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Abstract The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.

Abstract DNA polymerase III sliding clamp (DnaN) was recently validated as a new anti-tuberculosis target employing griselimycins. Three (2 S,4 R)−4-methylproline moieties of methylgriselimycin play significant roles in target binding and metabolic stability. Here, we identify the mycoplanecin biosynthetic gene cluster by genome mining using bait genes from the 4-methylproline pathway. We isolate and structurally elucidate four mycoplanecins comprising scarce homo-amino acids and 4-alkylprolines. Evaluating mycoplanecin E against Mycobacterium tuberculosis surprisingly reveals an excitingly low minimum inhibition concentration at 83 ng/mL, thus outcompeting griselimycin by approximately 24-fold. We show that mycoplanecins bind DnaN with nanomolar affinity and provide a co-crystal structure of mycoplanecin A-bound DnaN. Additionally, we reconstitute the biosyntheses of the unusual l-homoleucine, l-homonorleucine, and (2 S,4 R)−4-ethylproline building blocks by characterizing in vitro the full set of eight enzymes involved. The biosynthetic study, bioactivity evaluation, and drug target validation of mycoplanecins pave the way for their further development to tackle multidrug-resistant mycobacterial infections.

Abstract Background Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. Results In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models’ performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. Conclusions Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Abstract Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of glycans operating on the same topological level as the Symbol Nomenclature for Glycans (SNFG) that assigns colored, geometrical shapes to the main monomers. These symbols are then connected in tree-like structures, visualizing the glycan structure on a topological level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute the atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion from the IUPAC-condensed notation of glycans to SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modeling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github.com/kalininalab/GlyLES .

Among the current priorities for the development of the Russian oil and gas complex, which determine its competitiveness in the international hydrocarbon market, are its capabilities and the pace of its decarbonization. The development of decarbonization processes at the present requires the solution of a number of scientific, methodological and applied problems. Based on the results of a review of modern domestic and foreign approaches, methodological provisions are substantiated that determine the content and structure of the decarbonization category in the oil and gas complex. Decarbonization in the world economy system is defined as a systemic response to planetary climate change. In the system of the national economy (energy) — as a long-term socio-economic trend (pattern) of reducing the carbon intensity of GDP. In the system of the oil and gas complex — as a systemic process of its transformation in the national energy sector. In the system of industry management — as a process of complex, interconnected use of diverse (including fundamentally new) methods of solutions and technologies from various industries and fields of knowledge. Based on a systematic analysis of the problem field, the strategic goals and priorities for the development of the oil and gas complex have been clarified within the framework of the low-carbon development paradigm in the global energy system. The methods and tools of existing and being in the development and approval legislative documents have been studied. We proposed an approach to the formation of organizational and economic mechanisms that ensure the effective and sustainable development of the oil and gas complex in the context of decarbonization.

Recent studies showed that a low 25-hydroxyvitamin D (25(OH)D) level was associated with a higher risk of morbidity and severe course of COVID-19. Our study aimed to evaluate the effects of cholecalciferol supplementation on the clinical features and inflammatory markers in patients with COVID-19. A serum 25(OH)D level was determined in 311 COVID-19 patients. Among them, 129 patients were then randomized into two groups with similar concomitant medication. Group I (n = 56) received a bolus of cholecalciferol at a dose of 50,000 IU on the first and the eighth days of hospitalization. Patients from Group II (n = 54) did not receive the supplementation. We found significant differences between groups with the preferential increase in serum 25(OH)D level and Δ 25(OH)D in Group I on the ninth day of hospitalization (p < 0.001). The serum 25(OH)D level on the ninth day was negatively associated with the number of bed days (r = −0.23, p = 0.006); we did not observe other clinical benefits in patients receiving an oral bolus of cholecalciferol. Moreover, in Group I, neutrophil and lymphocyte counts were significantly higher (p = 0.04; p = 0.02), while the C-reactive protein level was significantly lower on the ninth day of hospitalization (p = 0.02). Patients with supplementation of 100,000 IU of cholecalciferol, compared to those without supplementation, showed a decrease in the frequencies of CD38++CD27 transitional and CD27−CD38+ mature naive B cells (p = 0.006 and p = 0.02) and an increase in the level of CD27−CD38− DN B cells (p = 0.02). Thus, the rise in serum 25(OH)D level caused by vitamin D supplementation in vitamin D insufficient and deficient patients may positively affect immune status and hence the course of COVID-19.

