 infusion-related reactions, occurring in ten patients. One dose-limiting toxicity occurred at DL5. Pharmacokinetics showed nonlinearity, with tissue saturation signs at DL5 and a terminal half-life in serum of 40 h. Of the 19 patients evaluable for response, 12 reached the predefined 9-week time point for assessment of drug antitumor activity, eight of those showing stable disease by computed tomography. One patient defined as stable disease by response evaluation criteria in solid tumors showed a 49% reduction in total tumor volume at best response. Transcriptomic analysis supported target engagement in tumor biopsies. In addition, we identified soluble factors that are potential pharmacodynamic and predictive response markers. Based on all these data, the recommended phase 2 dose was determined as DL5 (6.48 mg kg-1).ClinicalTrials.gov identifier: NCT04808362 .

Effects of lysine deacetylase inhibitor treatment on LPS responses of alveolar-like macrophages

Macrophages are key immune cells that can adapt their metabolic phenotype in response to different stimuli. Lysine deacetylases are important enzymes regulating inflammatory gene expression and lysine deacetylase inhibitors have been shown to exert anti-inflammatory effects in models of chronic obstructive pulmonary disease. We hypothesized that these anti-inflammatory effects may be associated with metabolic changes in macrophages. To validate this hypothesis, we used an unbiased and a targeted proteomic approach to investigate metabolic enzymes, as well as liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry, to quantify metabolites in combination with the measurement of functional parameters in primary murine alveolar-like macrophages after lipopolysaccharide-induced activation in the presence or absence of lysine deacetylase inhibition. We found that lysine deacetylase inhibition resulted in reduced production of inflammatory mediators such as tumor necrosis factor α and interleukin 1β. However, only minor changes in macrophage metabolism were observed, as only one of the lysine deacetylase inhibitors slightly increased mitochondrial respiration while no changes in metabolite levels were seen. However, lysine deacetylase inhibition specifically enhanced expression of proteins involved in ubiquitination, which may be a driver of the anti-inflammatory effects of lysine deacetylase inhibitors. Our data illustrate that a multiomics approach provides novel insights into how macrophages interact with cues from their environment. More detailed studies investigating ubiquitination as a potential driver of lysine deacetylase inhibition will help developing novel anti-inflammatory drugs for difficult-to-treat diseases such as chronic obstructive pulmonary disease.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

Secretomics reveals hormone-therapy of breast cancer may induce survival by facilitating hypercoagulation and immunomodulation in vitro

Tumour cell haematogenous dissemination is predicated on molecular changes that enhance their capacity for invasion and preparation of the pre-metastatic niche. It is increasingly evident that platelets play an essential role in this transformation. The systemic nature of signalling molecules and extravascular factors that participate in mediating platelet-tumour cell interactions led to the development of an in vitro co-culture using whole blood and breast tumour cells, allowing us to decipher the impact of hormone-therapy on tumour cells and associated changes in the plasma proteome. Using mass spectrometry, we determined dysregulation of proteins associated with maintaining an invasive tumour phenotype. Tumour changes in genes associated with EMT and survival were documented. This is postulated to be induced via tumour cell interactions with the coagulatory and immune systems. Results highlight tumour cell adaptability to both treatment and blood resulting in a pro-tumorigenic response and a hypercoagulatory state. We illustrate that the breast cancer cell secretome can be altered by hormone-therapy, subject to the tumour subphenotype and linked to platelet activation. More sophisticated co-culture systems are required to recapitulate these interactions to better understand tumorigenesis. Moreover, deeper plasma profiling, using abundant protein depleted and/or vesicle enriched strategies, will likely reveal additional secretory proteins related to tumour cell-platelet interactions.

Normalization methods in mass spectrometry-based analytical proteomics: A case study based on renal cell carcinoma datasets

Normalization is a crucial step in proteomics data analysis as it enables data adjustment and enhances comparability between datasets by minimizing multiple sources of variability, such as sampling, sample handling, storage, treatment, and mass spectrometry measurements. In this study, we investigated different normalization methods, including Z-score normalization, median divide normalization, and quantile normalization, to evaluate their performance using a case study based on renal cell carcinoma datasets. Our results demonstrate that when comparing datasets by pairs, both the Z-score and quantile normalization methods consistently provide better results in terms of the number of proteins identified and quantified as well as in identifying statistically significant up or down-regulated proteins. However, when three or more datasets are compared at the same time the differences are found to be negligible.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma

Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs.

