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Rare, biallelic loss-of-function mutations in DOCK8 result in a combined immune deficiency characterized by severe and recurrent cutaneous infections, eczema, allergies, and susceptibility to malignancy, as well as impaired humoral and cellular immunity and hyper-IgE. The advent of next-generation sequencing technologies has enabled the rapid molecular diagnosis of rare monogenic diseases, including inborn errors of immunity. These advances have resulted in the implementation of gene-guided treatments, such as hematopoietic stem cell transplant for DOCK8 deficiency. However, putative disease-causing variants revealed by next-generation sequencing need rigorous validation to demonstrate pathogenicity. Here, we report the eventual diagnosis of DOCK8 deficiency in a consanguineous family due to a novel homozygous intronic deletion variant that caused aberrant exon splicing and subsequent loss of expression of DOCK8 protein. Remarkably, the causative variant was not initially detected by clinical whole-genome sequencing but was subsequently identified and validated by combining advanced genomic analysis, RNA-seq, and flow cytometry. This case highlights the need to adopt multipronged confirmatory approaches to definitively solve complex genetic cases that result from variants outside protein-coding exons and conventional splice sites., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

It has become increasingly evident that T cell functions are subject to translational control in addition to transcriptional regulation. Here, by using live imaging of CD8 + T cells isolated from the Lifeact-EGFP mouse, we show that T cells exhibit a gain in fluorescence intensity following engagement of cognate tumour target cells. The GFP signal increase is governed by Erk1/2-dependent distal T cell receptor (TCR) signalling and its magnitude correlates with IFN-γ and TNF-α production, which are hallmarks of T cell activation. Enhanced fluorescence was due to increased translation of Lifeact-EGFP protein, without an associated increase in its mRNA. Activation-induced gains in fluorescence were also observed in naïve and CD4 + T cells from the Lifeact-EGFP reporter, and were readily detected by both flow cytometry and live cell microscopy. This unique, translationally controlled reporter of effector T cell activation simultaneously enables tracking of cell morphology, F-actin dynamics and activation state in individual migrating T cells. It is a valuable addition to the limited number of reporters of T cell dynamics and activation, and opens the door to studies of translational activity and heterogeneities in functional T cell responses in situ ., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

There is a current imperative to unravel the hierarchy of molecular pathways that drive the transition of early to established disease in rheumatoid arthritis (RA). Herein, we report a comprehensive RNA sequencing analysis of the molecular pathways that drive early RA progression in the disease tissue (synovium), comparing matched peripheral blood RNA-seq in a large cohort of early treatment-naive patients, namely, the Pathobiology of Early Arthritis Cohort (PEAC). We developed a data exploration website (https://peac.hpc.qmul.ac.uk/) to dissect gene signatures across synovial and blood compartments, integrated with deep phenotypic profiling. We identified transcriptional subgroups in synovium linked to three distinct pathotypes: fibroblastic pauci-immune pathotype, macrophage-rich diffuse-myeloid pathotype, and a lympho-myeloid pathotype characterized by infiltration of lymphocytes and myeloid cells. This is suggestive of divergent pathogenic pathways or activation disease states. Pro-myeloid inflammatory synovial gene signatures correlated with clinical response to initial drug therapy, whereas plasma cell genes identified a poor prognosis subgroup with progressive structural damage., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Peg10 (paternally expressed gene 10) is an imprinted gene that is essential for placental development. It is thought to derive from a Ty3-gyspy LTR (long terminal repeat) retrotransposon and retains Gag and Pol-like domains. Here we show that the Gag domain of PEG10 can promote vesicle budding similar to the HIV p24 Gag protein. Expressed in a subset of mouse endocrine organs in addition to the placenta, PEG10 was identified as a substrate of the deubiquitinating enzyme USP9X. Consistent with PEG10 having a critical role in placental development, PEG10-deficient trophoblast stem cells (TSCs) exhibited impaired differentiation into placental lineages. PEG10 expressed in wild-type, differentiating TSCs was bound to many cellular RNAs including Hbegf (Heparin-binding EGF-like growth factor), which is known to play an important role in placentation. Expression of Hbegf was reduced in PEG10-deficient TSCs suggesting that PEG10 might bind to and stabilize RNAs that are critical for normal placental development., Competing Interests: Authors MA, EV, HB, PL, DSK, RR, SK, JDW, SG, MR, KRA, RJN, MR-G, ZM, KN, and VMD were employees of Genentech. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All reagents generated at Genentech will be available through the Genentech MTA program. There are no patents, products in development or marketed products to declare.

