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Emerging clinical data show that three ceramide molecules, Cer d18:1/16:0, Cer d18:1/24:1, and Cer d18:1/24:0, are biomarkers of a fatal outcome in patients with cardiovascular disease. This finding raises basic questions about their metabolic origin, their contribution to disease pathogenesis, and the utility of targeting the underlying enzymatic machinery for treatment of cardiometabolic disorders. Here, we outline the development of a potent N-acetylgalactosamine-conjugated antisense oligonucleotide engineered to silence ceramide synthase 2 specifically in hepatocytes in vivo. We demonstrate that this compound reduces the ceramide synthase 2 mRNA level and that this translates into efficient lowering of protein expression and activity as well as Cer d18:1/24:1 and Cer d18:1/24:0 levels in liver. Intriguingly, we discover that the hepatocyte-specific antisense oligonucleotide also triggers a parallel modulation of blood plasma ceramides, revealing that the biomarkers predictive of cardiovascular death are governed by ceramide biosynthesis in hepatocytes. Our work showcases a generic therapeutic framework for targeting components of the ceramide enzymatic machinery to disentangle their roles in disease causality and to explore their utility for treatment of cardiometabolic disorders. ispartof: MOLECULAR THERAPY vol:30 issue:4 pages:1661-1674 ispartof: location:United States status: published

The study analyzed scientific texts based on a manually created database of synopses of theses in dentistry. The main goal was to structure medical texts into various topics by means of natural language processing techniques (topic modeling). Furthermore, a dynamic topic modeling showed the most popular in the field of dentistry over almost the last thirty years.

The study analyzed scientific texts based on a manually created database of synopses of theses in dentistry. The main goal was to structure medical texts into various topics by means of natural language processing techniques (topic modeling). Furthermore, a dynamic topic modeling showed the most popular in the field of dentistry over almost the last thirty years.

Diagnostics accuracy and usability of symptom checkers have been researched in several studies. Their ability to set a correct diagnosis especially in the urgent cases is questionable. There is one aspect of symptom checkers that has not been deeply studied yet. It is their ability to motivate patients to follow up after receiving a direct recommendation and to decrease a load on the health care professionals. The goal of this research is to study how patients behave after receiving a recommendation from a symptom checker and motivation of this behavior. We studied how patients react on the symptom checker recommendations and the motivation behind this behavior. In total we invited 3615 patients to have a symptom checker screening; 2374 of them agreed to run a symptom checker screening; 867 of them agreed to participate in the study. The proportion of the patients who agreed to have a symptom checker screening. So, we can clearly see that symptom checker screening doesn’t result in a significant decrease of the load on healthcare professionals. This is supported by the quantitative study results. The patients emphasized the ease of use of the tool and clearness of the recommendations it gives. However, they perceived it as rather a second opinion tool or a tool that helps to prepare to the doctor’s visit.

Electronic medical records (EMRs) include many valuable data about patients, which is, however, unstructured. Therefore, there is a lack of both labeled medical text data in Russian and tools for automatic annotation. As a result, today, it is hardly feasible for researchers to utilize text data of EMRs in training machine learning models in the biomedical domain. We present an unsupervised approach to medical data annotation. Syntactic trees are produced from initial sentences using morphological and syntactical analyses. In retrieved trees, similar subtrees are grouped using Node2Vec and Word2Vec and labeled using domain vocabularies and Wikidata categories. The usage of Wikidata categories increased the fraction of labeled sentences 5.5 times compared to labeling with domain vocabularies only. We show on a validation dataset that the proposed labeling method generates meaningful labels correctly for 92.7% of groups. Annotation with domain vocabularies and Wikidata categories covered more than 82% of sentences of the corpus, extended with timestamp and event labels 97% of sentences got covered. The obtained method can be used to label EMRs in Russian automatically. Additionally, the proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabulary.

