Alterations of the gut microbiome are associated with epigenetic age acceleration and physical fitness.

Epigenetic clocks can measure aging and predict the incidence of diseases and mortality. Higher levels of physical fitness are associated with a slower aging process and a healthier lifespan. Microbiome alterations occur in various diseases and during the aging process, yet their relation to epigenetic clocks is not explored. To fill this gap, we collected metagenomic (from stool), epigenetic (from blood), and exercise-related data from physically active individuals and, by applying epigenetic clocks, we examined the relationship between gut flora, blood-based epigenetic age acceleration, and physical fitness. We revealed that an increased entropy in the gut microbiome of physically active middle-aged/old individuals is associated with accelerated epigenetic aging, decreased fitness, or impaired health status. We also observed that a slower epigenetic aging and higher fitness level can be linked to altered abundance of some bacterial species often linked to anti-inflammatory effects. Overall our data suggest that alterations in the microbiome can be associated with epigenetic age acceleration and physical fitness.

Non-small cell lung cancer patients treated with Anti-PD1 immunotherapy show distinct microbial signatures and metabolic pathways according to progression-free survival and PD-L1 status.

Due to the high variance in response rates concerning anti-PD1 immunotherapy (IT), there is an unmet need to discover innovative biomarkers to predict immune checkpoint inhibitor (ICI)-efficacy. Our study included 62 Caucasian advanced-stage non-small cell lung cancer (NSCLC) patients treated with anti-PD1 ICI. Gut bacterial signatures were evaluated by metagenomic sequencing and correlated with progression-free survival (PFS), PD-L1 expression and other clinicopathological parameters. We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). We find that alpha-diversity showed no significant difference in any comparison. However, there was a significant difference in beta-diversity between patients with long- (>6 months) vs. short (≤6 months) PFS and between chemotherapy (CHT)-treated vs. CHT-naive cases. Short PFS was associated with increased abundance of Firmicutes (F) and Actinobacteria phyla, whereas elevated abundance of Euryarchaeota was specific for low PD-L1 expression. F/Bacteroides (F/B) ratio was significantly increased in patients with short PFS. Multivariate analysis revealed an association between Alistipes shahii, Alistipes finegoldii, Barnesiella visceriola, and long PFS. In contrast, Streptococcus salivarius, Streptococcus vestibularis, and Bifidobacterium breve were associated with short PFS. Using Random Forest machine learning approach, we find that taxonomic profiles performed superiorly in predicting PFS (AUC = 0.74), while metabolic pathways including Amino Acid Synthesis and Fermentation were better predictors of PD-L1 expression (AUC = 0.87). We conclude that specific metagenomic features of the gut microbiome, including bacterial taxonomy and metabolic pathways might be suggestive of ICI efficacy and PD-L1 expression in NSCLC patients.

ProkBERT family: genomic language models for microbiome applications.

Background: In the evolving landscape of microbiology and microbiome analysis, the integration of machine learning is crucial for understanding complex microbial interactions, and predicting and recognizing novel functionalities within extensive datasets. However, the effectiveness of these methods in microbiology faces challenges due to the complex and heterogeneous nature of microbial data, further complicated by low signal-to-noise ratios, context-dependency, and a significant shortage of appropriately labeled datasets. This study introduces the ProkBERT model family, a collection of large language models, designed for genomic tasks. It provides a generalizable sequence representation for nucleotide sequences, learned from unlabeled genome data. This approach helps overcome the above-mentioned limitations in the field, thereby improving our understanding of microbial ecosystems and their impact on health and disease. Methods: ProkBERT models are based on transfer learning and self-supervised methodologies, enabling them to use the abundant yet complex microbial data effectively. The introduction of the novel Local Context-Aware (LCA) tokenization technique marks a significant advancement, allowing ProkBERT to overcome the contextual limitations of traditional transformer models. This methodology not only retains rich local context but also demonstrates remarkable adaptability across various bioinformatics tasks. Results: In practical applications such as promoter prediction and phage identification, the ProkBERT models show superior performance. For promoter prediction tasks, the top-performing model achieved a Matthews Correlation Coefficient (MCC) of 0.74 for E. coli and 0.62 in mixed-species contexts. In phage identification, ProkBERT models consistently outperformed established tools like VirSorter2 and DeepVirFinder, achieving an MCC of 0.85. These results underscore the models' exceptional accuracy and generalizability in both supervised and unsupervised tasks. Conclusions: The ProkBERT model family is a compact yet powerful tool in the field of microbiology and bioinformatics. Its capacity for rapid, accurate analyses and its adaptability across a spectrum of tasks marks a significant advancement in machine learning applications in microbiology. The models are available on GitHub (https://github.com/nbrg-ppcu/prokbert) and HuggingFace (https://huggingface.co/nerualbioinfo) providing an accessible tool for the community.