SIGNIFICANCE

The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.

Elovanoid-N34 modulates TXNRD1 key in protection against oxidative stress-related diseases

The thioredoxin (TXN) system is an NADPH + H+/FAD redox-triggered effector that sustains homeostasis, bioenergetics, detoxifying drug networks, and cell survival in oxidative stress-related diseases. Elovanoid (ELV)-N34 is an endogenously formed lipid mediator in neural cells from omega-3 fatty acid precursors that modulate neuroinflammation and senescence gene programming when reduction-oxidation (redox) homeostasis is disrupted, enhancing cell survival. Limited proteolysis (LiP) screening of human retinal pigment epithelial (RPE) cells identified TXNRD1 isoforms 2, 3, or 5, the reductase of the TXN system, as an intracellular target of ELV-N34. TXNRD1 silencing confirmed that the ELV-N34 target was isoform 2 or 3. This lipid mediator induces TXNRD1 structure changes that modify the FAD interface domain, leading to its activity modulation. The addition of ELV-N34 decreased membrane and cytosolic TXNRD1 activity, suggesting localizations for the targeted reductase. These results show for the first time that the lipid mediator ELV-N34 directly modulates TXNRD1 activity, underling its protection in several pathologies when uncompensated oxidative stress (UOS) evolves.

Enhancing bacterial fitness and recombinant enzyme yield by engineering the quality control protease HtrA of Bacillus subtilis

IMPORTANCE

In the expanding market of recombinant proteins, microbial cell factories such as Bacillus subtilis are key players. Microbial cell factories experience secretion stress during high-level production of secreted proteins, which can negatively impact product yield and cell viability. The CssRS two-component system and CssRS-regulated quality control proteases HtrA and HtrB play critical roles in the secretion stress response. HtrA has a presumptive dual function in protein quality control by exerting both chaperone-like and protease activities. However, its potential role as a chaperone has not been explored in B. subtilis. Here, we describe for the first time the beneficial effects of proteolytically inactive HtrA on α-amylase yields and overall bacterial fitness.

Ventricular CSF proteomic profiles and predictors of surgical treatment outcome in chronic hydrocephalus

BACKGROUND

By applying an unbiased proteomic approach, we aimed to search for cerebrospinal fluid (CSF) protein biomarkers distinguishing between obstructive and communicating hydrocephalus in order to improve appropriate surgical selection for endoscopic third ventriculostomy vs. shunt implants. Our second study purpose was to look for potential CSF biomarkers distinguishing between patients with adult chronic hydrocephalus benefitting from surgery (responders) vs. those who did not (non-responders).

METHODS

Ventricular CSF samples were collected from 62 patients with communicating hydrocephalus and 28 patients with obstructive hydrocephalus. CSF was collected in relation to the patients' surgical treatment. As a control group, CSF was collected from ten patients with unruptured aneurysm undergoing preventive surgery (vascular clipping).

RESULTS

Mass spectrometry-based proteomic analysis of the samples identified 1251 unique proteins. No proteins differed significantly between the communicating hydrocephalus group and the obstructive hydrocephalus group. Four proteins were found to be significantly less abundant in CSF from communicating hydrocephalus patients compared to control subjects. A PCA plot revealed similar proteomic CSF profiles of obstructive and communicating hydrocephalus and control samples. For obstructive hydrocephalus, ten proteins were found to predict responders from non-responders.

CONCLUSION

Here, we show that the proteomic profile of ventricular CSF from patients with hydrocephalus differs slightly from control subjects. Furthermore, we find ten predictors of response to surgical outcome (endoscopic third ventriculostomy or ventriculo-peritoneal shunt) in patients with obstructive hydrocephalus.

An enhanced label-free proteomics approach for deep-diving into equine plasma proteome, including the discovery of protein biomarkers for strenuous exercise

