Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network

BACKGROUND: Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction. RESULTS: To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products) and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' – classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions – the 'compensatory complexes' theme. Thematic maps – networks rendered in terms of such themes – can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes. CONCLUSION: Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships

Xpert MTB/RIF Assay Shows Faster Clearance of Mycobacterium tuberculosis DNA with Higher Levels of Rifapentine Exposure.

The Xpert MTB/RIF assay is both sensitive and specific as a diagnostic test. Xpert also reports quantitative output in cycle threshold (CT) values, which may provide a dynamic measure of sputum bacillary burden when used longitudinally. We evaluated the relationship between Xpert CT trajectory and drug exposure during tuberculosis (TB) treatment to assess the potential utility of Xpert CT for treatment monitoring. We obtained serial sputum samples from patients with smear-positive pulmonary TB who were consecutively enrolled at 10 international clinical trial sites participating in study 29X, a CDC-sponsored Tuberculosis Trials Consortium study evaluating the tolerability, safety, and antimicrobial activity of rifapentine at daily doses of up to 20 mg/kg of body weight. Xpert was performed at weeks 0, 2, 4, 6, 8, and 12. Longitudinal CT data were modeled using a nonlinear mixed effects model in relation to rifapentine exposure (area under the concentration-time curve [AUC]). The rate of change of CT was higher in subjects receiving rifapentine than in subjects receiving standard-dose rifampin. Moreover, rifapentine exposure, but not assigned dose, was significantly associated with rate of change in CT (P = 0.02). The estimated increase in CT slope for every additional 100 μg · h/ml of rifapentine drug exposure (as measured by AUC) was 0.11 CT/week (95% confidence interval [CI], 0.05 to 0.17). Increasing rifapentine exposure is associated with a higher rate of change of Xpert CT, indicating faster clearance of Mycobacterium tuberculosis DNA. These data suggest that the quantitative outputs of the Xpert MTB/RIF assay may be useful as a dynamic measure of TB treatment response

A First Attempt to Bring Computational Biology into Advanced High School Biology Classrooms

Computer science has become ubiquitous in many areas of biological research, yet most high school and even college students are unaware of this. As a result, many college biology majors graduate without adequate computational skills for contemporary fields of biology. The absence of a computational element in secondary school biology classrooms is of growing concern to the computational biology community and biology teachers who would like to acquaint their students with updated approaches in the discipline. We present a first attempt to correct this absence by introducing a computational biology element to teach genetic evolution into advanced biology classes in two local high schools. Our primary goal was to show students how computation is used in biology and why a basic understanding of computation is necessary for research in many fields of biology. This curriculum is intended to be taught by a computational biologist who has worked with a high school advanced biology teacher to adapt the unit for his/her classroom, but a motivated high school teacher comfortable with mathematics and computing may be able to teach this alone. In this paper, we present our curriculum, which takes into consideration the constraints of the required curriculum, and discuss our experiences teaching it. We describe the successes and challenges we encountered while bringing this unit to high school students, discuss how we addressed these challenges, and make suggestions for future versions of this curriculum.We believe that our curriculum can be a valuable seed for further development of computational activities aimed at high school biology students. Further, our experiences may be of value to others teaching computational biology at this level. Our curriculum can be obtained at http://ecsite.cs.colorado.edu/?page_id=149#biology or by contacting the authors

Plasma Zonulin Levels in Childhood Nephrotic Syndrome

Objective: We conducted this study to test the hypothesis that plasma zonulin levels are elevated in pediatric patients with nephrotic syndrome compared to healthy controls.Study Design: Plasma zonulin levels were measured by ELISA in 114 children enrolled in the NEPTUNE study. Clinical and laboratory data were retrieved from the NEPTUNE database.Results: The median age of the patients was 10 (IQR = 5 to 14) years, 59 were male, 64 had minimal change disease, 47 focal segmental glomerulosclerosis, median eGFR was 96 (IQR = 80 to 114) ml/min/1.73 m2, and median urine protein:creatinine ratio was 0.5 (IQR = 0.1 to 3.4) (g:g). The plasma zonulin level was 14.2 ± 5.0 vs. 10.2 ± 2.5 ng/ml in healthy adults in a report using the same assay kit, P = 0.0025. These findings were confirmed in an independent cohort of children with nephrotic syndrome compared to healthy age-matched controls, P = 0.01. Zonulin concentrations did not differ in children with minimal change disease vs. focal segmental glomerulosclerosis, frequently relapsing vs. steroid-dependent vs. steroid-resistant clinical course, and were not influenced by the immunosuppressive treatment regimen. There was no relationship between plasma zonulin levels and the absolute or percentage change in proteinuria from enrollment until the time of the zonulin assay.Conclusion: Plasma zonulin levels are elevated in childhood nephrotic syndrome regardless of level of proteinuria or specific treatment. The cause of the high plasma zonulin levels and whether zonulin contributes to glomerular injury requires further study

Improving protein function prediction methods with integrated literature data

<p>Abstract</p> <p>Background</p> <p>Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.</p> <p>Results</p> <p>We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.</p> <p>Conclusion</p> <p>Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.</p

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A&gt;T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

Image-guided Tumor Ablation: Standardization of Terminology and Reporting Criteria

The field of interventional oncology with use of image-guided tumor ablation requires standardization of terminology and reporting criteria to facilitate effective communication of ideas and appropriate comparison between treatments that use different technologies, such as chemical (ethanol or acetic acid) ablation, and thermal therapies, such as radiofrequency (RF), laser, microwave, ultrasound, and cryoablation. This document provides a framework that will hopefully facilitate the clearest communication between investigators and will provide the greatest flexibility in comparison between the many new, exciting, and emerging technologies. An appropriate vehicle for reporting the various aspects of image-guided ablation therapy, including classification of therapies and procedure terms, appropriate descriptors of imaging guidance, and terminology to define imaging and pathologic findings, are outlined. Methods for standardizing the reporting of follow-up findings and complications and other important aspects that require attention when reporting clinical results are addressed. It is the group’s intention that adherence to the recommendations will facilitate achievement of the group’s main objective: improved precision and communication in this field that lead to more accurate comparison of technologies and results and, ultimately, to improved patient outcomes. The intent of this standardization of terminology is to provide an appropriate vehicle for reporting the various aspects of image-guided ablation therapy

Consensus guidelines for the definition of time-to-event end points in image-guided tumor ablation: results of the SIO and DATECAN initiative

International audienceThere is currently no consensus regarding preferred clinical outcome measures following image-guided tumor ablation or clear definitions of oncologic end points. This consensus document proposes standardized definitions for a broad range of oncologic outcome measures with recommendations on how to uniformly document, analyze, and report outcomes. The initiative was coordinated by the Society of Interventional Oncology in collaboration with the Definition for the Assessment of Time-to-Event End Points in Cancer Trials, or DATECAN, group. According to predefined criteria, based on experience with clinical trials, an international panel of 62 experts convened. Recommendations were developed using the validated three-step modified Delphi consensus method. Consensus was reached on when to assess outcomes per patient, per session, or per tumor; on starting and ending time and survival time definitions; and on time-to-event end points. Although no consensus was reached on the preferred classification system to report complications, quality of life, and health economics issues, the panel did agree on using the most recent version of a validated patient-reported outcome questionnaire. This article provides a framework of key opinion leader recommendations with the intent to facilitate a clear interpretation of results and standardize worldwide communication. Widespread adoption will improve reproducibility, allow for accurate comparisons, and avoid misinterpretations in the field of interventional oncology research. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Liddell in this issue

Proteorhodopsin Phototrophy Promotes Survival of Marine Bacteria during Starvation

Mutational analysis provides direct evidence for the link between proteorhodopsin light-harvesting and enhanced survival of marine bacteria