A low 25-hydroxyvitamin D (25(OH)D) level is considered as an independent risk factor for COVID-19 severity. However, the association between vitamin D status and outcomes in COVID-19 is controversial. In the present study we investigate the association between the serum 25(OH)D level, immune response, and clinical disease course in patients with COVID-19. A total of 311 patients hospitalized with COVID-19 were enrolled. For patients with a vitamin D deficiency/insufficiency, the prevalence of severe COVID-19 was higher than in those with a normal 25(OH)D level (p < 0.001). The threshold of 25(OH)D level associated with mortality was 11.4 ng/mL (p = 0.003, ROC analysis). The frequency of CD3+CD4+ T helper (Th) cells was decreased in patients with 25(OH)D level ≤ 11.4 ng/mL, compared to healthy controls (HCs). There were no differences in the frequency of naive, central memory (CM), effector memory (EM), and terminally differentiated effector memory Th cells in patients with COVID-19 compared to HCs. The frequency of T-follicular helpers was decreased both in patients with 25(OH)D level > 11.4 ng/mL (p < 0.001) and 25(OH)D level ≤ 11.4 ng/mL (p = 0.003) compared to HCs. Patients with 25(OH)D level > 11.4 ng/mL had an increased frequency of Th2 CM (p = 0.010) and decreased Th17 CM (p < 0.001). While the frequency of Th2 EM was significantly increased, the frequency of Th17 EM was significantly decreased in both groups compared to HCs. Thus, 25(OH)D level is an independent risk factor for the disease severity and mortality in patients with COVID-19. We demonstrate that the serum 25(OH)D level ≤ 11.4 ng/mL is associated with the stimulation of Th2 and the downregulation of Th17 cell polarization of the adaptive immunity in patients with COVID-19.

In the last 2 years, observational studies have shown that a low 25-hydroxyvitamin D (25(OH)D) level affected the severity of infection with the novel coronavirus (COVID-19). This study aimed to analyze the potential effect of vitamin D supplementation in reducing SARS-CoV-2 infection morbidity and severity in health care workers. Of 128 health care workers, 91 (consisting of 38 medical doctors (42%), 38 nurses (42%), and 15 medical attendants (16%)) were randomized into two groups receiving vitamin D supplementation. Participants of group I (n = 45) received water-soluble cholecalciferol at a dose of 50,000 IU/week for 2 consecutive weeks, followed by 5000 IU/day for the rest of the study. Participants of group II (n = 46) received water-soluble cholecalciferol at a dose of 2000 IU/day. For both groups, treatment lasted 3 months. Baseline serum 25(OH)D level in health care workers varied from 3.0 to 65.1 ng/mL (median, 17.7 (interquartile range, 12.2; 24.7) ng/mL). Vitamin D deficiency, insufficiency, and normal vitamin D status were diagnosed in 60%, 30%, and 10%, respectively. Only 78 subjects completed the study. Vitamin D supplementation was associated with an increase in serum 25(OH)D level, but only intake of 5000 IU/day was accompanied by normalization of serum 25(OH)D level, which occurred in 53% of cases. Neither vitamin D intake nor vitamin D deficiency/insufficiency were associated with a decrease in SARS-CoV-2 morbidity (odds ratio = 2.27; 95% confidence interval, 0.72 to 7.12). However, subjects receiving high-dose vitamin D had only asymptomatic SARS-CoV-2 in 10 (26%) cases; at the same time, participants who received 2000 IU/day showed twice as many SARS-CoV-2 cases, with mild clinical features in half of them.