Plasma proteins have been a valuable source of biomarkers for clinical uses and for monitoring of the illicit use of prohibited substances or practices in equine sports. We have previously reported the first use of label-free proteomics in profiling equine plasma proteome. This study aimed to refine the method by systematically evaluating various plasma fractionation methods and the use of narrower precursor mass ranges in data-independent acquisition (DIA) mass spectrometry (MS). Tandem fractionations of equine plasma with octanoic acid precipitation followed by solid-phase extraction (SPE) with C4 cartridges provided the largest increase in the number of new proteins identified. The use of two narrow precursor mass ranges of m/z 400-600 and 600-800 in DIA not only identified most proteins detectable by using a single mass range of m/z 350-1500 but also identified ~27% more proteins. The improved method was applied to analyse the plasma proteome of 'postrace' samples which, unlike other samples, had been collected from racehorses soon after racing. Multivariate data analysis has identified upregulation of 14 proteins and downregulation of six proteins in postrace plasma compared with the non-postrace plasma samples. Literature review of these proteins has provided evidence of exercise-induced haemolysis and changes in antioxidant enzyme activities, kinin system, insulin signalling and energy metabolism after strenuous exercise. The improved method has enabled a deeper profiling of the equine plasma proteome and identified the proteins associated with normal physiological changes after racing which are potential confounding factors in the development of a biomarker approach for doping control.

A systematic scientometric review of paternal inheritance of acquired metabolic traits

BACKGROUND

The concept of the inheritance of acquired traits, a foundational principle of Lamarck's evolutionary theory, has garnered renewed attention in recent years. Evidence for this phenomenon remained limited for decades but gained prominence with the Överkalix cohort study in 2002. This study revealed a link between cardiovascular disease incidence and the food availability experienced by individuals' grandparents during their slow growth periods, reigniting interest in the inheritance of acquired traits, particularly in the context of non-communicable diseases. This scientometric analysis and systematic review comprehensively explores the current landscape of paternally transmitted acquired metabolic traits.

RESULTS

Utilizing Scopus Advanced search and meticulous screening, we included mammalian studies that document the inheritance or modification of metabolic traits in subsequent generations of unexposed descendants. Our inclusive criteria encompass intergenerational and transgenerational studies, as well as multigenerational exposures. Predominantly, this field has been driven by a select group of researchers, potentially shaping the design and focus of existing studies. Consequently, the literature primarily comprises transgenerational rodent investigations into the effects of ancestral exposure to environmental pollutants on sperm DNA methylation. The complexity and volume of data often lead to multiple or redundant publications. This practice, while understandable, may obscure the true extent of the impact of ancestral exposures on the health of non-exposed descendants. In addition to DNA methylation, studies have illuminated the role of sperm RNAs and histone marks in paternally acquired metabolic disorders, expanding our understanding of the mechanisms underlying epigenetic inheritance.

CONCLUSIONS

This review serves as a comprehensive resource, shedding light on the current state of research in this critical area of science, and underscores the need for continued exploration to uncover the full spectrum of paternally mediated metabolic inheritance.

Identification of a specific exporter that enables high production of aconitic acid in Aspergillus pseudoterreus

Aconitic acid is an unsaturated tricarboxylic acid that is attractive for its potential use in manufacturing biodegradable and biocompatible polymers, plasticizers, and surfactants. Previously Aspergillus pseudoterreus was engineered as a platform to produce aconitic acid by deleting the cadA (cis-aconitic acid decarboxylase) gene in the itaconic acid biosynthetic pathway. In this study, the aconitic acid transporter gene (aexA) was identified using comparative global discovery proteomics analysis between the wild-type and cadA deletion strains. The protein AexA belongs to the Major Facilitator Superfamily (MFS). Deletion of aexA almost abolished aconitic acid secretion, while its overexpression led to a significant increase in aconitic acid production. Transportation of aconitic acid across the plasma membrane is a key limiting step in its production. In vitro, proteoliposome transport assay further validated AexA's function and substrate specificity. This research provides new approaches to efficiently pinpoint and characterize exporters of fungal organic acids and accelerate metabolic engineering to improve secretion capability and lower the cost of bioproduction.

