Retinal capillary plexus in Parkinson's disease using optical coherence tomography angiography

AIM

To evaluate the alterations of the retinal microvasculature and foveal avascular zone in patients with Parkinson's disease (PD) using optical coherence tomography angiography (OCT-A).

METHODS

A retrospective study of PD patients examined in the Ophthalmology Department of the General Hospital of Athens, "Georgios Gennimatas" from March 2021 to March 2022 was conducted. Totally 44 patients with PD were included and 18 healthy controls were examined, hence a total of 124 eyes were enrolled in the study. The foveal and parafoveal superficial and deep capillary plexus vascular density (fSCP-VD, fDCP-VD, pSCP-VD, pDCP-CD) and foveal avascular zone (FAZ) were quantified with OCTA. Optical coherence tomography (OCT) was used to measure macular thickness. Our statistical analysis was conducted by using a mixed effect linear regression model.

RESULTS

After adjustment for age and gender, the mean parafoveal superficial capillary plexus vascular density (pSCP-VD) and mean parafoveal deep capillary plexus vascular density (pDCP-VD) were significantly decreased in individuals with PD (P<0.001 in both) by -2.35 (95%CI -3.3, -1.45) and -7.5 (95%CI -10.4, -4.6) respectively. fSCP-VD and fDCP-VD didn't approach statistical significance. The FAZ area and perimeter were significantly decreased (P<0.001 in both) by -0.1 mm2 (95%CI -0.13, -0.07) and -0.49 mm2 (95%CI -0.66, -0.32) respectively. Circularity didn't approach statistical significance. Central retinal thickness (CRT) was significantly decreased in individuals with PD (P<0.001) by -23.1 µm (95%CI -30.2, -16) and temporal retinal thickness (TRT) was decreased (P=0.025) by -11 µm (95%CI -22, -1.5) while nasal retinal thickness (NRT) only approached statistical significance (P=0.066).

CONCLUSION

The mean pSCP-VD, pDCP-VD, CRT and TRT are significantly decreased and FAZ is altered in individuals with PD. These findings can be potentially used as biomarkers for the diagnosis and evaluation of early PD.

LIRcentral: a manually curated online database of experimentally validated functional LIR motifs

Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/LIR motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.eu), a freely available online repository for user-friendly access to comprehensive, high-quality information regarding LIR motifs from manually curated publications. Herein, we describe the development of LIRcentral and showcase currently available data and features, along with our plans for the expansion of this resource. Information incorporated in LIRcentral is useful for accomplishing a variety of research tasks, including: (i) guiding wet biology researchers for the characterization of novel instances of LIR motifs, (ii) giving bioinformaticians/computational biologists access to high-quality LIR motifs for building novel prediction methods for LIR motifs and LIR containing proteins (LIRCPs) and (iii) performing analyses to better understand the biological importance/features of functional LIR motifs. We welcome feedback on the LIRcentral content and functionality by all interested researchers and anticipate this work to spearhead a community effort for sustaining this resource which will further promote progress in studying LIR motifs/LIRCPs.Abbreviations: AIM, Atg8-family interacting motif; Atg8, autophagy related 8; GABARAP, GABA type A receptor-associated protein; LIR, LC3-interacting region; LIRCP, LIR-containing protein; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; PMID, PubMed identifier; PPI, protein-protein interaction; SLiM, short linear motif.

CGG toolkit: Software components for computational genomics

Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

Unraveling the functional dark matter through global metagenomics

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Unraveling the Role of Molecular Profiling in Predicting Treatment Response in Stage III Colorectal Cancer Patients: Insights from the IDEA International Study

BACKGROUND

This study aimed to investigate the molecular profiles of 237 stage III CRC patients from the international IDEA study. It also sought to correlate these profiles with Toll-like and vitamin D receptor polymorphisms, clinicopathological and epidemiological characteristics, and patient outcomes.

METHODS

Whole Exome Sequencing and PCR-RFLP on surgical specimens and blood samples, respectively, were performed to identify molecular profiling and the presence of Toll-like and vitamin D polymorphisms. Bioinformatic analysis revealed mutational status.

