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Abstract Human cytomegalovirus (HCMV) is a pathogen of high medical relevance. Subviral Dense Bodies (DB) were developed as a vaccine candidate to ameliorate the severe consequences of HCMV infection. Development of such a candidate vaccine for human application requires detailed knowledge of its interaction with the host. A comprehensive mass spectrometry (MS)- based analysis was performed regarding the changes in the proteome of cell culture cells, exposed to DB.

Protein abundance is controlled at the transcriptional, translational and posttranslational levels. Here, Öztürk et al. determine proteome changes resulting from individual knockout of 3308 nonessential genes in the yeast S. pombe, infer gene functionality, and show that protein upregulation under stable transcript expression utilizes optimal codons.

Summary: The study of neuronal signaling ex vivo requires the identification of the proteins that are represented within the neuronal axoplasm. Here, we describe a detailed protocol to isolate the axoplasm of peripheral and central axonal branches of sciatic dorsal root ganglia neurons in mice. The axoplasm is separated by 2D gel and digestion followed by proteomics analysis with MS/MS-LC. This protocol can be applied to dissect the axoplasmic protein expression signatures before and after a sciatic nerve or a spinal cord injury.For complete details on the use and execution of this protocol, please refer to Kong et al. (2020).

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care. Author summary Healthcare systems around the world are struggling to accommodate high numbers of the most severely ill patients with COVID-19. Moreover, the pandemic creates a pressing need to accelerate clinical trials investigating potential new therapeutics. While various biomarkers can discriminate and predict the future course of disease for patients of different disease severity, prognosis remains difficult for patient groups with similar disease severity, e.g. patients requiring intensive care. Established risk assessments in intensive care medicine such as the SOFA or APACHE II show only limited reliability in predicting future disease outcomes for COVID-19. In this study we hypothesized that the plasma proteome, which reflects the complete set of proteins that are expressed by an organism and are present in the blood, and which is known to comprehensively capture the host response to COVID-19, can be leveraged to allow for prediction of survival in the most critically ill patients with COVID-19. Here, we found 14 proteins, which over time changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.

(1) Background: Cells infected with the human cytomegalovirus (HCMV) produce subviral particles, termed dense bodies (DBs), both in-vitro and in-vivo. They are released from cells, comparable to infectious virions, and are enclosed by a membrane that resembles the viral envelope and mediates the entry into cells. To date, little is known about how the DB uptake influences the gene expression in target cells. The purpose of this study was to investigate the impact of DBs on cells, in the absence of a viral infection. (2) Methods: Mass spectrometry, immunoblot analyses, siRNA knockdown, and a CRISPR-CAS9 knockout, were used to investigate the changes in cellular gene expression following a DB exposure; (3) Results: A number of interferon-regulated genes (IRGs) were upregulated after the fibroblasts and endothelial cells were exposed to DBs. This upregulation was dependent on the DB entry and mediated by the type I interferon signaling through the JAK-STAT pathway. The induction of IRGs was mediated by the sensing of the DB-introduced DNA by the pattern recognition receptor cGAS. (4) Conclusions: The induction of a strong type I IFN response by DBs is a unique feature of the HCMV infection. The release of DBs may serve as a danger signal and concomitantly contribute to the induction of a strong, antiviral immune response.