Non-invasive diagnosis of early cutaneous squamous cell carcinoma

Early cutaneous squamous cell carcinoma (cSCC) can be challenging to diagnose using clinical criteria as it could present similar to actinic keratosis (AK) or Bowen's disease (BD), precursors of cSCC. Currently, histopathological assessment of an invasive biopsy is the gold standard for diagnosis. A non-invasive diagnostic approach would reduce patient and health system burden. Therefore, this study used non-invasive sampling by tape-stripping coupled with data-independent acquisition mass spectrometry (DIA-MS) proteomics to profile the proteome of histopathologically diagnosed AK, BD and cSCC, as well as matched normal samples. Proteomic data were analysed to identify proteins and biological functions that are significantly different between lesions. Additionally, a support vector machine (SVM) machine learning algorithm was used to assess the usefulness of proteomic data for the early diagnosis of cSCC. A total of 696 proteins were identified across the samples studied. A machine learning model constructed using the proteomic data classified premalignant (AK + BD) and malignant (cSCC) lesions at 77.5% accuracy. Differential abundance analysis identified 144 and 21 protein groups that were significantly changed in the cSCC, and BD samples compared to the normal skin, respectively (adj. p < 0.05). Changes in pivotal carcinogenic pathways such as LXR/RXR activation, production of reactive oxygen species, and Hippo signalling were observed that may explain the progression of cSCC from premalignant lesions. In summary, this study demonstrates that DIA-MS analysis of tape-stripped samples can identify non-invasive protein biomarkers with the potential to be developed into a complementary diagnostic tool for early cSCC.

Transcriptomic and Proteomic Changes Driving Pulmonary Fibrosis Resolution in Young and Old Mice

Bleomycin-induced pulmonary fibrosis in mice mimics major hallmarks of idiopathic pulmonary fibrosis. Yet in this model, it spontaneously resolves over time. We studied molecular mechanisms of fibrosis resolution and lung repair, focusing on transcriptional and proteomic signatures and the effect of aging. Old mice showed incomplete and delayed lung function recovery 8 weeks after bleomycin instillation. This shift in structural and functional repair in old bleomycin-treated mice was reflected in a temporal shift in gene and protein expression. We reveal gene signatures and signaling pathways that underpin the lung repair process. Importantly, the downregulation of WNT, BMP, and TGFβ antagonists Frzb, Sfrp1, Dkk2, Grem1, Fst, Fstl1, and Inhba correlated with lung function improvement. Those genes constitute a network with functions in stem cell pathways, wound, and pulmonary healing. We suggest that insufficient and delayed downregulation of those antagonists during fibrosis resolution in old mice explains the impaired regenerative outcome. Together, we identified signaling pathway molecules with relevance to lung regeneration that should be tested in-depth experimentally as potential therapeutic targets for pulmonary fibrosis.

The atrial and ventricular myocardial proteome of end-stage lamin heart disease

Lamins A/C (encoded by LMNA gene) can lead to dilated cardiomyopathy (DCM). This pilot study sought to explore the postgenomic phenotype of end-stage lamin heart disease. Consecutive patients with end-stage lamin heart disease (LMNA-group, n = 7) and ischaemic DCM (ICM-group, n = 7) undergoing heart transplantation were prospectively enrolled. Samples were obtained from left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV) and interventricular septum (IVS), avoiding the infarcted myocardial segments in the ICM-group. Samples were analysed using a discovery 'shotgun' proteomics approach. We found that 990 proteins were differentially abundant between LMNA and ICM samples with the LA being most perturbed (16-fold more than the LV). Abundance of lamin A/C protein was reduced, but lamin B increased in LMNA LA/RA tissue compared to ICM, but not in LV/RV. Carbonic anhydrase 3 (CA3) was over-abundant across all LMNA tissue samples (LA, LV, RA, RV, and IVS) when compared to ICM. Transthyretin was more abundant in the LV/RV of LMNA compared to ICM, while sarcomeric proteins such as titin and cardiac alpha-cardiac myosin heavy chain were generally less abundant in RA/LA of LMNA. Protein expression profiling and enrichment analysis pointed towards sarcopenia, extracellular matrix remodeling, deficient myocardial energetics, redox imbalances, and abnormal calcium handling in LMNA samples. Compared to ICM, end-stage lamin heart disease is a biventricular but especially a biatrial disease appearing to have an abundance of lamin B, CA3 and transthyretin, potentially hinting to compensatory responses.

High-quality Cymbidium mannii genome and multifaceted regulation of crassulacean acid metabolism in epiphytes

