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.

Wastewater monitoring as a supplementary surveillance tool for capturing SARS-COV-2 community spread. A case study in two Greek municipalities.

A pilot study was conducted from late October 2020 until mid-April 2021, aiming to examine the association between SARS-CoV-2 RNA concentrations in untreated wastewater and recorded COVID-19 cases in two Greek municipalities. A population of Random Forest and Linear Regression Machine Learning models was trained and evaluated incorporating the concentrations of SARS-CoV-2 RNA in 111 wastewater samples collected from the inlets of two Wastewater Treatment Plants, along with physicochemical parameters of the wastewater influent. The model's predictions were adequately associated with the 7-day cumulative cases with the correlation coefficients (after 5-fold cross validation) ranging from 0.754 to 0.960 while the mean relative errors ranged from 30.42% to 59.46%. Our results provide indications that wastewater-based predictions can be applied in diverse settings and in prolonged time periods, although the accuracy of these predictions may be mitigated. Wastewater-based epidemiology can support and strengthen epidemiological surveillance.

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.

Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons.

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca-). Exhaled breath samples from 49 Ca+ patients, 36 Ca- patients and 52 healthy controls (HC) were analyzed by an SPME-GC-MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca- patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca- patients, which was not achieved by the targeted approach.

Molecular and physiological characterization of the monosaccharide transporters gene family in Medicago truncatula.

Monosaccharide transporters (MSTs) represent key components of the carbon transport and partitioning mechanisms in plants, mediating the cell-to-cell and long-distance distribution of a wide variety of monosaccharides. In this study, we performed a thorough structural, molecular, and physiological characterization of the monosaccharide transporter gene family in the model legume Medicago truncatula. The complete set of MST family members was identified with a novel bioinformatic approach. Prolonged darkness was used as a test condition to identify the relevant transcriptomic and metabolic responses combining MST transcript profiling and metabolomic analysis. Our results suggest that MSTs play a pivotal role in the efficient partitioning and utilization of sugars, and possibly in the mechanisms of carbon remobilization in nodules upon photosynthate-limiting conditions, as nodules are forced to acquire a new role as a source of both C and N.

Association of p16 (CDKN2A) polymorphisms with the development of HPV16-related precancerous lesions and cervical cancer in the Greek population.

The tumor suppressor protein p16 plays a fundamental role in cell cycle regulation and exerts a protective effect against tumor growth. Two different polymorphisms at positions 540 and 580 at the 3'UTR of exon 3 of p16 gene are implicated in several types of cancer, while their role in cervical cancer development remains rather vague. In the present study, we investigated for the impact of p16 genotypes/haplotypes on patients' vulnerability to cervical disease and examined whether these factors can be used as progression markers in the Greek population. A total of 96 HPV16 positive samples and histologically confirmed as LSIL (42 samples), HSIL (44 samples), and cervical cancer cases (10 samples) along with 50 control cases were tested. The identification of p16 polymorphisms was performed by PCR-RFLP methodology. The present analysis revealed that women with p16 540 CG/GG genotype are at a 2.7-fold higher risk of developing HPV16-associated HSIL (OR = 2.7, 95%CI: 1.01-6.6, P = 0.028). The G allele can be regarded as a risk factor of developing HSIL in the Greek population (OR = 2.7, 95%CI: 1.2-5.9, P = 0.012). Moreover, p16 polymorphism C580T is not associated with the growth of cervical lesion in Greek patients, while 540G/580C haplotype can be regarded as a risk haplotype of developing HSIL (OR = 3.67, 95%CI: 1.56-8.6, P = 0.0019). Our results demonstrated that p16 C540G polymorphism influence patients' susceptibility to more severe dysplasia and consequently this polymorphism could potentially emerge as a valuable biomarker for HSIL development in the Greek population.

Intra- and inter-serotypic recombinations in the 5΄ UTR-VP4 region of Echovirus 30 strains.

Recombination has been recognized as a major mechanism of evolution in enteroviruses. The Echovirus 30 (E-30) strain Gior was sequenced and phylogenetically compared to all available E-30 sequences to detect recombination events between the 5΄UTR and VP1 genomic regions. The comparison of phylogenetic trees of the 5΄UTR and VP1 revealed incongruences concerning strains, lineages and sub-lineages. Comparative analysis of 62 E-30 sub-genomic sequences revealed six different recombination events that almost all occurred in the same region, having a start point in the 3΄end of the 5΄ UTR and end point in VP4. The only exception was the sub-lineage of Gior for which both borders of recombination were located in the 5΄UTR. These results describe for the first time recombination events in this region in circulating EV-B strains, revealing the exact points of these recombination events, highlighting the impact of such events on the evolution and epidemiology of enteroviruses.

