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The biotechnology industry has become a key element in modern societies. Within this industry, the production of recombinant enzymes and biopharmaceutical proteins is of major importance. The global markets for such recombinant proteins are growing rapidly and, accordingly, there is a continuous need for new production platforms that can deliver protein products in greater yields, with higher quality and at lower costs. This calls for the development of next-generation super-secreting cell factories. One of the microbial cell factories that can meet these challenges is the Gram-positive bacterium Bacillus subtilis , an inhabitant of the upper layers of the soil that has the capacity to secrete proteins in the gram per litre range. The engineering of B. subtilis into a next-generation super-secreting cell factory requires combined Systems and Synthetic Biology approaches. In this way, the bacterial protein secretion machinery can be optimized from the single molecule to the network level while, at the same time, taking into account the balanced use of cellular resources. Although highly ambitious, this is an achievable objective due to recent advances in functional genomics and Systems- and Synthetic Biological analyses of B. subtilis cells.

Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system. Given the chronic and heterogenous nature of the disease, treatment with various therapies is a frequent scenario in clinical practice. In persons with chronic morbidity such as MS patients, polypharmacy can give rise to considerable health problems. The aim of the present study was to examine the frequency of polypharmacy among relapsing-remitting (RR) MS patients as well as to analyse sociodemographic and clinical factors, which might be associated with polypharmacy (use of five or more medications). Differences in medication between MS patients with and without secondary illnesses (PwSI and Pw/oSI), between men and women and between patients with and without polypharmacy (PwP and Pw/oP) were examined. For 145 RRMS outpatients, we prospectively collected data by means of anamnesis, patient records, clinical examination and a structured patient interview. This was followed by comparative analyses of various patient subgroups (PwP vs. Pw/oP, PwSI vs. Pw/oSI, men vs. women). The proportion of included MS patients with polypharmacy (use of ≥5 medications) was 30.3%. PwP were significantly older than Pw/oP (45.9 vs. 41.7 years), had a lower level of education and showed a significantly higher median EDSS score (3.0 vs. 2.0). Comorbidities (p<0.001; odds ratio [OR] = 6.293) and higher EDSS scores (p = 0.029; OR = 1.440) were associated with a higher risk of polypharmacy. The proportion of polypharmacy among PwSI was approximately four times higher than among Pw/oSI (46.8% vs. 11.8%). Particularly in the use of antihypertensives, gastrointestinal drugs and dietary supplements, there were differences between Pw/oP and PwP. We found a high burden of polypharmacy in patients with RRMS. This particularly applies to more severely disabled MS patients who suffer from comorbidities.

Phytoplankton blooms characterize temperate ocean margin zones in spring. We investigated the bacterioplankton response to a diatom bloom in the North Sea and observed a dynamic succession of populations at genus-level resolution. Taxonomically distinct expressions of carbohydrate-active enzymes (transporters; in particular, TonB-dependent transporters) and phosphate acquisition strategies were found, indicating that distinct populations of Bacteroidetes, Gammaproteobacteria, and Alphaproteobacteria are specialized for successive decomposition of algal-derived organic matter. Our results suggest that algal substrate availability provided a series of ecological niches in which specialized populations could bloom. This reveals how planktonic species, despite their seemingly homogeneous habitat, can evade extinction by direct competition.

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics. Clinical proteomics critically depends on the ability to acquire highly reproducible data over an extended period of time. Here, the authors assess reproducibility over four months across different mass spectrometers and develop a computational approach to mitigate variation among instruments over time.

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton’s tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow’s milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of multiple sclerosis (MS), an inflammatory disorder of the central nervous system. Here, we propose a new consensus-based strategy to analyse and integrate miRNA and gene expression data in MS as well as other publically available data to gain a deeper understanding of the role of miRNAs in MS and to overcome the challenges posed by studies with limited patient sample sizes. We processed and analysed microarray datasets and compared the expression of genes and miRNAs in the blood of MS patients and controls. We then used our consensus and integration approach to construct two molecular networks dysregulated in MS: a miRNA- and a gene-based network. We identified 18 differentially expressed (DE) miRNAs and 128 DE genes that may contribute to the regulatory alterations behind MS. The miRNAs were linked to immunological and neurological pathways and we exposed let-7b-5p and miR-345-5p as promising blood-derived disease biomarkers in MS. The results suggest that DE miRNAs are more informative than DE genes in uncovering pathways potentially involved in MS. Our findings provide novel insights into the regulatory mechanisms and networks underlying MS.