Learning treatment methods and disease progression is significant part of medicine. Graph representation of data provides wide area for visualization and optimization of structure. Present work is dedicated to suggest method of data processing for increasing information interpretability. Graph compression algorithm based on maximum clique search is applied to data set with acute coronary syndrome treatment trajectories. Results of compression are studied using graph entropy measures.

Endometrial cancer (EC) is the most common gynecological tumor in high-income countries, and its incidence has increased over time. The most critical risk factor for EC is the long-term unopposed exposure to increased estrogens both exogenous and endogenous. Machine learning can be used as a promising tool to resolve longstanding challenges and support identification of the risk factors and their correlations before the clinical trials and make them more focused. In this paper we present the results of the research of the correlation analysis of Endometrial cancer risk factors. The study was performed with EC patients of the Almazov center in Saint-Petersburg, Russia. All women involved in the current study underwent radical surgical intervention due to EC. After initial cancer treatment, they were referred to the Almazov center outpatient specialists for follow-up visits. Many of them were readmitted of the inpatient clinic due to relapse. We extracted a variety of parameters related to lifestyle, dietary habits, socioeconomic, and reproductive features from the inpatient and outpatient databases of Almazov center. The medical records of the women with enough data were included in the study. Prediction of Progression-free survival (PFS) and overall survival (OS) were analyzed respectively. The AUC of ROC was calculated for PFS = 0.93 and for OS = 0.94.

Business process modeling aims to construct digital representations of processes being executed in the company. However, models derived from the event logs of their execution tend to overcomplicate the desired representation, making them difficult to apply. The most accurate recovery of the business process model requires a comprehensive study of the various artifacts stored in the company's information system. This paper, however, aims to explore the possibility to automatically obtain the most accurate model of business process, using mutual optimization of models recovered from a set of event logs. Further, the obtained models are executed in multi-agent simulation model of company, and the resulting event logs are examined to determine patterns that are specific to distinct employees and those that generally characterize business process.

The relevance of this study lies in improvement of machine learning models understanding. We present a method for interpreting clustering results and apply it to the case of clinical pathways modeling. This method is based on statistical inference and allows to get the description of the clusters, determining the influence of a particular feature on the difference between them. Based on the proposed approach, it is possible to determine the characteristic features for each cluster. Finally, we compare the method with the Bayesian inference explanation and with the interpretation of medical experts [1].

SUMMARYTemporal molecular changes in ageing mammalian organs are of relevance to disease etiology because many age-related diseases are linked to changes in the transcriptional and epigenetic machinery that regulate gene expression. We performed quantitative proteome analysis of chromatin-enriched protein extracts to investigate the dynamics of the chromatin-proteomes of the mouse brain, heart, lung, kidney, liver, and spleen at 3, 5, 10, and 15 months of age. Each organ exhibited a distinct chromatin-proteome and sets of unique proteins. The brain and spleen chromatin-proteomes were the most extensive, diverse, and heterogenous among the six organs. The spleen chromatin proteome appeared static during the lifespan, presenting a young phenotype that reflects the permanent alertness state and important role of this organ in physiological defense and immunity. We identified a total of 5928 proteins, including 2472 nuclear or chromatin associated proteins across the six mouse organs. Up to 3125 proteins were quantified in each organ demonstrating distinct and organ-specific temporal protein expression timelines and regulation at the post-translational level. Bioinformatics meta- analysis of these chromatin proteomes revealed distinct physiological and ageing- related features for each organ. Our results demonstrate the efficiency of organelle specific proteomics for in vivo studies of a model organism and consolidate the hypothesis that chromatin-associated proteins are involved in distinct and specific physiological functions in ageing organs.HIGHLIGHTSQuantitative chromatin-proteome analysis during mouse lifespan;Chromatin analysis in vitro and in vivo mouse models;Distinct chromatin proteomes of six organs during mouse lifespan;Correlations between ageing and chromatin regulation in mammalian lifespan.

The paper presents the research results of the structure, phase composition and mechanical properties of aluminum oxide ceramic specimens obtained by the additive manufacturing technology. The manufacturing process and the original equipment are described. The formation of several types of pores and surface boundaries between the layers is shown during ceramic 3D printing. It is found that for the different printing strategies, the different mechanical properties are observed in the obtained specimens that are stipulated by the structural anisotropy.

Метою дослідження було порівняння впливу базисної терапії (лізиноприлу та амлодипіну) в якості монотерапії та їх поєднання з донатором оксиду азоту – L-аргініном на клінічні та гемодинамічні показники у жінок у перименопаузальному періоді, хворих на артеріальну гіпертензію з коморбідним цукровим діабетом та без нього. Матеріал та методи дослідження: обстежено 104 пацієнтки із застосуванням імуноферментних (визначення рівня ендотеліну-1) та інструментальних (ехокардіографія з допплерометрією) методів дослідження. Результати. Отримані результати свідчать про те, що на фоні призначеного лікування відбувається оптимізація скоротливості міокарда та регрес діастолічної дифункції у всіх групах. Найбільше ці зміни виражені в підгрупі пацієнтів з артеріальною гіпертензією, які додатково до базисної терапії приймали L-аргінін. Щодо зміни рівня ендотеліну-1, як маркера порушення функції ендотелію, необхідно зазначити, що у пацієнтів, які отримували L-аргінін, бачили достовірне зниження його рівня. Обгрунтовано доцільність додавання до базисної терапії L-аргініну та його позитивний вплив на ендотеліальну дисфункцію, що проявлявся зниженням рівня ендотеліна-1. Висновки. В ході проведення дослідження ми дійшли висновку щодо оптимізації скоротливості міокарда на фоні призначеного лікування у всіх групах. Найбільше ці зміни виражені в підгрупі пацієнтів з артеріальною гіпертензією та цукровим діабетом, які додатково до схеми лікування отримували L-аргінін (регрес діастолічної дисфункції з 2-го до 1-го типу відбувся у 21 особи (47,7 %), що доводить його позитивний вплив на показники тиску наповнення лівого шлуночка та його розслаблення)

The present study is devoted to interpretable artificial intelligence in medicine. In our previous work we proposed an approach to clustering results interpretation based on Bayesian Inference. As an application case we used clinical pathways clustering explanation. However, the approach was limited by working for only binary features. In this work, we expand the functionality of the method and adapt it for modelling posterior distributions of continuous features. To solve the task, we apply BEST algorithm to provide Bayesian t-testing and use NUTS algorithm for posterior sampling. The general results of both binary and continuous interpretation provided by the algorithm have been compared with the interpretation of two medical experts.

Summary Acetylation of lysine 16 on histone H4 (H4K16ac) is catalyzed by histone acetyltransferase KAT8 and can prevent chromatin compaction in vitro. Although extensively studied in Drosophila, the functions of H4K16ac and two KAT8-containing protein complexes (NSL and MSL) are not well understood in mammals. Here, we demonstrate a surprising complex-dependent activity of KAT8: it catalyzes H4K5ac and H4K8ac as part of the NSL complex, whereas it catalyzes the bulk of H4K16ac as part of the MSL complex. Furthermore, we show that MSL complex proteins and H4K16ac are not required for cell proliferation and chromatin accessibility, whereas the NSL complex is essential for cell survival, as it stimulates transcription initiation at the promoters of housekeeping genes. In summary, we show that KAT8 switches catalytic activity and function depending on its associated proteins and that, when in the NSL complex, it catalyzes H4K5ac and H4K8ac required for the expression of essential genes.

Plants utilize a plethora of peptide signals to regulate their immune response. Peptide ligands and their cognate receptors involved in immune signaling share common motifs among many species of vascular plants. However, the origin and evolution of immune peptides is still poorly understood. Here, we searched for genes encoding small secreted peptides in the genomes of three bryophyte lineages-mosses, liverworts and hornworts-that occupy a critical position in the study of land plant evolution. We found that bryophytes shared common predicted small secreted peptides (SSPs) with vascular plants. The number of SSPs is higher in the genomes of mosses than in both the liverwort Marchantia polymorpha and the hornwort Anthoceros sp. The synthetic peptide elicitors-AtPEP and StPEP-specific for vascular plants, triggered ROS production in the protonema of the moss Physcomitrella patens, suggesting the possibility of recognizing peptide ligands from angiosperms by moss receptors. Mass spectrometry analysis of the moss Physcomitrella patens, both the wild type and the Δcerk mutant secretomes, revealed peptides that specifically responded to chitosan treatment, suggesting their role in immune signaling.

Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by predicting the presence of surgery for patients with the acute coronary syndrome.

The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.

Nonhost resistance, a resistance of plant species against all nonadapted pathogens, is considered the most durable and efficient immune system in plants. To increase our understanding of the response of barley plants to infection by powdery mildew, Blumeria graminis f. sp. tritici, we used quantitative proteomic analysis (LC-MS/MS). We compared the response of two genotypes of barley cultivar Golden Promise, wild type (WT) and plants with overexpression of phytoglobin (previously hemoglobin) class 1 (HO), which has previously been shown to significantly weaken nonhost resistance. A total of 8804 proteins were identified and quantified, out of which the abundance of 1044 proteins changed significantly in at least one of the four comparisons (‘i’ stands for ‘inoculated’)- HO/WT and HOi/WTi (giving genotype differences), and WTi/WT and HOi/HO (giving treatment differences). Among these differentially abundant proteins (DAP) were proteins related to structural organization, disease/defense, metabolism, transporters, signal transduction and protein synthesis. We demonstrate that quantitative changes in the proteome can explain physiological changes observed during the infection process such as progression of the mildew infection in HO plants that was correlated with changes in proteins taking part in papillae formation and preinvasion resistance. Overexpression of phytoglobins led to modification in signal transduction prominently by dramatically reducing the number of kinases induced, but also in the turnover of other signaling molecules such as phytohormones, polyamines and Ca2+. Thus, quantitative proteomics broaden our understanding of the role NO and phytoglobins play in barley during nonhost resistance against powdery mildew.

Background Salivary cell secretion (SCS) plays a critical role in blood feeding by medicinal leeches, making them of use for certain medical purposes even today. Results We annotated the Hirudo medicinalis genome and performed RNA-seq on salivary cells isolated from three closely related leech species, H. medicinalis, Hirudo orientalis, and Hirudo verbana. Differential expression analysis verified by proteomics identified salivary cell-specific gene expression, many of which encode previously unknown salivary components. However, the genes encoding known anticoagulants have been found to be expressed not only in salivary cells. The function-related analysis of the unique salivary cell genes enabled an update of the concept of interactions between salivary proteins and components of haemostasis. Conclusions Here we report a genome draft of Hirudo medicinalis and describe identification of novel salivary proteins and new homologs of genes encoding known anticoagulants in transcriptomes of three medicinal leech species. Our data provide new insights in genetics of blood-feeding lifestyle in leeches.

Overexpression of phytoglobins (formerly plant hemoglobins) increases the survival rate of plant tissues under hypoxia stress by the following two known mechanisms: (1) scavenging of nitric oxide (NO) in the phytoglobin/NO cycle and (2) mimicking ethylene priming to hypoxia when NO scavenging activates transcription factors that are regulated by levels of NO and O2 in the N-end rule pathway. To map the cellular and metabolic effects of hypoxia in barley (Hordeum vulgare L., cv. Golden Promise), with or without priming to hypoxia, we studied the proteome and metabolome of wild type (WT) and hemoglobin overexpressing (HO) plants in normoxia and after 24 h hypoxia (WT24, HO24). The WT plants were more susceptible to hypoxia than HO plants. The chlorophyll a + b content was lowered by 50% and biomass by 30% in WT24 compared to WT, while HO plants were unaffected. We observed an increase in ROS production during hypoxia treatment in WT seedlings that was not observed in HO seedlings. We identified and quantified 9694 proteins out of which 1107 changed significantly in abundance. Many proteins, such as ion transporters, Ca2+-signal transduction, and proteins related to protein degradation were downregulated in HO plants during hypoxia, but not in WT plants. Changes in the levels of histones indicates that chromatin restructuring plays a role in the priming of hypoxia. We also identified and quantified 1470 metabolites, of which the abundance of &gt, 500 changed significantly. In summary the data confirm known mechanisms of hypoxia priming by ethylene priming and N-end rule activation, however, the data also indicate the existence of other mechanisms for hypoxia priming in plants.

Biological functions of many proteins are governed by post-translational modifications (PTMs). In particular, the rich PTM complement in histones controls the gene expression and chromatin structure with major health implications via a combinatoric language. Deciphering that "histone code" is the great challenge for proteomics given an astounding number of possible proteoforms, including isomers with different PTM positions. These must be disentangled on the top- or middle-down level to preserve the key PTM connectivity, which condensed-phase separations failed to achieve. We reported the capability of ion mobility spectrometry (IMS) methods to resolve such isomers for model histone tails. Here, we advance to biological samples, showing middle-down analyses of histones from mouse embryonic stem cells via online chromatography to fractionate proteoforms with distinct PTM sets, differential or field asymmetric waveform IMS (FAIMS) to resolve the isomers, and Orbitrap mass spectrometry with electron transfer dissociation to identify the resolved species.

Protein arginine methyltransferase 5 (PRMT5) has emerged as a promising cancer drug target, and three PRMT5 inhibitors are currently in clinical trials for multiple malignancies. In this study, we investigated the role of PRMT5 in human acute myeloid leukemia (AML). Using an enzymatic dead version of PRMT5 and a PRMT5-specific inhibitor, we demonstrated the requirement of the catalytic activity of PRMT5 for the survival of AML cells. We then identified PRMT5 substrates using multiplexed quantitative proteomics and investigated their role in the survival of AML cells. We found that the function of the splicing regulator SRSF1 relies on its methylation by PRMT5 and that loss of PRMT5 leads to changes in alternative splicing of multiple essential genes. This explains the requirement of PRMT5 for leukemia cell survival. We show that PRMT5 regulates binding of SRSF1 to mRNAs and proteins and provide potential biomarkers for the treatment response to PRMT5 inhibitors.

We study the way dynamics affects modelling in chronic heart failure (CHF) tasks. By dynamics we understand the patient history and the appearance of new events, states and variables changing in time. The goal is to understand what impact past data has on prediction quality. Three different experiments have been conducted: CHF episode results prediction (better, worse, no change), CHF stage classification and heart rate value prediction. For modelling we use clinical data of CHF patients. For each task the groups of static and dynamic features are selected and analyzed. For each task 3 machine learning algorithms were trained: XGBoost, Logistic Regression, and Random Forest for multi classification and Linear Regression, Decision Tree and XGBoost for the regression task. Different combinations of features were examined from both groups applying forward feature selection algorithm. The results confirm that the highest predictions quality is reached with combination of static and dynamic features.

This study proposes a graph-based method for representing the dynamics of chronic diabetes as a complex process with different characteristics. The study was based on the case histories of 6864 patients with diabetes mellitus, 90% of whom suffer from type 2 diabetes. Our method allows to predict the sequence of events during the development of type 2 diabetes for each patient. Typical developmental trajectories of the disease were investigated, their clustering was carried out, the trajectory patterns were identified and studied. Based on the constructed directed graph reflecting transitions between different conditions of the patients, the clustering of diabetic statuses was carried out using the Modularity Class method; 8 clusters were selected, each of them was interpreted and studied. The method of the disease developmental trajectories creation by means of machine learning methods was described. Unlike static models of a disease course, this method considers complete past information on the patient and his or her previous events, using each event of the course of disease to predict the next event.

The study is aimed towards the assessment and improvement of complex informational, healthcare, and medical processes existing in the distributed environment during monitoring and providing healthcare services to patients with chronic diseases. The study is performed within a framework of a project for development a complex unified medical information system capable to work on large scale (on population-level). The presented experimental studies show that predictive modelling enables unified detection of remote patients' state using self-measuring and distributed electronic health records. In addition, an experimental mobile application is presented as a tool for data collection and user-system interaction during chronic disease monitoring and treatment.

This article describes the study results of echocardiographic (ECHO) test data for 4P medicine applied to cardiovascular patients. Data from more than 145,000 echocardiographic tests were analyzed. One of the objectives of the study is the possibility to identify patterns and relationships in patient characteristics for more accurate appointment procedures based on the history of the disease and the individual characteristics of the patient. This is achieved by using classifications models based on machine learning methods. Early detection of disease risks and "accurate" appointment of diagnostic procedures makes a significant contribution to value-based medicine. Moreover, it was also possible to identify the classes and characteristics of patients for whom repeated diagnostic procedures are well founded. Calculation of personal risks from empirical retrospective data helps to detect the disease in early stages. Identifying patients with high risk of disease complications allow physicians to make right decisions about timely treatment, which can significantly improve the quality of treatment, and help to avoid diseases complications, optimize costs and improve the quality of medical care.

This investigation focused on obtaining samples from ceramic composite materials, based on the ZrO2&ndash, 20%Al2O3 system, using the additive layer-by-layer fusion technology for thermoplastic systems. The structure and phase composition of the initial powders were studied, experimental samples were produced, and the structure and properties of the experimental samples that were obtained using additive technologies were analysed. The measured static bending strength of the samples was 450 &plusmn, 70 MPa, microhardness was 14 GPa, and the elasticity modulus was 280 &plusmn, 25 GPa. The strength of these samples are slightly inferior to that of similar materials, obtained using Ceramic Injection Molding technology because our samples were characterised by the residual porosity of about 15%.

Capillary ultrahigh-pressure liquid chromatography (cUHPLC) is essential for in-depth characterization of complex biomolecule mixtures by LC-MS. We developed a simple and fast method called FlashPack for custom packing of capillary columns of 50-100 cm length with sub- 2 m sorbent particles. FlashPack uses high sorbent concentrations of 500-1,000 mg/ml for packing at relatively low pressure of 100 bar. Column blocking by sorbent aggregation is avoided during the packing by gentle mechanical tapping of the capillary proximal end by a slowly rotating magnet bar. Utilizing a standard 100-bar pressure bomb, Flashpack allows for production of 15-25 cm cUHPLC columns within a few minutes and of 50 cm cUHPLC columns in less than an hour. Columns exhibit excellent reproducibility of back-pressure, retention time, and resolution (CV 8.7%). FlashPack cUHPLC columns are inexpensive, robust and deliver performance comparable to commercially available cUHPLC columns. The FlashPack method is versatile and enables production of cUHPLC columns using a variety of sorbent materials.

Capillary ultra-high-pressure liquid chromatography (cUHPLC) is essential for in-depth characterization of complex biomolecule mixtures by LC-MS. We developed a simple and fast method called FlashPack for custom packing of capillary columns of 50-100 cm length with sub-2-μm sorbent particles. FlashPack uses high sorbent concentrations of 500-1000 mg/ml for packing at relatively low pressure of 100 bar. Column blocking by sorbent aggregation is avoided during the packing of sorbent particles by gentle mechanical tapping of the capillary proximal end by a slowly rotating magnet bar. Utilizing a standard 100 bar pressure bomb, Flashpack allows for production of 15-25 cm cUHPLC columns within a few minutes and of 50 cm cUHPLC columns in less than an hour. Columns exhibit excellent reproducibility of back-pressure, retention time and resolution (CV 8,7 %). FlashPack cUHPLC columns are inexpensive, robust and deliver performance comparable to commercially available cUHPLC columns. The FlashPack method is versatile and enables production of cUHPLC columns using a variety of sorbent materials.

Streptomycetes are multicellular bacteria with complex developmental cycles. They are of biotechnological importance as they produce most bioactive compounds used in biomedicine, e.g. antibiotic, antitumoral and immunosupressor compounds. Streptomyces genomes encode many Ser/Thr/Tyr kinases, making this genus an outstanding model for the study of bacterial protein phosphorylation events. We used mass spectrometry based quantitative proteomics and phosphoproteomics to characterize bacterial differentiation and activation of secondary metabolism of Streptomyces coelicolor. We identified and quantified 3461 proteins corresponding to 44.3% of the S. coelicolor proteome across three developmental stages: vegetative hypha (first mycelium); secondary metabolite producing hyphae (second mycelium); and sporulating hyphae. A total of 1350 proteins exhibited more than 2-fold expression changes during the bacterial differentiation process. These proteins include 136 regulators (transcriptional regulators, transducers, Ser/Thr/Tyr kinases, signaling proteins), as well as 542 putative proteins with no clear homology to known proteins which are likely to play a role in differentiation and secondary metabolism. Phosphoproteomics revealed 85 unique protein phosphorylation sites, 58 of them differentially phosphorylated during differentiation. Computational analysis suggested that these regulated protein phosphorylation events are implicated in important cellular processes, including cell division, differentiation, regulation of secondary metabolism, transcription, protein synthesis, protein folding and stress responses. We discovered a novel regulated phosphorylation site in the key bacterial cell division protein FtsZ (pSer319) that modulates sporulation and regulates actinorhodin antibiotic production. We conclude that manipulation of distinct protein phosphorylation events may improve secondary metabolite production in industrial streptomycetes, including the activation of cryptic pathways during the screening for new secondary metabolites from streptomycetes.

Streptomycetes are multicellular bacteria with complex developmental cycles. They are of biotechnological importance as they produce most bioactive compounds used in biomedicine, e.g. antibiotic, antitumoral and immunosupressor compounds. Streptomyces genomes encode a large number of Ser/Thr/Tyr kinases, making this genus an outstanding model for the study of bacterial protein phosphorylation events. We used mass spectrometry based quantitative proteomics and phosphoproteomics to characterize bacterial differentiation and activation of secondary metabolism of Streptomyces coelicolor. We identified and quantified 3461 proteins corresponding to 44.3% of the S. coelicolor proteome across three developmental stages: vegetative hypha (MI); secondary metabolite producing hyphae (MII); and sporulating hyphae. A total of 1350 proteins exhibited more than 2-fold expression changes during the bacterial differentiation process. These proteins include 136 regulators (transcriptional regulators, transducers, Ser/Thr/Tyr kinases, signalling proteins), as well as 542 putative proteins with no clear homology to known proteins which are likely to play a role in differentiation and secondary metabolism. Phosphoproteomics revealed 85 unique protein phosphorylation sites, 58 of them differentially phosphorylated during differentiation. Computational analysis suggested that these regulated protein phosphorylation events are implicated in important cellular processes, including cell division, differentiation, regulation of secondary metabolism, transcription, protein synthesis, protein folding and stress responses. We discovered a novel regulated phosphorylation site in the key bacterial cell division protein FtsZ (pSer319) that modulates sporulation and regulates actinorhodin antibiotic production. We conclude that manipulation of distinct protein phosphorylation events may improve secondary metabolite production in industrial streptomycetes, including the activation of cryptic pathways during the screening for new secondary metabolites from streptomycetes.