RESULTS

Among the enrolled patients, 63.7% were male, 66.7% had left-sided tumors, and 55.7% received CAPOX as adjuvant chemotherapy. Whole exome sequencing identified 59 mutated genes in 11 different signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) CRC panel. On average, patients had 8 mutated genes (range, 2-21 genes). Mutations in ARAF and MAPK10 emerged as independent prognostic factors for reduced DFS (p = 0.027 and p < 0.001, respectively), while RAC3 and RHOA genes emerged as independent prognostic factors for reduced OS (p = 0.029 and p = 0.006, respectively). Right-sided tumors were also identified as independent prognostic factors for reduced DFS (p = 0.019) and OS (p = 0.043). Additionally, patients with tumors in the transverse colon had mutations in genes related to apoptosis, PIK3-Akt, Wnt, and MAPK signaling pathways.

CONCLUSIONS

Molecular characterization of tumor cells can enhance our understanding of the disease course. Mutations may serve as promising prognostic biomarkers, offering improved treatment options. Confirming these findings will require larger patient cohorts and international collaborations to establish correlations between molecular profiling, clinicopathological and epidemiological characteristics and clinical outcomes.

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.

Social Cognition Impairments in Association to Clinical, Cognitive, Mood, and Fatigue Features in Multiple Sclerosis: A Study Protocol

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system (CNS), characterized by the diffuse grey and white matter damage. Cognitive impairment (CI) is a frequent clinical feature in patients with MS (PwMS) that can be prevalent even in early disease stages, affecting the physical activity and active social participation of PwMS. Limited information is available regarding the influence of MS in social cognition (SC), which may occur independently from the overall neurocognitive dysfunction. In addition, the available information regarding the factors that influence SC in PwMS is limited, e.g., factors such as a patient's physical disability, different cognitive phenotypes, mood status, fatigue. Considering that SC is an important domain of CI in MS and may contribute to subjects' social participation and quality of life, we herein conceptualize and present the methodological design of a cross-sectional study in 100 PwMS of different disease subtypes. The study aims (a) to characterize SC impairment in PwMS in the Greek population and (b) to unveil the relationship between clinical symptoms, phenotypes of CI, mood status and fatigue in PwMS and the potential underlying impairment on tasks of SC.

A Prospective, Observational Study Assessing Effectiveness, Safety, and QoL of Greek Patients with Multiple Sclerosis Under Treatment with Fingolimod

INTRODUCTION

Fingolimod is the first approved oral therapy for relapsing-remitting multiple sclerosis (RRMS). The present study aimed to further characterize fingolimod's safety profile, and to assess the patient-reported treatment satisfaction and impact of fingolimod on the quality of life (QoL) of patients with multiple sclerosis (MS) treated in routine care in Greece.

METHODS

This was a multicenter, prospective, observational, 24-month study conducted in Greece by hospital-based and private practice neurologists who specialize in MS. Eligible patients had initiated fingolimod within 15 days in accordance with the locally approved label. Safety outcomes included any adverse event (AE) observed during the study period and efficacy outcomes included both objective (disability progression and 2-year annualized relapse rate) and patient-reported assessments (Treatment Satisfaction Questionnaire for Medication (TSQM) v1.4 and the EuroQol (EQ)-5-dimension (5D) 3-level instruments).

RESULTS

A total of 489 eligible patients (age 41.2 ± 9.8 years; 63.7% female; 4.2% treatment-naive) were exposed to fingolimod for a median of 23.7 months. During the observation period, 20.5% of the participants experienced 233 AEs. Lymphopenia (8.8%), leukopenia (4.2%), hepatic enzyme increased (3.4%), and infections (3.0%) were the most common. Most patients (89.3%) did not experience disability progression; the 2-year annualized relapse rate decreased by 94.7% compared to baseline. The median EQ-visual analogue scale (VAS) was 74.5 at month 24 vs. 65.0 at enrollment (p < 0.001) and the EQ-5D index score was 0.80 vs. 0.78, respectively. Significant improvements were noted in the TSQM global satisfaction and effectiveness domain scores between 6 and 24 months post enrollment (median scores at month 24, 71.4 and 66.7, respectively) (p < 0.001). Significant increases from enrollment to the 24th month were also noted in the patients' global satisfaction and effectiveness domain scores [mean change of 7.4 ± 17.7 (p = 0.005) and mean increase of 5.4 ± 16.2) (p = 0.043), respectively].

CONCLUSION

In the real-world setting of Greece, fingolimod demonstrates a clinical benefit and a predictable and manageable safety profile, which contribute towards high patient-reported treatment satisfaction and improvements in the QoL of patients with MS.

Genome-Wide Analysis of lncRNA-mRNA Co-Expression Networks in CD133+/CD44+ Stem-like PDAC Cells

Pancreatic ductal adenocarcinoma (PDAC), the second most prevalent gastrointestinal malignancy and the most common type of pancreatic cancer is linked with poor prognosis and, eventually, with high mortality rates. Early detection is seldom, while tumor heterogeneity and microarchitectural alterations benefit PDAC resistance to conventional therapeutics. Although emerging evidence suggest the core role of cancer stem cells (CSCs) in PDAC aggressiveness, unique stem signatures are poorly available, thus limiting the efforts of anti-CSC-targeted therapy. Herein, we report the findings of the first genome-wide analyses of mRNA/lncRNA transcriptome profiling and co-expression networks in PDAC cell line-derived CD133+/CD44+ cells, which were shown to bear a CSC-like phenotype in vitro and in vivo. Compared to CD133-/CD44- cells, the CD133+/CD44+ population demonstrated significant expression differences in both transcript pools. Using emerging bioinformatic tools, we performed lncRNA target coding gene prediction analysis, which revealed significant Gene Ontology (GO), pathway, and network enrichments in many dyregulated lncRNA nearby (cis or trans) mRNAs, with reported involvement in the regulation of CSC phenotype and functions. In this context, the construction of lncRNA/mRNA networks by ingenuity platforms identified the lncRNAs ATF2, CHEK1, DCAF8, and PAX8 to interact with "hub" SC-associated mRNAs. In addition, the expressions of the above lncRNAs retrieved by TCGA-normalized RNAseq gene expression data of PAAD were significantly correlated with clinicopathological features of PDAC, including tumor grade and stage, nodal metastasis, and overall survival. Overall, our findings shed light on the identification of CSC-specific lncRNA signatures with potential prognostic and therapeutic significance in PDAC.

Intravenous thrombolysis for acute ischemic stroke associated with known left ventricular thrombus: safe or not?

Safety and efficacy data on intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) in case of known left ventricular thrombus (LVT) are lacking. We present the case of a 35-year-old male with disabling AIS and known LVT that was treated successfully with intravenous alteplase. Apart from neurological improvement, post-procedural full thrombus lysis was also evident. Even though performing IVT in similar instances constitutes a difficult decision for physicians, it may be reasonable in the context of acute disabling stroke.

N1 versus N2 and PMN-MDSC: A critical appraisal of current concepts on tumor-associated neutrophils and new directions for human oncology

Research on tumor-associated neutrophils (TAN) currently surges because of the well-documented strong clinical relevance of tumor-infiltrating neutrophils. This relevance is illustrated by strong correlations between high frequencies of intratumoral neutrophils and poor outcome in the majority of human cancers. Recent high-dimensional analysis of murine neutrophils provides evidence for unexpected plasticity of neutrophils in murine models of cancer and other inflammatory non-malignant diseases. New analysis tools enable deeper insight into the process of neutrophil differentiation and maturation. These technological and scientific developments led to the description of an ever-increasing number of distinct transcriptional states and associated phenotypes in murine models of disease and more recently also in humans. At present, functional validation of these different transcriptional states and potential phenotypes in cancer is lacking. Current functional concepts on neutrophils in cancer rely mainly on the myeloid-derived suppressor cell (MDSC) concept and the dichotomous and simple N1-N2 paradigm. In this manuscript, we review the historic development of those concepts, critically evaluate these concepts against the background of our own work and provide suggestions for a refinement of current concepts in order to facilitate the transition of TAN research from experimental insight to clinical translation.

IL-6 Signaling Attenuates TNF-α Production by Plasmacytoid Dendritic Cells in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is characterized by autoimmune joint destruction with debilitating consequences. Despite treatment advancements with biologic therapies, a significant proportion of RA patients show an inadequate clinical response, and restoration of immune self-tolerance represents an unmet therapeutic need. We have previously described a tolerogenic phenotype of plasmacytoid dendritic cells (pDCs) in RA patients responding to anti-TNF-α agents. However, the molecular mechanisms involved in tolerogenic reprogramming of pDCs in RA remain elusive. In this study, guided by transcriptomic analysis of CD303+CD123+ pDCs from RA patients in remission, we revealed enhanced expression of IL-6R and its downstream signaling compared with healthy pDCs. Functional assessment demonstrated that IL-6R engagement resulted in marked reduction of TNF-α secretion by pDCs whereas intracellular TNF-α was significantly increased. Accordingly, pharmacologic inhibition of IL-6R signaling restored TNF-α secretion levels by pDCs. Mechanistic analysis demonstrated impaired activity and decreased lysosomal degradation of ADAM17 (a disintegrin and metalloproteinase 17) sheddase in pDCs, which is essential for TNF-α cleavage. Importantly, reduction of TNF-α secretion by IL-6-treated pDCs attenuated the inflammatory potential of RA patient-derived synovial fibroblasts. Collectively, these findings position pDCs as an important source of TNF-α in RA pathogenesis and unravel an anti-inflammatory mechanism of IL-6 by limiting the pDC-derived TNF-α secretion.

Distinct chromosomal “niches” in the genome of Saccharomyces cerevisiae provide the background for genomic innovation and shape the fate of gene duplicates

Nearly one third of Saccharomyces cerevisiae protein coding sequences correspond to duplicate genes, equally split between small-scale duplicates (SSD) and whole-genome duplicates (WGD). While duplicate genes have distinct properties compared to singletons, to date, there has been no systematic analysis of their positional preferences. In this work, we show that SSD and WGD genes are organized in distinct gene clusters that occupy different genomic regions, with SSD being more peripheral and WGD more centrally positioned close to centromeric chromatin. Duplicate gene clusters differ from the rest of the genome in terms of gene size and spacing, gene expression variability and regulatory complexity, properties that are also shared by singleton genes residing within them. Singletons within duplicate gene clusters have longer promoters, more complex structure and a higher number of protein–protein interactions. Particular chromatin architectures appear to be important for gene evolution, as we find SSD gene-pair co-expression to be strongly associated with the similarity of nucleosome positioning patterns. We propose that specific regions of the yeast genome provide a favourable environment for the generation and maintenance of small-scale gene duplicates, segregating them from WGD-enriched genomic domains. Our findings provide a valuable framework linking genomic innovation with positional genomic preferences.

OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis

Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analyzing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly^2.0, a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images. OnTheFly^2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform named entity recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms. Multiple files can be analyzed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein-protein and protein-chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly^2.0 currently supports 197 species and is available at http://bib.fleming.gr:3838/OnTheFly/ and http://onthefly.pavlopouloslab.info.

Erf Affects Commitment and Differentiation of Osteoprogenitor Cells in Cranial Sutures via the Retinoic Acid Pathway

ETS2 repressor factor (ERF) haploinsufficiency causes late-onset craniosynostosis (CRS) (OMIM entry 600775; CRS4) in humans, while in mice Erf insufficiency also leads to a similar multisuture synostosis phenotype preceded by mildly reduced calvarium ossification. However, neither the cell types affected nor the effects per se have been identified so far. Here, we establish an ex vivo system for the expansion of suture-derived mesenchymal stem and progenitor cells (sdMSCs) and analyze the role of Erf levels in their differentiation. Cellular data suggest that Erf insufficiency specifically decreases osteogenic differentiation of sdMSCs, resulting in the initially delayed mineralization of the calvarium. Transcriptome analysis indicates that Erf is required for efficient osteogenic lineage commitment of sdMSCs. Elevated retinoic acid catabolism due to increased levels of the cytochrome P450 superfamily member Cyp26b1 as a result of decreased Erf levels appears to be the underlying mechanism leading to defective differentiation. Exogenous addition of retinoic acid can rescue the osteogenic differentiation defect, suggesting that Erf affects cranial bone mineralization during skull development through retinoic acid gradient regulation.

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics

Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

Neurocognitive impairment and social cognition in multiple sclerosis

PURPOSE/AIM OF THE STUDY

The impairment of neurocognitive functions occurs in all subtypes of multiple sclerosis, even from the earliest stages of the disease. Commonly reported manifestations of cognitive impairment include deficits in attention, conceptual reasoning, processing efficiency, information processing speed, memory (episodic and working), verbal fluency (language), and executive functions. Multiple sclerosis patients also suffer from social cognition impairment, which affects their social functioning. The objective of the current paper is to assess the effect of neurocognitive impairment and its potential correlation with social cognition performance and impairment in multiple sclerosis patients.