Staphylococcus aureus is a major pathogen that colonizes about 20% of the human population. Intriguingly, this Gram-positive bacterium can survive and thrive under a wide range of different conditions, both inside and outside the human body. Here, we investigated the transcriptional adaptation of S. aureus HG001, a derivative of strain NCTC 8325, across experimental conditions ranging from optimal growth in vitro to intracellular growth in host cells. These data establish an extensive repertoire of transcription units and non-coding RNAs, a classification of 1412 promoters according to their dependence on the RNA polymerase sigma factors SigA or SigB, and allow identification of new potential targets for several known transcription factors. In particular, this study revealed a relatively low abundance of antisense RNAs in S. aureus, where they overlap only 6% of the coding genes, and only 19 antisense RNAs not co-transcribed with other genes were found. Promoter analysis and comparison with Bacillus subtilis links the small number of antisense RNAs to a less profound impact of alternative sigma factors in S. aureus. Furthermore, we revealed that Rho-dependent transcription termination suppresses pervasive antisense transcription, presumably originating from abundant spurious transcription initiation in this A+T-rich genome, which would otherwise affect expression of the overlapped genes. In summary, our study provides genome-wide information on transcriptional regulation and non-coding RNAs in S. aureus as well as new insights into the biological function of Rho and the implications of spurious transcription in bacteria.

Synthesis of acetate from carbon dioxide and molecular hydrogen is considered to be the first carbon assimilation pathway on earth. It combines carbon dioxide fixation into acetyl-CoA with the production of ATP via an energized cell membrane. How the pathway is coupled with the net synthesis of ATP has been an enigma. The anaerobic, acetogenic bacterium Acetobacterium woodii uses an ancient version of this pathway without cytochromes and quinones. It generates a sodium ion potential across the cell membrane by the sodium-motive ferredoxin:NAD oxidoreductase (Rnf). The genome sequence of A. woodii solves the enigma: it uncovers Rnf as the only ion-motive enzyme coupled to the pathway and unravels a metabolism designed to produce reduced ferredoxin and overcome energetic barriers by virtue of electron-bifurcating, soluble enzymes.

The undefined poly(3-hydroxybutyrate)- (PHB-) negative mutant R. eutropha PHB-4 was generated in 1970 by 1-nitroso-3-nitro-1-methylguanidine (NMG) treatment. Although being scientific relevant, its genotype remained unknown since its isolation except a recent first investigation. In this study, the mutation causing the PHA-negative phenotype of R. eutropha PHB-4 was confirmed independently: sequence analysis of the phaCAB operon identified a G320A mutation in phaC yielding a stop codon, leading to a massively truncated PhaC protein of 106 amino acids (AS) in R. eutropha PHB-4 instead of 589 AS in the wild type. No other mutations were observed within the phaCAB operon. As further mutations probably occurred in the genome of mutant PHB-4 potentially causing secondary effects on the cells' metabolism, the main focus of the study was to perform a 2D PAGE-based proteome analysis in order to identify differences in the proteomes of the wild type and mutant PHB-4. A total of 20 differentially expressed proteins were identified which provide valuable insights in the metabolomic changes of mutant PHB-4. Besides excretion of pyruvate, mutant PHB-4 encounters the accumulation of intermediates such as pyruvate and acetyl-CoA by enhanced expression of the observed protein species: (i) ThiJ supports biosynthesis of cofactor TPP and thereby reinforces the 2-oxoacid dehydrogenase complexes as PDHC, ADHC and OGDHC in order to convert pyruvate at a higher rate and the (ii) 3-isopropylmalate dehydrogenase LeuB3 apparently directs pyruvate to synthesis of several amino acids. Different (iii) acylCoA-transferases enable transfer reactions between organic acid intermediates, and (iv) citrate lyase CitE4 regenerates oxaloacetate from citrate for conversion with acetyl-CoA in the TCC in an anaplerotic reaction. Substantial amounts of reduction equivalents generated in the TCC are countered by (v) synthesis of more ubiquinones due to enhanced synthesis of MenG2 and MenG3, thereby improving the respiratory chain which accepts electrons from NADH and succinate.