COVID-19 Infection Alters the Microbiome: Elite Athletes and Sedentary Patients Have Similar Bacterial Flora.

Regular exercise can upgrade the efficiency of the immune system and beneficially alter the composition of the gastro-intestinal microbiome. We tested the hypothesis that active athletes have a more diverse microbiome than sedentary subjects, which could provide better protection against COVID-19 during infection. Twenty active competing athletes (CA) (16 male and 4 females of the national first and second leagues), aged 24.15 ± 4.7 years, and 20 sedentary subjects (SED) (15 male and 5 females), aged 27.75 ± 7.5 years, who had been diagnosed as positive for COVID-19 by a PCR test, served as subjects for the study. Fecal samples collected five to eight days after diagnosis and three weeks after a negative COVID-19 PCR test were used for microbiome analysis. Except for two individuals, all subjects reported very mild and/or mild symptoms of COVID-19 and stayed at home under quarantine. Significant differences were not found in the bacterial flora of trained and untrained subjects. On the other hand, during COVID-19 infection, at the phylum level, the relative abundance of Bacteroidetes was elevated during COVID-19 compared to the level measured three weeks after a negative PCR test (p < 0.05) when all subjects were included in the statistical analysis. Since it is known that Bacteroidetes can suppress toll-like receptor 4 and ACE2-dependent signaling, thus enhancing resistance against pro-inflammatory cytokines, it is suggested that Bacteroidetes provide protection against severe COVID-19 infection. There is no difference in the microbiome bacterial flora of trained and untrained subjects during and after a mild level of COVID-19 infection.

The Nonsteroidal Anti-Inflammatory Drug Ketorolac Alters the Small Intestinal Microbiota and Bile Acids Without Inducing Intestinal Damage or Delaying Peristalsis in the Rat.

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) induce significant damage to the small intestine, which is accompanied by changes in intestinal bacteria (dysbiosis) and bile acids. However, it is still a question of debate whether besides mucosal inflammation also other factors, such as direct antibacterial effects or delayed peristalsis, contribute to NSAID-induced dysbiosis. Here we aimed to assess whether ketorolac, an NSAID lacking direct effects on gut bacteria, has any significant impact on intestinal microbiota and bile acids in the absence of mucosal inflammation. We also addressed the possibility that ketorolac-induced bacterial and bile acid alterations are due to a delay in gastrointestinal (GI) transit. Methods: Vehicle or ketorolac (1, 3 and 10 mg/kg) were given to rats by oral gavage once daily for four weeks, and the severity of mucosal inflammation was evaluated macroscopically, histologically, and by measuring the levels of inflammatory proteins and claudin-1 in the distal jejunal tissue. The luminal amount of bile acids was measured by liquid chromatography-tandem mass spectrometry, whereas the composition of microbiota by sequencing of bacterial 16S rRNA. GI transit was assessed by the charcoal meal method. Results: Ketorolac up to 3 mg/kg did not cause any signs of mucosal damage to the small intestine. However, 3 mg/kg of ketorolac induced dysbiosis, which was characterized by a loss of families belonging to Firmicutes (Paenibacillaceae, Clostridiales Family XIII, Christensenellaceae) and bloom of Enterobacteriaceae. Ketorolac also changed the composition of small intestinal bile by decreasing the concentration of conjugated bile acids and by increasing the amount of hyodeoxycholic acid (HDCA). The level of conjugated bile acids correlated negatively with the abundance of Erysipelotrichaceae, Ruminococcaceae, Clostridiaceae 1, Muribaculaceae, Bacteroidaceae, Burkholderiaceae and Bifidobacteriaceae. Ketorolac, under the present experimental conditions, did not change the GI transit. Conclusion: This is the first demonstration that low-dose ketorolac disturbed the delicate balance between small intestinal bacteria and bile acids, despite having no significant effect on intestinal mucosal integrity and peristalsis. Other, yet unidentified, factors may contribute to ketorolac-induced dysbiosis and bile dysmetabolism.