MATERIALS AND METHODS

An overview of the available-to-date literature on neurocognitive impairment and social cognition performance in multiple sclerosis patients by disease subtype was performed.

RESULTS

It is not clear if social cognition impairment occurs independently or secondarily to neurocognitive impairment. There are associations of variable strengths between neurocognitive and social cognition deficits and their neural basis is increasingly investigated.

CONCLUSIONS

The prompt detection of neurocognitive predictors of social cognition impairment that may be applicable to all multiple sclerosis subtypes and intervention are crucial to prevent further neural and social cognition decline in multiple sclerosis patients.

Enhancement of Penetration of Millimeter Waves by Field Focusing Applied to Breast Cancer Detection

OBJECTIVE

The potentialities of improving the penetration of millimeter waves for breast cancer imaging are here explored.

METHODS

A field focusing technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification.

RESULTS

The theoretical results are numerically validated via the design and simulation of two circularly polarized antennas. The experimental validation of the designed antennas, using tissue-mimicking phantoms, is provided, being in good agreement with the theoretical predictions.

CONCLUSION

The possibility of focusing, within a lossy medium, the electromagnetic power at millimeter-wave frequencies is demonstrated.

SIGNIFICANCE

Field focusing can be a key for using millimeter waves for breast cancer detection.

UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

Low complexity regions in the proteins of prokaryotes perform important functional roles and are highly conserved

We provide the first high-throughput analysis of the properties and functional role of Low Complexity Regions (LCRs) in more than 1500 prokaryotic and phage proteomes. We observe that, contrary to a widespread belief based on older and sparse data, LCRs actually have a significant, persistent and highly conserved presence and role in many and diverse prokaryotes. Their specific amino acid content is linked to proteins with certain molecular functions, such as the binding of RNA, DNA, metal-ions and polysaccharides. In addition, LCRs have been repeatedly identified in very ancient, and usually highly expressed proteins of the translation machinery. At last, based on the amino acid content enriched in certain categories, we have developed a neural network web server to identify LCRs and accurately predict whether they can bind nucleic acids, metal-ions or are involved in chaperone functions. An evaluation of the tool showed that it is highly accurate for eukaryotic proteins as well.

ProteoSign: an end-user online differential proteomics statistical analysis platform

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign.

Tissue-mimicking materials for breast phantoms up to 50 GHz

Millimeter (mm)-wave imaging has been recently proposed as a new technique for breast cancer detection, based on the significant dielectric contrast between healthy and tumor tissues. Here we propose a procedure to fabricate, electromagnetically characterize and preserve realistic breast tissue-mimicking phantoms for testing mm-wave imaging prototypes. Low-cost, non-toxic and easy-to-produce mixtures made of sunflower oil, water and gelatin were prepared and their dielectric properties were for the first time measured in the (0.5-50) GHz frequency range using a coaxial probe kit. Different oil and gelatin percentages were tested. An alternative recipe based on a waste-oil hardener was also proposed. Finally, water and sunflower oil were investigated as preservation media. The mixtures electromagnetic properties were in good agreement with those of human breast ex vivo samples. By changing the ingredient concentrations or using different solidifying agents it was possible to mimic different tissue types. Besides, we show that sunflower oil represents an effective preservation medium for the developed materials. The first breast phantom mimicking a tumor mass into healthy tissues up to 50 GHz was also successfully fabricated. Results demonstrated the potential of the designed recipes to mimic breast tissues with different biological characteristics, preserving dielectric properties over time. Thus, this study represents a fundamental step towards the development of heterogeneous breast phantoms able to mimic the electromagnetic behavior of healthy and tumor tissues for mm-wave imaging applications.

Hypothesis, analysis and synthesis, it's all Greek to me

The linguistic foundations of science and technology include many terms that have been borrowed from ancient languages. In the case of terms with origins in the Greek language, the modern meaning can often differ significantly from the original one. Here we use the PubMed database to demonstrate the prevalence of words of Greek origin in the language of modern science, and call for scientists to exercise care when coining new terms.

Reproducibility of gait kinematics and kinetics in chronic stroke patients

BACKGROUND

The search for reliable techniques to assess gait in stroke patients is crucial for the design and follow-up of rehabilitation programs.

OBJECTIVE

To assess the reproducibility of kinematic and kinetic gait parameters in chronic stroke patients using a three-dimensional gait analysis system.