Epiphytes with crassulacean acid metabolism (CAM) photosynthesis are widespread among vascular plants, and repeated evolution of CAM photosynthesis is a key innovation for micro-ecosystem adaptation. However, we lack a complete understanding of the molecular regulation of CAM photosynthesis in epiphytes. Here, we report a high-quality chromosome-level genome assembly of a CAM epiphyte, Cymbidium mannii (Orchidaceae). The 2.88-Gb orchid genome with a contig N50 of 22.7 Mb and 27 192 annotated genes was organized into 20 pseudochromosomes, 82.8% of which consisted of repetitive elements. Recent expansions of long terminal repeat retrotransposon families have made a major contribution to the evolution of genome size in Cymbidium orchids. We reveal a holistic scenario of molecular regulation of metabolic physiology using high-resolution transcriptomics, proteomics, and metabolomics data collected across a CAM diel cycle. Patterns of rhythmically oscillating metabolites, especially CAM-related products, reveal circadian rhythmicity in metabolite accumulation in epiphytes. Genome-wide analysis of transcript and protein level regulation revealed phase shifts during the multifaceted regulation of circadian metabolism. Notably, we observed diurnal expression of several core CAM genes (especially βCA and PPC) that may be involved in temporal fixation of carbon sources. Our study provides a valuable resource for investigating post-transcription and translation scenarios in C. mannii, an Orchidaceae model for understanding the evolution of innovative traits in epiphytes.

Functional mass spectrometry imaging maps phospholipase-A2 enzyme activity during osteoarthritis progression

Background: Enzymes are central components of many physiological processes, and changes in enzyme activity are linked to numerous disease states, including osteoarthritis (OA). Assessing changes in enzyme function can be challenging because of difficulties in separating affected tissue areas that result in the homogenisation of healthy and diseased cells. Direct correlation between spatially-resolved enzyme distribution(s) and diseased cells/tissues can thus lead to advances in our understanding of OA pathophysiology. Herein, we present a method that uses mass spectrometry imaging (MSI) to visualise the distribution of lipase enzymes and their downstream lipid products in fresh bone and cartilage tissue sections. Immunohistostaining of adjacent tissue sections was then used to identify OA cells/tissues, which were then statistically correlated with molecular-level images. Methods: MSI was used to image lipase enzymes, their substrates, and their metabolic products to validate enzymatic activity and correlate to OA regions determined by immunohistochemistry (IHC). Based on the modified Mankin score, six non-OA and OA patient-matched osteochondral samples were analysed by matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). Due to the involvement of phospholipase A2 (PLA2) in inflammatory pathways, explant tissues were treated with IL-1β to mimic inflammation observed in OA. Bovine explant tissues were then subject to MSI methods to observe the spatial distribution of PLA2. Results: Compared with non-OA samples, OA samples showed an elevated level of multiple arachidonic acid (AA)-containing phospholipids (P < 0.001), in which the elevation in the surface and deep layer cartilage of OA tissues is correlated to elevated PLA2 activity (P < 0.001). Bovine explant tissues treated with IL-1β to mimic OA pathophysiology validated these results and displayed elevated PLA2 levels in OA mimic samples relative to the controls (P < 0.001). It was established that the PLA2G2A isoform specifically was responsible for PLA2 enzyme activity changes in OA tissues (P < 0.001). Conclusion: Our results present a reliable method for imaging enzyme dynamics in OA cartilage, which sets up the foundation for future spatial enzyme dynamics in the OA field. We demonstrated that OA patients exhibit increased expression of PLA2G2A at the superficial and deep cartilage zone that degrades cartilage differently at the spatial level. A tissue-specific PLA2G2A precision inhibition may be the potential target for OA.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

Characterizing the urinary proteome of prematurity-associated lung disease in school-aged children

INTRODUCTION

Although different phenotypes of lung disease after preterm birth have recently been described, the underlying mechanisms associated with each phenotype are poorly understood. We, therefore, compared the urinary proteome for different spirometry phenotypes in preterm-born children with preterm- and term-born controls.

METHODS

Preterm and term-born children aged 7-12 years, from the Respiratory Health Outcomes in Neonates (RHiNO) cohort, underwent spirometry and urine collection. Urine was analysed by Nano-LC Mass-Spectrometry with Tandem-Mass Tag labelling. The preterm-born children were classified into phenotypes of prematurity-associated preserved ratio impaired spirometry (pPRISm, FEV₁ < lower limit of normal (LLN), FEV₁/FVC ≥ LLN), prematurity-associated obstructive lung disease (POLD, FEV₁ < LLN, FEV₁/FVC < LLN) and preterm controls (FEV₁ ≥ LLN,). Biological relationships between significantly altered protein abundances were analysed using Ingenuity Pathways Analysis software, and receiver operator characteristic curves were calculated.