A comprehensive time course and correlation analysis of indomethacin-induced inflammation, bile acid alterations and dysbiosis in the rat small intestine.

It has been proposed that changes in microbiota due to nonsteroidal anti-inflammatory drugs (NSAIDs) alter the composition of bile, and elevation of hydrophobic secondary bile acids contributes to small intestinal damage. However, little is known about the effect of NSAIDs on small intestinal bile acids, and whether bile alterations correlate with mucosal injury and dysbiosis. Here we determined the ileal bile acid metabolome and microbiota 24, 48 and 72 h after indomethacin treatment, and their correlation with each other and with tissue damage in rats. In parallel with the development of inflammation, indomethacin increased the ileal proportion of glycine and taurine conjugated bile acids, but not bile hydrophobicity. Firmicutes decreased with time, whereas Gammaproteobacteria increased first, but declined later and were partially replaced by Bilophila, Bacteroides and Fusobacterium. Mucosal injury correlated negatively with unconjugated bile acids and Gram-positive bacteria, and positively with taurine conjugates and some Gram-negative taxa. Strong positive correlation was found between Lactobacillaceae, Ruminococcaceae, Clostridiaceae and unconjugated bile acids. Indomethacin-induced dysbiosis was not likely due to direct antibacterial effects or alterations in luminal pH. Here we provide the first detailed characterization of indomethacin-induced time-dependent alterations in small intestinal bile acid composition, and their associations with mucosal injury and dysbiosis. Our results suggest that increased bile hydrophobicity is not likely to contribute to indomethacin-induced small intestinal damage.

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.

Colonization Dynamics of Multidrug-Resistant Klebsiella pneumoniae Are Dictated by Microbiota-Cluster Group Behavior over Individual Antibiotic Susceptibility: A Metataxonomic Analysis.

Gastrointestinal carriage of multidrug-resistant (MDR) bacteria is one of the main risk factors for developing serious, difficult-to-treat infections. Given that there is currently no all-round solution to eliminate colonization with MDR bacteria, it is particularly important to understand the dynamic process of colonization to aid the development of novel decolonization strategies. The aim of our present study was to perform metataxonomic analyses of gut microbiota dynamics during colonization with an extended-spectrum ß-lactamase (ESBL)- and carbapenemase-producing Klebsiella pneumoniae (ECKP) strain in mice; additionally, to ascertain the effects of antibiotic administration (ampicillin, ceftazidime, and ciprofloxacin) on the establishment and elimination of ECKP intestinal colonization. We have found that the phyla Bacteroidetes and Firmicutes were most dominant in all of the treatment groups; however, Bacteroidetes was more common in the groups treated with antibiotics compared to the control group. Significant differences were observed among the different antibiotic-treated groups in beta but not alpha diversity, implying that the difference is the relative abundance of some bacterial community members. Bacteria from the Lachnospiraceae family (including Agathobacter, Anaerostipes, Lachnoclostridium 11308, Lachnospiraceae UCG-004, Lachnospiraceae NK3A20 group 11318, Lachnospiraceae NK4A136 group 11319, Roseburia, and Tyzzerella) showed an inverse relationship with the carriage rate of the ECKP strain, whereas members of Enterobacteriaceae and the ECKP strain have shown a correlational relationship. Our results suggest that the composition of the microbial community plays a primary role in the MDR-colonization rate, whereas the antibiotic susceptibility of individual MDR strains affects this process to a lesser extent. Distinct bacterial families have associated into microbial clusters, collecting taxonomically close species to produce survival benefits in the gut. These associations do not develop at random, as they may be attributed to the presence of specific metabolomic networks. A new concept should be introduced in designing future endeavors for MDR decolonization, supplemented by knowledge of the composition of the host bacterial community and the identification of bacterial clusters capable of suppressing or enhancing the invader species.