METHODS

Ten chronic stroke patients were assessed while walking along a 20 m walkway at their natural speed, using a gait analysis system of six infrared cameras and two force plates. Each patient performed 10 gait trials on 2 separate days. Inter-measurement agreement was assessed with the Coefficient of Multiple Correlation, while Root Mean Square Differences were used to quantify the variability of the trials.

RESULTS

The majority of kinetics and kinematics showed excellent reproducibility in all patients. Joints' power seemed to be more reliable compared with joints' angle and moment. Most parameters presented greater variability in non-paretic than the paretic leg, while they were less variable in the sagittal compared with the non-sagittal planes. Less than 10 trials were sufficient to obtain excellent reproducibility for most kinematic and kinetic parameters.

CONCLUSIONS

The reproducibility of movement assessment through three-dimensional gait analysis appears excellent in chronic stroke patients.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks

OBJECTIVE

Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks.

RESULTS

Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

DrugQuest - a text mining workflow for drug association discovery

Background

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.

Results

Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank “Description”, “Indication”, “Pharmacodynamics” and “Mechanism of Action” text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface.

Conclusions

DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest.

Annotation inconsistencies beyond sequence similarity-based function prediction - phylogeny and genome structure

The function annotation process in computational biology has increasingly shifted from the traditional characterization of individual biochemical roles of protein molecules to the system-wide detection of entire metabolic pathways and genomic structures. The so-called genome-aware methods broaden misannotation inconsistencies in genome sequences beyond protein function assignments, encompassing phylogenetic anomalies and artifactual genomic regions. We outline three categories of error propagation in databases by providing striking examples – at various levels of appreciation by the community from traditional to emerging, thus raising awareness for future solutions.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of "metagenomics", often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

Prediction of miRNA targets

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Dehydroepiandrosterone: an ancestral ligand of neurotrophin receptors

Dehydroepiandosterone (DHEA), the most abundant steroid in humans, affects multiple cellular functions of the endocrine, immune, and nervous systems. However, up to quite recently, no receptor has been described specifically for it, whereas most of its physiological actions have been attributed to its conversion to either androgens or estrogens. DHEA interacts and modulate a variety of membrane and intracellular neurotransmitter and steroid receptors. We have recently reported that DHEA protects neuronal cells against apoptosis, interacting with TrkA, the high-affinity prosurvival receptor of the neurotrophin, nerve growth factor. Intrigued by its pleiotropic effects in the nervous system of a variety of species, we have investigated the ability of DHEA to interact with the other two mammalian neurotrophin receptors, ie, the TrkB and TrkC, as well as their invertebrate counterparts (orthologs) in mollusks Lymnaea and Aplysia and in cephalochordate fish Amphioxus. Amazingly, DHEA binds to all Trk receptors, although with lower affinity by 2 orders of magnitude compared with that of the polypeptidic neurotrophins. DHEA effectively induced the first step of the TrkA and TrkC receptors activation (phosphorylation at tyrosine residues), including the vertebrate neurotrophin nonresponding invertebrate Lymnaea and Aplysia receptors. Based on our data, we hypothesize that early in evolution, DHEA may have acted as a nonspecific neurotrophic factor promoting neuronal survival. The interaction of DHEA with all types of neurotrophin receptors offers new insights into the largely unidentified mechanisms of its actions on multiple tissues and organs known to express neurotrophin receptors.

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery

SUMMARY

The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing.

AVAILABILITY

The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest.

CONTACT

g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drosophila Spag is the homolog of RNA polymerase II-associated protein 3 (RPAP3) and recruits the heat shock proteins 70 and 90 (Hsp70 and Hsp90) during the assembly of cellular machineries

The R2TP is a recently identified Hsp90 co-chaperone, composed of four proteins as follows: Pih1D1, RPAP3, and the AAA(+)-ATPases RUVBL1 and RUVBL2. In mammals, the R2TP is involved in the biogenesis of cellular machineries such as RNA polymerases, small nucleolar ribonucleoparticles and phosphatidylinositol 3-kinase-related kinases. Here, we characterize the spaghetti (spag) gene of Drosophila, the homolog of human RPAP3. This gene plays an essential function during Drosophila development. We show that Spag protein binds Drosophila orthologs of R2TP components and Hsp90, like its yeast counterpart. Unexpectedly, Spag also interacts and stimulates the chaperone activity of Hsp70. Using null mutants and flies with inducible RNAi, we show that spaghetti is necessary for the stabilization of snoRNP core proteins and target of rapamycin activity and likely the assembly of RNA polymerase II. This work highlights the strong conservation of both the HSP90/R2TP system and its clients and further shows that Spag, unlike Saccharomyces cerevisiae Tah1, performs essential functions in metazoans. Interaction of Spag with both Hsp70 and Hsp90 suggests a model whereby R2TP would accompany clients from Hsp70 to Hsp90 to facilitate their assembly into macromolecular complexes.