RESULTS

Urine was analysed from 160 preterm-born children and 44 term controls. 27 and 21 were classified into the pPRISm and POLD groups, respectively. A total of 785 proteins were detected. Compared to preterm-born controls, sixteen significantly altered proteins in the pPRISm group were linked to six biological processes related to upregulation of inflammation and T-cell biology. In contrast, four significantly altered proteins in the POLD group were linked with neutrophil accumulation. Four proteins (DNASE1, PGLYRP1, B2M, SERPINA3) in combination had an area under the curve of 0.73 for pPRISm and three combined proteins (S100A8, MMP9 and CTSC) had AUC of 0.76 for POLD.

CONCLUSIONS

In this exploratory study, we demonstrate differential associations of the urinary proteome with pPRISm and POLD.

TRIAL REGISTRATION

EudraCT: 2015-003712-20.

Proteomic analysis of tears in dry eye disease: A prospective, double-blind multicenter study

PURPOSE

To identify specific dry eye disease (DED) tear biomarker(s) using tear proteomic analysis, clinical parameters, and their correlations before and after DED treatment.

METHODS

A prospective, double-blinded, national multicenter clinical study was performed using data from 80 DED patients. The patients were treated with 0.1% cyclosporine (CsA, n = 28), 0.05% CsA (n = 26), or 3% diquafosol (DQS, n = 26) eye drops, and tear proteome changes and clinical outcomes (tear break-up time [TBUT], corneal erosion [Cor-Er], conjunctival erosion [Conj-Er], and symptom assessment in dry eye [SANDE] scores) were observed at 4, 8, and 12 weeks. For all clinical parameters, correlation analysis was performed between the three drug conditions and the differentially expressed proteins (DEPs) from the proteomic analysis.

RESULTS

AFM, ALCAM, CFB, H1-4, PON1, RAP1B, and RBP4 were identified in all treatment groups and were downregulated after treatment. All clinical parameters significantly improved at 12 weeks than at baseline (p-value <0.0001); however, their values were not significantly different among groups, except for Cor-Er (p-value = 0.007). Compared with the DQS group, Cor-Er score significantly improved after treatment with 0.1% and 0.05% CsA. The seven DEPs identified in all groups were not consistently correlated with the clinical parameters (p-value >0.05).

CONCLUSIONS

Despite differences in drug concentration and action mechanisms, the improvement levels of TBUT, Cor-Er, and SANDE scores were clinically adequate. However, useful tear protein biomarkers, clinically acceptable biomarker combinations correlating with clinical parameters, and clinically acceptable levels of specificity and sensitivity were not identified.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

The Introduction of Detergents in Thermal Proteome Profiling Requires Lowering the Applied Temperatures for Efficient Target Protein Identification

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.

Proteomic profiling of cutaneous melanoma explains the aggressiveness of distant organ metastasis

Despite recent developments in managing metastatic melanomas, patients' overall survival remains low. Therefore, the current study aims to understand better the proteome-wide changes associated with melanoma metastasis that will assist with identifying targeted therapies. The latest development in mass spectrometry-based proteomics, together with extensive bioinformatics analysis, was used to investigate the molecular changes in 60 formalin-fixed and paraffin-embedded samples of primary and lymph nodes (LN) and distant organ metastatic melanomas. A total of 4631 proteins were identified, of which 72 and 453 were significantly changed between the LN and distant organ metastatic melanomas compared to the primary lesions (adj. p-value <0.05). An increase in proteins such as SLC9A3R1, CD20 and GRB2 and a decrease in CST6, SERPINB5 and ARG1 were associated with regional LN metastasis. By contrast, increased metastatic activities in distant organ metastatic melanomas were related to higher levels of CEACAM1, MC1R, AKT1 and MMP3-9 and decreased levels of CDKN2A, SDC1 and SDC4 proteins. Furthermore, machine learning analysis classified the lesions with up to 92% accuracy based on their metastatic status. The findings from this study provide up to date proteome-level information about the progression of melanomas to regional LN and distant organs, leading to the identification of protein signatures with potential for clinical translation.