Association between Gut Microbial Diversity and Carotid Intima-Media Thickness.

Background and Objectives: There is an increasing focus on the effect of the gut microbiome on developing atherosclerosis, but there is still no unified standpoint. We aimed to find associations between intestinal microbiome diversity and a marker of subclinical atherosclerosis, the carotid intima-media thickness (IMT). Materials and Methods: Recruited from the Hungarian Twin Registry, 108 monozygotic (MZ) twins (mean age 52.4 ± 14.1 years, 58% female) underwent a comprehensive carotid ultrasound examination (Samsung RS85). Of the 108 MZ twins, 14 pairs (mean age 65 ± 6.4 years, 71% female) discordant for carotid IMT were selected to undergo a stool sample collection. A special stool sampling container was mailed and received from each participant. After DNA extraction, library construction was performed specifically for the V3-V4 hypervariable region of microbial 16S rRNA. Next, the microbiome composition of the samples was determined using Kraken software. Two hypotheses were tested with the exact permutation test: (1) in the group with normal IMT, the Shannon index of the phyla is higher; and (2) the Firmicutes/Bacteroidetes ratio is greater in the group with high IMT values. Furthermore, the abundance of different bacterial strains present at higher and normal IMT was also explored. Statistical analysis was carried out using R software. Results: Increased Firmicutes/Bacteroidetes ratio was associated with increased IMT (mean Firmicutes/Bacteroidetes ratio of IMT > 0.9 and IMT < 0.9 groups: 2.299 and 1.436, respectively; p = 0.031). In the group with normal IMT values, a substantially higher fraction of Prevotellaceae was observed in contrast with subjects having subclinical atherosclerosis. However, there was no significant difference in the alpha diversity between the two groups. Conclusions: The determining role of individual genera and their proportions in the development and progression of atherosclerosis can be assumed. Further studies are needed to clarify if these findings can be used as potential therapeutic targets.

The influence of antibiotics on transitory resistome during gut colonization with CTX-M-15 and OXA-162 producing Klebsiella pneumoniae ST15.