Biological information extraction and co-occurrence analysis

Nowadays, it is possible to identify terms corresponding to biological entities within passages in biomedical text corpora: critically, their potential relationships then need to be detected. These relationships are typically detected by co-occurrence analysis, revealing associations between bioentities through their coexistence in single sentences and/or entire abstracts. These associations implicitly define networks, whose nodes represent terms/bioentities/concepts being connected by relationship edges; edge weights might represent confidence for these semantic connections.This chapter provides a review of current methods for co-occurrence analysis, focusing on data storage, analysis, and representation. We highlight scenarios of these approaches implemented by useful tools for information extraction and knowledge inference in the field of systems biology. We illustrate the practical utility of two online resources providing services of this type-namely, STRING and BioTextQuest-concluding with a discussion of current challenges and future perspectives in the field.

Unraveling genomic variation from next generation sequencing data

Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.

Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production

Excessive pro-inflammatory cytokine production in the bone marrow has been associated with the pathogenesis of myelodysplastic syndromes. We herein investigated the involvement of toll-like receptors and their endogenous ligands in the induction/maintenance of the inflammatory process in the marrow of patients with myelodysplastic syndromes. We evaluated the expression of toll-like receptors in marrow monocytes of patients (n=27) and healthy controls (n=25) by flow-cytometry and also assessed the activation of the respective signaling using a real-time polymerase chain reaction-based array. We measured the high mobility group box-1 protein, a toll-like receptor-4 ligand, in marrow plasma and long-term bone marrow culture supernatants by an enzyme-linked immunosorbent assay and we performed cross-over experiments using marrow plasma from patients and controls in the presence/absence of a toll-like receptor-4 inhibitor to evaluate the pro-inflammatory cytokine production by chemiluminescence. We assessed the apoptotic cell clearance capacity of patients' macrophages using a fluorescence microscopy-based assay. We found over-expression of toll-like receptor-4 in patients' marrow monocytes compared to that in controls; this over-expression was associated with up-modulation of 53 genes related to the respective signaling. Incubation of patients' monocytes with autologous, but not with normal, marrow plasma resulted in over-production of pro-inflammatory cytokines, an effect that was abrogated by the toll-like receptor-4 inhibitor suggesting that the pro-inflammatory cytokine production in myelodysplastic syndromes is largely mediated through toll-like receptor-4. The levels of high mobility group box-1 protein were increased in patients' marrow plasma and culture supernatants compared to the levels in controls. Patients' macrophages displayed an impaired capacity to engulf apoptotic cells and this defect was associated with excessive release of high mobility group box-1 protein by dying cells. A primary apoptotic cell clearance defect of marrow macrophages in myelodysplastic syndromes may contribute to the induction/maintenance of the inflammatory process through aberrant release of molecules inducing toll-like receptor-4 such as high mobility group box-1 protein.

Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey

More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.

A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2

Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about the strengths or weaknesses of each particular algorithm, the fact of the matter is that they fall substantially short of capturing the full detail of physical, temporal and spatial requirements of miRNA::target-mRNA interactions. Here, we introduce a novel miRNA target prediction tool called Targetprofiler that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets. Using a large scale protein downregulation data set we validate our method and compare its performance to existing tools. We find that Targetprofiler exhibits greater correlation between computational predictions and protein downregulation and predicts experimentally verified miRNA targets more accurately than three other tools. Concurrently, we use primer extension to identify the mature sequence of a novel miRNA gene recently identified within a cancer associated genomic region and use Targetprofiler to predict its potential targets. Experimental verification of the ability of this small RNA molecule to regulate the expression of CCND2, a gene with documented oncogenic activity, confirms its functional role as a miRNA. These findings highlight the competitive advantage of our tool and its efficacy in extracting biologically significant results.