Normalization of organ-on-a-Chip samples for mass spectrometry based proteomics and metabolomics via Dansylation-based assay

Mass spectrometry based 'omics pairs well with organ-on-a-chip-based investigations, which often have limited cellular material for sampling. However, a common issue with these chip-based platforms is well-to-well or chip-to-chip variability in the proteome and metabolome due to factors such as plate edge effects, cellular asynchronization, effluent flow, and limited cell count. This causes high variability in the quantitative multi-omics analysis of samples, potentially masking true biological changes within the system. Solutions to this have been approached via data processing tools and post-acquisition normalization strategies such as constant median, constant sum, and overall signal normalization. Unfortunately, these methods do not adequately correct for the large variations, resulting in a need for increased biological replicates. The methods in this work utilize a dansylation based assay with a subset of labeled metabolites that allow for pre-acquisition normalization to better correlate the biological perturbations that truly occur in chip-based platforms. BCA protein assays were performed in tandem with a proteomics pipeline to achieve pre-acquisition normalization. The CN Bio PhysioMimix was seeded with primary hepatocytes and challenged with VX after six days of culture, and the metabolome and proteome were analyzed using the described normalization methods. A decreased coefficient of variation percentage is achieved, significant changes are observed through the proteome and metabolome, and better classification of biological replicates acquired because of these strategies.

Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas

Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.

Effects of GHR Deficiency and Juvenile Hypoglycemia on Immune Cells of a Porcine Model for Laron Syndrome

Laron syndrome (LS) is a rare genetic disorder characterized by low levels of insulin-like growth factor 1 (IGF1) and high levels of growth hormone (GH) due to mutations in the growth hormone receptor gene (GHR). A GHR-knockout (GHR-KO) pig was developed as a model for LS, which displays many of the same features as humans with LS-like transient juvenile hypoglycemia. This study aimed to investigate the effects of impaired GHR signaling on immune functions and immunometabolism in GHR-KO pigs. GHR are located on various cell types of the immune system. Therefore, we investigated lymphocyte subsets, proliferative and respiratory capacity of peripheral blood mononuclear cells (PBMCs), proteome profiles of CD4− and CD4+ lymphocytes and IFN-α serum levels between wild-type (WT) controls and GHR-KO pigs, which revealed significant differences in the relative proportion of the CD4+CD8α− subpopulation and in IFN-α levels. We detected no significant difference in the respiratory capacity and the capacity for polyclonal stimulation in PBMCs between the two groups. But proteome analysis of CD4+ and CD4− lymphocyte populations revealed multiple significant protein abundance differences between GHR-KO and WT pigs, involving pathways related to amino acid metabolism, beta-oxidation of fatty acids, insulin secretion signaling, and oxidative phosphorylation. This study highlights the potential use of GHR-KO pigs as a model for studying the effects of impaired GHR signaling on immune functions.

Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome

Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.

Changes of development from childhood to late adulthood in rats tracked by urinary proteome

To date, studies of development have mainly focused on the embryonic stage and a short time thereafter. There has been little research on the whole life of an individual from childhood to aging and death. For the first time, we used non-invasive urinary proteome technology to track changes in several important developmental timepoints in a group of rats, covering 10 timepoints from childhood, adolescence, young adulthood, middle adulthood, and near death in old age. Similar to previous studies on puberty, proteins were detected involved in sexual or reproductive maturation, mature spermatozoa in seminiferous tubules (first seen), gonadal hormones, decline of oestradiol, brain growth, and central nervous system myelination, and our differential protein enrichment pathways also included reproductive system development, tube development, response to hormone, response to oestradiol, brain development, and neuron development. Similar to previous studies in young adults, proteins were detected involved in musculoskeletal maturity, peak bone mass, development of the immune system, and growth and physical development, and our differential protein enrichment pathways also included skeletal system development, bone regeneration, system development, immune system processes, myeloid leukocyte differentiation, growth, and developmental growth. Studies on aging-related changes in neurons and neurogenesis have been reported, and we also found relevant pathways in aged rats, such as regulation of neuronal synaptic plasticity and positive regulation of long-term neuronal synaptic plasticity. However, at all timepoints throughout life, there were many biological pathways revealed by differential urinary protein enrichment involving multiple organs, tissues, systems, etc., that have not been mentioned in existing studies. This study shows comprehensive and detailed changes in rat lifetime development through the urinary proteome, helping to fill the gap in development research. Moreover, it provides a new approach to monitoring changes in human health and diseases of aging using the urinary proteome.

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

BACKGROUND

Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.

RESULTS

Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.

CONCLUSION

Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User's guide of MetaboDirect are freely available through ( https://github.com/Coayala/MetaboDirect ) and ( https://metabodirect.readthedocs.io/en/latest/ ), respectively. Video Abstract.

Differential proteomic profile of lumbar and ventricular cerebrospinal fluid

BACKGROUND

Pathological cerebral conditions may manifest in altered composition of the cerebrospinal fluid (CSF). Although diagnostic CSF analysis seeks to establish pathological disturbances in the brain proper, CSF is generally sampled from the lumbar compartment for reasons of technical ease and ethical considerations. We here aimed to compare the molecular composition of CSF obtained from the ventricular versus the lumbar CSF compartments to establish a relevance for employing lumbar CSF as a proxy for the CSF bathing the brain tissue.

METHODS

CSF was collected from 46 patients with idiopathic normal pressure hydrocephalus (iNPH) patients during their diagnostic workup (lumbar samples) and in connection with their subsequent CSF diversion shunt surgery (ventricular samples). The mass-spectrometry-based proteomic profile was determined in these samples and in addition, selected biomarkers were quantified with ELISA (S100B, neurofilament light (NfL), amyloid-β (Aβ₄₀, Aβ₄₂), and total tau (T-tau) and phosphorylated tau (P-tau) forms). The latter analysis was extended to include paired porcine samples obtained from the lumbar compartment and the cerebromedullary cistern closely related to the ventricles.

RESULTS

In total 1231 proteins were detected in the human CSF. Of these, 216 distributed equally in the two CSF compartments, whereas 22 were preferentially (or solely) present in the ventricular CSF and four in the lumbar CSF. The selected biomarkers of neurodegeneration and Alzheimer's disease displayed differential distribution, some with higher (S100B, T-tau, and P-tau) and some with lower (NfL, Aβ₄₀, Aβ₄₂) levels in the ventricular compartment. In the porcine samples, all biomarkers were most abundant in the lumbar CSF.

CONCLUSIONS

The overall proteomic profile differs between the ventricular and the lumbar CSF compartments, and so does the distribution of clinically employed biomarkers. However, for a range of CSF proteins and biomarkers, one can reliably employ lumbar CSF as a proxy for ventricular CSF if or a lumbar/cranial index for the particular molecule has been established. It is therefore important to verify the compartmental preference of the proteins or biomarkers of interest prior to extrapolating from lumbar CSF to that of the ventricular fluid bordering the brain.

Spatial proteomics in three-dimensional intact specimens

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum

Multiple reaction monitoring (MRM) mass spectrometry is emerging as a relevant tool for measuring customized molecular markers in freshwater sentinel species. While this technique is typically used for the validation of protein molecular markers preselected from shotgun experiments, recent gains of MRM multiplexing capacity offer new possibilities to conduct large-scale screening of animal proteomes. By combining the strength of active biomonitoring strategies and MRM technologies, this study aims to propose a new strategy for the discovery of candidate proteins that respond to environmental variability. For this purpose, 249 peptides derived from 147 proteins were monitored by MRM in 273 male gammarids caged in 56 environmental sites, representative of the diversity of French water bodies. A methodology is here proposed to identify a set of customized housekeeping peptides (HKPs) used to correct analytical batch effects and allow proper comparison of peptide levels in gammarids. A comparative analysis performed on HKPs-normalized data resulted in the identification of peptides highly modulated in the environment and derived from proteins likely involved in the environmental stress response. Overall, this study proposes a breakthrough approach to screen and identify potential proteins responding to relevant environmental conditions in sentinel species.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Itaconic acid production is regulated by LaeA in Aspergillus pseudoterreus

The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus. To understand its role in regulating metabolism, we deleted and overexpressed laeA, and assessed the transcriptome, proteome, and secreted metabolome prior to and during initiation of phosphate limitation induced itaconic acid production. We found that secondary metabolite clusters, including the itaconic acid biosynthetic gene cluster, are regulated by laeA and that laeA is required for high yield production of itaconic acid. Overexpression of LaeA improves itaconic acid yield at the expense of biomass by increasing the expression of key biosynthetic pathway enzymes and attenuating the expression of genes involved in phosphate acquisition and scavenging. Increased yield was observed in optimized conditions as well as conditions containing excess nutrients that may be present in inexpensive sugar containing feedstocks such as excess phosphate or complex nutrient sources. This suggests that global regulators of metabolism may be useful targets for engineering metabolic flux that is robust to environmental heterogeneity.

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The Itaconic acid biosynthetic gene cluster is regulated by laeA.

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LaeA is required for production of itaconic acid.

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Overexpression of laeA attenuates genes involved in phosphate acquisition.

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Global regulator engineering increases robustness of itaconic acid production.