Great efforts have been made to limit the transmission of carbapenemase-producing Enterobacteriaceae (CPE), however, the intestinal reservoir of these strains and its modulation by various antibiotics remain largely unexplored. Our aim was to assess the effects of antibiotic administration (ampicillin, ceftazidime, ciprofloxacin) on the establishment and elimination of intestinal colonization with a CTX-M-15 ESBL and OXA-162 carbapenemase producing Klebsiella pneumoniae ST15 (KP5825) in a murine (C57BL/6 male mice) model. Whole genome sequencing of KP5825 strain was performed on an Illumina MiSeq platform. Conjugation assays were carried out by broth mating method. In colonization experiments, 5 × 106 CFU of KP5825 was administered to the animals by orogastric gavage, and antibiotics were administered in their drinking water for two weeks and were changed every day. The gut colonization rates with KP5825 were assessed by cultivation and qPCR. In each of the stool samples, the gene copy number of blaOXA-162 and blaCTX-M-15 were determined by qPCR. Antibiotic concentrations in the stool were determined by high pressure liquid chromatography and a bioanalytical method. The KP5825 contained four different plasmid replicon types, namely IncFII(K), IncL, IncFIB and ColpVC. IncL (containing the blaOXA-162 resistance gene within a Tn1991.2 genetic element) and IncFII(K) (containing the blaCTX-M-15 resistance gene) plasmids were successfully conjugated. During ampicillin and ceftazidime treatments, colonization rate of KP5825 increased, while, ciprofloxacin treatments in both concentrations (0.1 g/L and 0.5 g/L) led to significantly decreased colonization rates. The gene copy number blaOXA-162 correlated with K. pneumoniae in vivo, while a major elevation was observed in the copy number of blaCTX-M-15 from the first day to the fifteenth day in the 0.5 g/L dose ceftazidime treatment group. Our results demonstrate that commonly used antibiotics may have diverse impacts on the colonization rates of intestinally-carried CPE, in addition to affecting the gene copy number of their resistance genes, thus facilitating their stable persistance and dissemination.

Bladder cancer-related microbiota: examining differences in urine and tissue samples.

The microbiota isolated from the urine of bladder carcinoma patients exhibits significantly increased compositional abundance of some bacterial genera compared to the urine of healthy patients. Our aim was to compare the microbiota composition of cancerous tissues and urine samples collected from the same set of patients in order to improve the accuracy of diagnostic measures. Tissue samples were collected from patients during cancer tissue removal by transurethral resection. In parallel, urine samples were obtained by transurethral resectoscopy from the same patients. The V3-V4 region of the bacterial 16S rRNA gene was sequenced and analyzed using the Kraken pipeline. In the case of four patients, duplicate microbiota analysis from distant parts of the cancerous tissues was highly reproducible, and independent of the site of tissue collection of any given patient. Akkermansia, Bacteroides, Clostridium sensu stricto, Enterobacter and Klebsiella, as "five suspect genera", were over-represented in tissue samples compared to the urine. To our knowledge, this is the first study comparing urinary and bladder mucosa-associated microbiota profiles in bladder cancer patients. More accurate characterization of changes in microbiota composition during bladder cancer progression could provide new opportunities in the development of appropriate screening or monitoring methods.

Multiple Benefits of Plasmid-Mediated Quinolone Resistance Determinants in Klebsiella pneumoniae ST11 High-Risk Clone and Recently Emerging ST307 Clone.

International high-risk clones of Klebsiella pneumoniae are among the most common nosocomial pathogens. Increased diversity of plasmid-encoded antimicrobial resistance genes facilitates spread of these clones causing significant therapeutic difficulties. The purpose of our study was to investigate fluoroquinolone resistance in extended-spectrum beta-lactamase (ESBL)-producing strains, including four K. pneumoniae and a single K. oxytoca, isolated from blood cultures in Hungary. Whole-genome sequencing and molecular typing including multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE) were performed in selected strains. Gene expression of plasmid-mediated quinolone resistance determinants (PMQR) was investigated by quantitative-PCR. MLST revealed that three K. pneumoniae strains belonged to ST11 and one to ST307 whereas K. oxytoca belonged to ST52. The isolates harbored different ß-lactamase genes, however, all K. pneumoniae uniformly carried bla CTX-M-15. The K. pneumoniae isolates exhibited resistance to fluoroquinolones and carried various PMQR genes namely, two ST11 strains harbored qnrB4, the ST307 strain harbored qnrB1 and all K. pneumoniae harbored oqxAB efflux pump. Levofloxacin and moxifloxacin MIC values of K. pneumoniae ST11 and ST307 clones correlated with qnr and oqxAB expression levels. The qnrA1 carrying K. oxytoca ST52 exhibited reduced susceptibility to fluoroquinolones. The maintained expression of qnr genes in parallel with chromosomal mutations indicate an additional protective role of Qnr proteins that can support dissemination of high-risk clones. During development of high-level fluoroquinolone resistance, high-risk clones retain fitness thus, enabling them for dissemination in hospital environment. Based on our knowledge this is the first report of ST307 clone in Hungary, that is emerging as a potential high-risk clone worldwide. High-level fluoroquinolone resistance in parallel with upregulated PMQR gene expression are linked to high-risk K. pneumoniae clones.