T-RECs: rapid and large-scale detection of recombination events among different evolutionary lineages of viral genomes.

BACKGROUND: Many computational tools that detect recombination in viruses are not adapted for the ongoing genomic revolution. A computational tool is needed, that will rapidly scan hundreds/thousands of genomes or sequence fragments and detect candidate recombination events that may later be further analyzed with more sensitive and specialized methods. RESULTS: T-RECs, a Windows based graphical tool, employs pairwise alignment of sliding windows and can perform (i) genotyping, (ii) clustering of new genomes, (iii) detect recent recombination events among different evolutionary lineages, (iv) manual inspection of detected recombination events by similarity plots and (v) annotation of genomic regions. CONCLUSIONS: T-RECs is very effective, as demonstrated by an analysis of 555 Norovirus complete genomes and 2500 sequence fragments, where a recombination hotspot was identified at the ORF1-ORF2 junction.

The Ectocarpus genome and the independent evolution of multicellularity in brown algae.

Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1). We report the 214 million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae, closely related to the kelps (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic approaches to explore these and other aspects of brown algal biology further.

Discovery Strategies of Bioactive Compounds Synthesized by Nonribosomal Peptide Synthetases and Type-I Polyketide Synthases Derived from Marine Microbiomes.

Considering that 70% of our planet's surface is covered by oceans, it is likely that undiscovered biodiversity is still enormous. A large portion of marine biodiversity consists of microbiomes. They are very attractive targets of bioprospecting because they are able to produce a vast repertoire of secondary metabolites in order to adapt in diverse environments. In many cases secondary metabolites of pharmaceutical and biotechnological interest such as nonribosomal peptides (NRPs) and polyketides (PKs) are synthesized by multimodular enzymes named nonribosomal peptide synthetases (NRPSes) and type-I polyketide synthases (PKSes-I), respectively. Novel findings regarding the mechanisms underlying NRPS and PKS evolution demonstrate how microorganisms could leverage their metabolic potential. Moreover, these findings could facilitate synthetic biology approaches leading to novel bioactive compounds. Ongoing advances in bioinformatics and next-generation sequencing (NGS) technologies are driving the discovery of NRPs and PKs derived from marine microbiomes mainly through two strategies: genome-mining and metagenomics. Microbial genomes are now sequenced at an unprecedented rate and this vast quantity of biological information can be analyzed through genome mining in order to identify gene clusters encoding NRPSes and PKSes of interest. On the other hand, metagenomics is a fast-growing research field which directly studies microbial genomes and their products present in marine environments using culture-independent approaches. The aim of this review is to examine recent developments regarding discovery strategies of bioactive compounds synthesized by NRPS and type-I PKS derived from marine microbiomes and to highlight the vast diversity of NRPSes and PKSes present in marine environments by giving examples of recently discovered bioactive compounds.

Recombination among human non-polio enteroviruses: implications for epidemiology and evolution.

Human enteroviruses (EV) belong to the Picornaviridae family and are among the most common viruses infecting humans. They consist of up to 100 immunologically and genetically distinct types: polioviruses, coxsackieviruses A and B, echoviruses, and the more recently characterized 43 EV types. Frequent recombinations and mutations in enteroviruses have been recognized as the main mechanisms for the observed high rate of evolution, thus enabling them to rapidly respond and adapt to new environmental challenges. The first signs of genetic exchanges between enteroviruses came from polioviruses many years ago, and since then recombination has been recognized, along with mutations, as the main cause for reversion of vaccine strains to neurovirulence. More recently, non-polio enteroviruses became the focus of many studies, where recombination was recognized as a frequent event and was correlated with the appearance of new enterovirus lineages and types. The accumulation of multiple inter- and intra-typic recombination events could also explain the series of successive emergences and disappearances of specific enterovirus types that could in turn explain the epidemic profile of circulation of several types. This review focuses on recombination among human non-polio enteroviruses from all four species (EV-A, EV-B, EV-C, and EV-D) and discusses the recombination effects on enterovirus epidemiology and evolution.

Evaluation and properties of the budding yeast phosphoproteome.

We have assembled a reliable phosphoproteomic data set for budding yeast Saccharomyces cerevisiae and have investigated its properties. Twelve publicly available phosphoproteome data sets were triaged to obtain a subset of high-confidence phosphorylation sites (p-sites), free of "noisy" phosphorylations. Analysis of this combined data set suggests that the inventory of phosphoproteins in yeast is close to completion, but that these proteins may have many undiscovered p-sites. Proteins involved in budding and protein kinase activity have high numbers of p-sites and are highly over-represented in the vast majority of the yeast phosphoproteome data sets. The yeast phosphoproteome is characterized by a few proteins with many p-sites and many proteins with a few p-sites. We confirm a tendency for p-sites to cluster together and find evidence that kinases may phosphorylate off-target amino acids that are within one or two residues of their cognate target. This suggests that the precise position of the phosphorylated amino acid is not a stringent requirement for regulatory fidelity. Compared with nonphosphorylated proteins, phosphoproteins are more ancient, more abundant, have longer unstructured regions, have more genetic interactions, more protein interactions, and are under tighter post-translational regulation. It appears that phosphoproteins constitute the raw material for pathway rewiring and adaptation at various evolutionary rates.

Minimisation of metabolic networks defines a new functional class of genes.

Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates. The composition of these MMNs has defined a new functional class of genes termed Network Efficiency Determinants (NEDs). These genes, whilst not essential, are very rarely eliminated in constructing an MMN, suggesting that it is difficult for metabolism to be re-routed to obviate the need for such genes. Moreover, the removal of NED genes from an MMN significantly reduces its global efficiency. Bioinformatic analyses of the NED genes have revealed that not only do these genes have more genetic interactions than the bulk of metabolic genes but their protein products also show more protein-protein interactions. In yeast, NED genes are predominantly single-copy and are highly conserved across evolutionarily distant organisms. These features confirm the importance of the NED genes to the metabolic network, including why they are so rarely excluded during minimisation.

Contribution of Microbiota to Bioactivity Exerted by Bee Bread.

Bee-collected pollen (BCP) and bee bread (BB) are honey bee products known for their beneficial biological properties. The main goal of this study was to investigate BB microbiota and its contribution to bioactivity exerted by BB. The microbiota of BB samples collected at different maturation stages was investigated via culture-independent (Next Generation Sequencing, NGS) and culture-dependent methods. Microbial communities dynamically fluctuate during BB maturation, ending in a stable microbial community structure in mature BB. Bee bread bacterial isolates were tested for phenotypes and genes implicated in the production and secretion of enzymes as well as antibacterial activity. Out of 309 bacterial isolates, 41 secreted hemicellulases, 13 cellulases, 39 amylases, 132 proteinases, 85 Coomassie brilliant blue G or R dye-degrading enzymes and 72 Malachite Green dye-degrading enzymes. Furthermore, out of 309 bacterial isolates, 42 exhibited antibacterial activity against Staphylococcus aureus, 34 against Pseudomonas aeruginosa, 47 against Salmonella enterica ser. Typhimurium and 43 against Klebsiella pneumoniae. Artificially fermented samples exerted higher antibacterial activity compared to fresh BCP, strongly indicating that BB microbiota contribute to BB antibacterial activity. Our findings suggest that BB microbiota is an underexplored source of novel antimicrobial agents and enzymes that could lead to new applications in medicine and the food industry.

Posttranslational regulation impacts the fate of duplicated genes.

Gene and genome duplications create novel genetic material on which evolution can work and have therefore been recognized as a major source of innovation for many eukaryotic lineages. Following duplication, the most likely fate is gene loss; however, a considerable fraction of duplicated genes survive. Not all genes have the same probability of survival, but it is not fully understood what evolutionary forces determine the pattern of gene retention. Here, we use genome sequence data as well as large-scale phosphoproteomics data from the baker's yeast Saccharomyces cerevisiae, which underwent a whole-genome duplication approximately 100 mya, and show that the number of phosphorylation sites on the proteins they encode is a major determinant of gene retention. Protein phosphorylation motifs are short amino acid sequences that are usually embedded within unstructured and rapidly evolving protein regions. Reciprocal loss of those ancestral sites and the gain of new ones are major drivers in the retention of the two surviving duplicates and in their acquisition of distinct functions. This way, small changes in the sequences of unstructured regions in proteins can contribute to the rapid rewiring and adaptation of regulatory networks.

Choose your partners: dimerization in eukaryotic transcription factors.

In many eukaryotic transcription factor gene families, proteins require a physical interaction with an identical molecule or with another molecule within the same family to form a functional dimer and bind DNA. Depending on the choice of partner and the cellular context, each dimer triggers a sequence of regulatory events that lead to a particular cellular fate, for example, proliferation or differentiation. Recent syntheses of genomic and functional data reveal that partner choice is not random; instead, dimerization specificities, which are strongly linked to the evolution of the protein family, apply. Our focus is on understanding these interaction specificities, their functional consequences and how they evolved. This knowledge is essential for understanding gene regulation and designing a new generation of drugs.

Mutation Profile of HPV16 L1 and L2 Genes in Different Geographic Areas.

The causal relationship between HPV and cervical cancer in association with the high prevalence of high risk HPV genotypes led to the design of HPV vaccines based on the major capsid L1 protein. In recent years, capsid protein L2 has also become a focal point in the field of vaccine research. The present review focuses on the variability of HPV16 L1 and L2 genes, emphasizing the distribution of specific amino acid changes in the epitopes of capsid proteins. Moreover, a substantial bioinformatics analysis was conducted to describe the worldwide distribution of amino acid substitutions throughout HPV16 L1, L2 proteins. Five amino acid changes (T176N, N181T; EF loop), (T266A; FG loop), (T353P, T389S; HI loop) are frequently observed in the L1 hypervariable surface loops, while two amino acid substitutions (D43E, S122P) are adjacent to L2 specific epitopes. These changes have a high prevalence in certain geographic regions. The present review suggests that the extensive analysis of the amino acid substitutions in the HPV16 L1 immunodominant loops may provide insights concerning the ability of the virus in evading host immune response in certain populations. The genetic variability of the HPV16 L1 and L2 epitopes should be extensively analyzed in a given population.

Transcriptomic Analysis of Pseudomonas aeruginosa Response to Pine Honey via RNA Sequencing Indicates Multiple Mechanisms of Antibacterial Activity.

Pine honey is a unique type of honeydew honey produced exclusively in Eastern Mediterranean countries like Greece and Turkey. Although the antioxidant and anti-inflammatory properties of pine honey are well documented, few studies have investigated so far its antibacterial activity. This study investigates the antibacterial effects of pine honey against P. aeruginosa PA14 at the molecular level using a global transcriptome approach via RNA-sequencing. Pine honey treatment was applied at sub-inhibitory concentration and short exposure time (0.5× of minimum inhibitory concentration -MIC- for 45 min). Pine honey induced the differential expression (>two-fold change and p ≤ 0.05) of 463 genes, with 274 of them being down-regulated and 189 being up-regulated. Gene ontology (GO) analysis revealed that pine honey affected a wide range of biological processes (BP). The most affected down-regulated BP GO terms were oxidation-reduction process, transmembrane transport, proteolysis, signal transduction, biosynthetic process, phenazine biosynthetic process, bacterial chemotaxis, and antibiotic biosynthetic process. The up-regulated BP terms, affected by pine honey treatment, were those related to the regulation of DNA-templated transcription, siderophore transport, and phosphorylation. Pathway analysis revealed that pine honey treatment significantly affected two-component regulatory systems, ABC transporter systems, quorum sensing, bacterial chemotaxis, biofilm formation and SOS response. These data collectively indicate that multiple mechanisms of action are implicated in antibacterial activity exerted by pine honey against P. aeruginosa.

Biological Properties of Bee Bread Collected from Apiaries Located across Greece.

Bee bread is the only fermented product of the beehive. It constitutes the main source of proteins, lipids, vitamins, and macro- and microelements in honeybee nutrition and it exerts antioxidant and antimicrobial properties, though research on these aspects has been limited so far. In this study 18 samples of Greek bee bread, two of which were monofloral, were collected during different seasons from diverse locations such as Crete and Mount Athos and were tested for their bioactivity. Samples were analyzed for their antibacterial properties, antioxidant activity, total phenolic content (TPC), and total flavonoid content (TFC). The antimicrobial activity of each sample was tested against Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Salmonella typhimurium. Our data demonstrate that all samples exert inhibitory and most of them bactericidal activity against at least two pathogens. Furthermore, all samples exert significant antioxidant activity, where the monofloral Castanea Sativa sample demonstrated superior antioxidant activity. Nevertheless, the antioxidant and antimicrobial activity were not strongly correlated. Furthermore, machine learning methods demonstrated that the palynological composition of the samples is a good predictor of their TPC and ABTS activity. This is the first study that focuses on the biological properties of Greek bee bread and demonstrates that bee bread can be considered a functional food and a possible source of novel antimicrobial compounds.

Reconstruction of ancestral protein interaction networks for the bZIP transcription factors.

As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.