BioTextQuest: a web-based biomedical text mining suite for concept discovery

SUMMARY

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed.

AVAILABILITY

http://biotextquest.biol.ucy.ac.cy

CONTACT

vprobon@ucy.ac.cy; iliopj@med.uoc.gr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Formation of hybrid wormlike micelles upon mixing cetyl trimethylammonium bromide with poly(methyl methacrylate-co-sodium styrene sulfonate) copolymers in aqueous solution

The association of cetyltrimethylammonium bromide, CTAB, with a series of P(MMAx-co-SSNa) random copolymers of sodium styrene sulfonate (SSNa) with methyl methacrylate (MMA) was explored in aqueous solution as a function of the MMA molar content, x, of the copolymers. The polyelectrolyte/surfactant complexation in aqueous solution was verified through pyrene fluorescence probing. In addition, turbidimetry studies in dilute or more concentrated aqueous solutions elucidated the phase separation behavior of the P(MMAx-co-SSNa)/CTAB systems as a function of the copolymer composition x and the surfactant to polyelectrolyte mixing charge ratio. It is found that practically phase separation is completely suppressed within the studied mixing range when the MMA content of the copolymers is ∼30-40 mol%. For lower MMA contents the polyelectrolyte/surfactant complex separates out from water, while for higher x values the solubility limits of the copolymers in water are attained. For the intermediate MMA contents, viscoelastic systems are obtained in more concentrated polymer/surfactant solutions provided that the polyelectrolyte is fully complexed with the cationic surfactant ((1)H NMR results). Moreover, the (1)H NMR studies indicate that hybrid P(MMAx-co-SSNa)/CTAB wormlike micelles are formed in water under these conditions. Finally, it is shown that addition of salt prevents syneresis problems and facilitates the rheological investigation.

Mapping functional associations in the entire genome of Drosophila melanogaster using fusion analysis

We have previously shown that the detection of gene fusion events can contribute towards the elucidation of functional associations of proteins within entire genomes. Here we have analysed the entire genome of Drosophila melanogaster using fusion analysis and two additional constraints that improve the reliability of the predictions, viz. low sequence similarity and low degree of paralogy of the component proteins involved in a fusion event. Imposing these constraints, the total number of unique component pairs is reduced from 18 654 to a mere 220 cases, which are expected to represent some of the most reliably detected functionally associated proteins. Using additional information from sequence databases, we have been able to detect pairs of functionally associated proteins with important functions in cellular and developmental pathways, such as spermatogenesis and programmed cell death.

Gene socialization: gene order, GC content and gene silencing in Salmonella

Background

Genes of conserved order in bacterial genomes tend to evolve slower than genes whose order is not conserved. In addition, genes with a GC content lower than the GC content of the resident genome are known to be selectively silenced by the histone-like nucleoid structuring protein (H-NS) in Salmonella.

Results

In this study, we use a comparative genomics approach to demonstrate that in Salmonella, genes whose order is not conserved (or genes without homologs) in closely related bacteria possess a significantly lower average GC content in comparison to genes that preserve their relative position in the genome. Moreover, these genes are more frequently targeted by H-NS than genes that have conserved their genomic neighborhood. We also observed that duplicated genes that do not preserve their genomic neighborhood are, on average, under less selective pressure.

Conclusions

We establish a strong association between gene order, GC content and gene silencing in a model bacterial species. This analysis suggests that genes that are not under strong selective pressure (evolve faster than others) in Salmonella tend to accumulate more AT-rich mutations and are eventually silenced by H-NS. Our findings may establish new approaches for a better understanding of bacterial genome evolution and function, using information from functional and comparative genomics.

NOBLAST and JAMBLAST: New Options for BLAST and a Java Application Manager for BLAST results

NOBLAST (New Options for BLAST) is an open source program that provides a new user-friendly tabular output format for various NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn, Tblastx, Mega BLAST and Psi BLAST) without any use of a parser and provides E-value correction in case of use of segmented BLAST database. JAMBLAST using the NOBLAST output allows the user to manage, view and filter the BLAST hits using a number of selection criteria.

AVAILABILITY

A distribution package of NOBLAST and JAMBLAST including detailed installation procedure is freely available from http://sourceforge.net/projects/JAMBLAST/ and http://sourceforge.net/projects/NOBLAST.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.