Human cytomegalovirus (HCMV) infection is associated with severe disease conditions either following congenital transmission of the virus or viral reactivation in immunosuppressed individuals. Consequently, the establishment of a protective vaccine is of high medical need. Several candidates have been tested in preclinical and clinical studies, yet no vaccine has been licensed. Subviral dense bodies (DB) are a promising vaccine candidate. We have recently provided a GMP-compliant protocol for the production of DB, based on a genetically modified version of the HCMV laboratory strain Towne, expressing the pentameric complex of envelope protein gH-gL-pUL128-131 (Towne-UL130rep). In this work, we genetically attenuated Towne-UL130rep by abrogating the expression of the tegument protein pUL25 and by fusing the destabilizing domain ddFKBP to the N-terminus of the IE1- and IE2-proteins of HCMV. The resulting strain, termed TR-VAC, produced high amounts of DB under IE1/IE2 repressive conditions and concomitant supplementation of the viral terminase inhibitor letermovir to the producer cell culture. TR-VAC DB retained the capacity to induce neutralizing antibodies. A complex pattern of host protein induction was observed by mass spectrometry following exposure of primary human monocytes with TR-VAC DB. Human monocyte-derived dendritic cells (DC) moderately increased the expression of activation markers and MHC molecules upon stimulation with TR-VAC DB. In a co-culture with autologous T cells, the TR-VAC DB-stimulated DC induced a robust HCMV-specific T cell-activation and –proliferation. Exposure of donor-derived monocytic cells to DB led to the activation of a rapid innate immune response. This comprehensive data set thus shows that TR-VAC is an optimal attenuated seed virus strain for the production of a DB vaccine to be tested in clinical studies.

Developmental differentiation is a universal biological process that allows cells to adapt to different environments to perform specific functions. African trypanosomes progress through a tightly regulated life cycle in order to survive in different host environments when they shuttle between an insect vector and a vertebrate host. Transcriptomics has been useful to gain insight into RNA changes during stage transitions; however, RNA levels are only a moderate proxy for protein abundance in trypanosomes. We quantified 4270 protein groups during stage differentiation from the mammalian-infective to the insect form and provide classification for their expression profiles during development. Our label-free quantitative proteomics study revealed previously unknown components of the differentiation machinery that are involved in essential biological processes such as signaling, posttranslational protein modifications, trafficking and nuclear transport. Furthermore, guided by our proteomic survey, we identified the cause of the previously observed differentiation impairment in the histone methyltransferase DOT1B knock-out strain as it is required for accurate karyokinesis in the first cell division during differentiation. This epigenetic regulator is likely involved in essential chromatin restructuring during developmental differentiation, which might also be important for differentiation in higher eukaryotic cells. Our proteome dataset will serve as a resource for detailed investigations of cell differentiation to shed more light on the molecular mechanisms of this process in trypanosomes and other eukaryotes.

Given the significant increases in the incidence of metabolic diseases, efficient strategies for preventing and treating of these common disorders are urgently needed. This includes the development of phytopharmaceutical products or functional foods to prevent or cure metabolic diseases. Plant extracts from edible biomaterial provide a potential resource of structurally diverse molecules that can synergistically interfere with complex disorders. In this study we describe the safe application of ethanolic chamomile (Matricaria recutita) flowers extract (CFE) for the treatment and prevention of type 2 diabetes and associated disorders. We show in vitro that this extract activates in particular nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) and its isotypes. In a cellular context, in human primary adipocytes CFE administration (300 µg/ml) led to specific expression of target genes of PPARγ, whereas in human hepatocytes CFE-induced we detected expression changes of genes that were regulated by PPARα. In vivo treatment of insulin-resistant high-fat diet (HFD)-fed C57BL/6 mice with CFE (200 mg/kg/d) for 6 weeks considerably reduced insulin resistance, glucose intolerance, plasma triacylglycerol, non-esterified fatty acids (NEFA) and LDL/VLDL cholesterol. Co-feeding of lean C57BL/6 mice a HFD with 200 mg/kg/d CFE for 20 weeks showed effective prevention of fatty liver formation and hepatic inflammation, indicating additionally hepatoprotective effects of the extract. Moreover, CFE treatment did not reveal side effects, which have otherwise been associated with strong synthetic PPAR-targeting molecules, such as weight gain, liver disorders, hemodilution or bone cell turnover. Taken together, modulation of PPARs and other factors by chamomile flowers extract has the potential to prevent or treat type 2 diabetes and related disorders.

BACKGROUND: In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications. METHODOLOGY: We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection. CONCLUSIONS: With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome.