Evaluation of the zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level

Recruitment of ubiquitin-activating enzyme UBA1 to DNA by poly(ADP-ribose) promotes ATR signalling

The DNA damage response (DDR) ensures cellular adaptation to genotoxic insults. In the crowded environment of the nucleus, the assembly of productive DDR complexes requires multiple protein modifications. How the apical E1 ubiquitin activation enzyme UBA1 integrates spatially and temporally in the DDR remains elusive. Using a human cell-free system, we show that poly(ADP-ribose) polymerase 1 promotes the recruitment of UBA1 to DNA. We find that the association of UBA1 with poly(ADP-ribosyl)ated protein–DNA complexes is necessary for the phosphorylation replication protein A and checkpoint kinase 1 by the serine/threonine protein kinase ataxia-telangiectasia and RAD3-related, a prototypal response to DNA damage. UBA1 interacts directly with poly(ADP-ribose) via a solvent-accessible and positively charged patch conserved in the Animalia kingdom but not in Fungi. Thus, ubiquitin activation can anchor to poly(ADP-ribose)-seeded protein assemblies, ensuring the formation of functional ataxia-telangiectasia mutated and RAD3-related-signalling complexes

The t cell receptor (Trb) locus in tursiops truncatus: From sequence to structure of the alpha/beta heterodimer in the human/dolphin comparison

The bottlenose dolphin (Tursiops truncatus) belongs to the Cetartiodactyla and, similarly to other cetaceans, represents the most successful mammalian colonization of the aquatic envi-ronment. Here we report a genomic, evolutionary, and expression study of T. truncatus T cell receptor beta (TRB) genes. Although the organization of the dolphin TRB locus is similar to that of the other artiodactyl species, with three in tandem D-J-C clusters located at its 3’ end, its unique-ness is given by the reduction of the total length due essentially to the absence of duplications and to the deletions that have drastically reduced the number of the germline TRBV genes. We have analyzed the relevant mature transcripts from two subjects. The simultaneous availability of rear-ranged T cell receptor α (TRA) and TRB cDNA from the peripheral blood of one of the two speci-mens, and the human/dolphin amino acids multi-sequence alignments, allowed us to calculate the most likely interactions at the protein interface between the alpha/beta heterodimer in complex with major histocompatibility class I (MH1) protein. Interacting amino acids located in the com-plementarity-determining region according to IMGT numbering (CDR-IMGT) of the dolphin variable V-alpha and beta domains were identified. According to comparative modelization, the atom pair contact sites analysis between the human MH1 grove (G) domains and the T cell receptor (TR) V domains confirms conservation of the structure of the dolphin TR/pMH

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Inferred Allelic Variants of Immunoglobulin Receptor Genes: a system for their evaluation, documentation, and naming

Immunoglobulins or antibodies are the main effector molecules of the B-cell lineage and are encoded by hundreds of variable (V), diversity (D), and joining (J) germline genes, which recombine to generate enormous IG diversity. Recently, high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) of recombined V-(D)-J genes has offered unprecedented insights into the dynamics of IG repertoires in health and disease. Faithful biological interpretation of AIRR-seq studies depends upon the annotation of raw AIRR-seq data, using reference germline gene databases to identify the germline genes within each rearrangement. Existing reference databases are incomplete, as shown by recent AIRR-seq studies that have inferred the existence of many previously unreported polymorphisms. Completing the documentation of genetic variation in germline gene databases is therefore of crucial importance. Lymphocyte receptor genes and alleles are currently assigned by the Immunoglobulins, T cell Receptors and Major Histocompatibility Nomenclature Subcommittee of the International Union of Immunological Societies (IUIS) and managed in IMGT®, the international ImMunoGeneTics information system® (IMGT). In 2017, the IMGT Group reached agreement with a group of AIRR-seq researchers on the principles of a streamlined process for identifying and naming inferred allelic sequences, for their incorporation into IMGT®. These researchers represented the AIRR Community, a network of over 300 researchers whose objective is to promote all aspects of immunoglobulin and T-cell receptor repertoire studies, including the standardization of experimental and computational aspects of AIRR-seq data generation and analysis. The Inferred Allele Review Committee (IARC) was established by the AIRR Community to devise policies, criteria, and procedures to perform this function. Formalized evaluations of novel inferred sequences have now begun and submissions are invited via a new dedicated portal (https://ogrdb.airr-community.org). Here, we summarize recommendations developed by the IARC—focusing, to begin with, on human IGHV genes—with the goal of facilitating the acceptance of inferred allelic variants of germline IGHV genes. We believe that this initiative will improve the quality of AIRR-seq studies by facilitating the description of human IG germline gene variation, and that in time, it will expand to the documentation of TR and IG genes in many vertebrate species

Identification of phylogenetically conserved enhancer elements implicated in pancreas development in the WISP1 and CTGF orthologs

AbstractWISP1 and CTGF are members of the CCN family of growth factors encoding extracellular matrix proteins participating in several developmental and tumorigenic processes. Both are induced by the WNT signaling pathway, and microarray data suggest that expression of WISP1 and CTGF is repressed by Neurogenin3 (Ngn3 (NEUROG3)), a transcription factor directing specification of the endocrine pancreas. Single-cell reverse transcription polymerase chain reaction analysis suggested that this was a cell autonomous effect. To identify possible common regulatory networks involved in WISP1 and CTGF gene expression, their genomic regions were searched for common transcription factor motifs using a combination of in silico approaches and documented knowledge concerning pancreas development. This analysis revealed the presence of a conserved enhancer in both CTGF and WISP1 regulatory regions in 10 species covering a wide evolutionary distance. This enhancer contains binding sites for Ngn1/3 (NEUROG1/3) and transcription factors that are critically involved in pancreas development. Furthermore, it contained binding sites for three additional transcription factor families, which may indicate novel players are involved in this process