Acquired qnrVC1 and blaNDM-1 resistance markers in an international high-risk Pseudomonas aeruginosa ST773 clone.

A multidrug-resistant Pseudomonas aeruginosa PS1 isolated from urine clinical sample was investigated in this study. The strain exhibited resistance to piperacillin/tazobactam, ciprofloxacin, imipenem, ceftazidime but it was susceptible to colistin. Analysis of whole-genome sequencing data by ResFinder detected various resistance determinants including qnrVC1 and blaNDM-1. The multiresistant P. aeruginosa isolate belonged to ST773 high-risk clone. The qnrVC1 and blaNDM-1 determinants were incorporated into different integrons. Expression of blaNDM-1 was fourfold and qnrVC1 was twofold increased, compared to that of rpsL housekeeping gene. Mutations in gyrA Thr83Leu and parC Ser87Leu were detected and additionally qnrVC1 expression indicates protective effect of QnrVC1 pentapeptid protein on gyrase and topoisomerase. High-risk P. aeruginosa clones integrate various carbapenemase and other resistance determinants into their genomes that facilitates further dissemination of multiresistance among clinical isolates. We report blaNDM-1 and qnrVC1 genes in P. aeruginosa ST773 international high-risk clone.

Exercise and probiotics attenuate the development of Alzheimer's disease in transgenic mice: Role of microbiome.

It has been suggested that exercise training and probiotic supplementation could decelerate the progress of functional and biochemical deterioration in APP/PS1 transgenic mice (APP/PS1TG). APP/PS1TG mice were subjected to exercise training and probiotic treatments and functional, biochemical and microbiome markers were analyzed. Under these conditions the mice significantly outperformed controls on The Morris Maze Test, and the number of beta-amyloid plaques decreased in the hippocampus. B. thetaiotaomicron levels correlated highly with the results of the Morris Maze Test (p < 0.05), and this group of bacteria was significantly elevated in the microbiome of the APP/PS1TG mice compared to the wild type. L. johnsonii levels positively correlated with the beta amyloid content and area. Data revealed that exercise and probiotic treatment can decrease the progress of Alzheimer's Disease and the beneficial effects could be partly mediated by alteration of the microbiome.

Occurrence of Ordered and Disordered Structural Elements in Postsynaptic Proteins Supports Optimization for Interaction Diversity.

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.

Differential signal sensitivities can contribute to the stability of multispecies bacterial communities.

BACKGROUND: Bacterial species present in multispecies microbial communities often react to the same chemical signal but at vastly different concentrations. The existence of different response thresholds with respect to the same signal molecule has been well documented in quorum sensing which is one of the best studied inter-cellular signalling mechanisms in bacteria. The biological significance of this phenomenon is still poorly understood, and cannot be easily studied in nature or in laboratory models. The aim of this study is to establish the role of differential signal response thresholds in stabilizing microbial communities. RESULTS: We tested binary competition scenarios using an agent-based model in which competing bacteria had different response levels with respect to signals, cooperation factors or both, respectively. While in previous scenarios fitter species outcompete slower growing competitors, we found that stable equilibria could form if the fitter species responded to a higher chemical concentration level than the slower growing competitor. We also found that species secreting antibiotic could form a stable community with other competing species if antibiotic production started at higher response thresholds. CONCLUSIONS: Microbial communities in nature rely on the stable coexistence of species that necessarily differ in their fitness. We found that differential response thresholds provide a simple and elegant way for keeping slower growing species within the community. High response thresholds can be considered as self-restraint of the fitter species that allows metabolically useful but slower growing species to remain within a community, and thereby the metabolic repertoire of the community will be maintained. REVIEWERS: This article was reviewed by Michael Gromiha, Sebastian Maurer-Stroh, István Simon and L. Aravind.

CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D.

The CX and DPX web-based servers at http://pongor.itk.ppke.hu/bioinfoservices are dedicated to the analysis of protein 3D structures submitted by the users as Protein Data Bank (PDB) files. CX computes an atomic protrusion index, cx that makes it possible to highlight the protruding atoms within a protein 3D structure. DPX calculates a depth index, dpx for buried atoms, and allows one to visualize the distribution of buried residues. CX and DPX visualize 3D structures colored according to the calculated indices and return PDB files that can be visualized using standard programs. A combined server site, the Protein Core Workbench allows visualization of dpx, cx, solvent-accessible area as well as the number of atomic contacts as 3D plots and 1D sequence plots. Online visualization of the 3D structures and 1D sequence plots are available in all three servers. Mirror sites are available at http://hydra.icgeb.trieste.it/protein/ .

Propagation on Molecular Interaction Networks: Prediction of Effective Drug Combinations and Biomarkers in Cancer Treatment.

BACKGROUND: Biomedical sciences use a variety of data sources on drug molecules, genes, proteins, diseases and scientific publications etc. This system can be best pictured as a giant data-network linked together by physical, functional, logical and similarity relationships. A new hypothesis or discovery can be considered as a new link that can be deduced from the existing connections. For instance, interactions of two pharmacons - if not already known - represent a testable novel hypothesis. Such implicit effects are especially important in complex diseases such as cancer. METHODS: The method we applied was to test whether novel drug combinations or novel biomarkers can be predicted from a network of existing oncological databases. We start from the hypothesis that novel, implicit links can be discovered between the network neighborhoods of data items. RESULTS: We showed that the overlap of network neighborhoods is strongly correlated with the pairwise interaction strength of two pharmacons used in cancer therapy, and it is also well correlated with clinical data. In a second case study we employed this strategy to the discovery of novel biomarkers based on text analysis. In 2012 we prioritized 10 potential biomarkers for ovarian cancers, 2 of which were in fact described as such in the subsequent years. CONCLUSION: The strategy seems to hold promises for prioritizing new drug combinations or new biomarkers for experimental testing. Its use is naturally limited by the sparsity and the quality of experimental data, however both of these aspects are expected to improve given the development of current databases.

A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results.

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer.

Census of solo LuxR genes in prokaryotic genomes.

luxR genes encode transcriptional regulators that control acyl homoserine lactone-based quorum sensing (AHL QS) in Gram negative bacteria. On the bacterial chromosome, luxR genes are usually found next or near to a luxI gene encoding the AHL signal synthase. Recently, a number of luxR genes were described that have no luxI genes in their vicinity on the chromosome. These so-called solo luxR genes may either respond to internal AHL signals produced by a non-adjacent luxI in the chromosome, or can respond to exogenous signals. Here we present a survey of solo luxR genes found in complete and draft bacterial genomes in the NCBI databases using HMMs. We found that 2698 of the 3550 luxR genes found are solos, which is an unexpectedly high number even if some of the hits may be false positives. We also found that solo LuxR sequences form distinct clusters that are different from the clusters of LuxR sequences that are part of the known luxR-luxI topological arrangements. We also found a number of cases that we termed twin luxR topologies, in which two adjacent luxR genes were in tandem or divergent orientation. Many of the luxR solo clusters were devoid of the sequence motifs characteristic of AHL binding LuxR proteins so there is room to speculate that the solos may be involved in sensing hitherto unknown signals. It was noted that only some of the LuxR clades are rich in conserved cysteine residues. Molecular modeling suggests that some of the cysteines may be involved in disulfide formation, which makes us speculate that some LuxR proteins, including some of the solos may be involved in redox regulation.