The NLRC4 inflammasome recognizes bacterial flagellin and components of the type III secretion apparatus. NLRC4 stimulation leads to caspase-1 activation followed by a rapid lytic cell death known as pyroptosis. NLRC4 is linked to pathogen-free auto-inflammatory diseases, suggesting a role for NLRC4 in sterile inflammation. Here, we show that NLRC4 activates an alternative cell death program morphologically similar to apoptosis in caspase-1-deficient BMDMs. By performing an unbiased genome-wide CRISPR/Cas9 screen with subsequent validation studies in gene-targeted mice, we highlight a critical role for caspase-8 and ASC adaptor in an alternative apoptotic pathway downstream of NLRC4. Furthermore, caspase-1 catalytically dead knock-in (Casp1 C284A KI) BMDMs genetically segregate pyroptosis and apoptosis, and confirm that caspase-1 does not functionally compete with ASC for NLRC4 interactions. We show that NLRC4/caspase-8-mediated apoptotic cells eventually undergo plasma cell membrane damage in vitro, suggesting that this pathway can lead to secondary necrosis. Unexpectedly, we found that DFNA5/GSDME, a member of the pore-forming gasdermin family, is dispensable for the secondary necrosis that follows NLRC4-mediated apoptosis in macrophages. Together, our data confirm the existence of an alternative caspase-8 activation pathway diverging from the NLRC4 inflammasome in primary macrophages.

Members of the ISWI family of chromatin remodelers mobilize nucleosomes to control DNA accessibility and, in some cases, are required for recovery from DNA damage. However, it remains poorly understood how the non-catalytic ISWI subunits BAZ1A and BAZ1B might contact chromatin to direct the ATPase SMARCA5. Here, we find that the plant homeodomain of BAZ1A, but not that of BAZ1B, has the unusual function of binding DNA. Furthermore, the BAZ1A bromodomain has a non-canonical gatekeeper residue and binds relatively weakly to acetylated histone peptides. Using CRISPR-Cas9-mediated genome editing we find that BAZ1A and BAZ1B each recruit SMARCA5 to sites of damaged chromatin and promote survival. Genetic engineering of structure-designed bromodomain and plant homeodomain mutants reveals that reader modules of BAZ1A and BAZ1B, even when non-standard, are critical for DNA damage recovery in part by regulating ISWI factors loading at DNA lesions and supporting transcriptional programs required for survival.ISWI chromatin remodelers regulate DNA accessibility and have been implicated in DNA damage repair. Here, the authors uncover functions, in response to DNA damage, for the bromodomain of the ISWI subunit BAZ1B and for the non-canonical PHD and bromodomain modules of the paralog BAZ1A.

Alveolar type II (AT2) cell dysfunction contributes to a number of significant human pathologies including respiratory distress syndrome, lung adenocarcinoma, and debilitating fibrotic diseases, but the critical transcription factors that maintain AT2 cell identity are unknown. Here we show that the E26 transformation-specific (ETS) family transcription factor Etv5 is essential to maintain AT2 cell identity. Deletion of Etv5 from AT2 cells produced gene and protein signatures characteristic of differentiated alveolar type I (AT1) cells. Consistent with a defect in the AT2 stem cell population, Etv5 deficiency markedly reduced recovery following bleomycin-induced lung injury. Lung tumorigenesis driven by mutant KrasG12D was also compromised by Etv5 deficiency. ERK activation downstream of Ras was found to stabilize Etv5 through inactivation of the cullin-RING ubiquitin ligase CRL4 COP1/DET1 that targets Etv5 for proteasomal degradation. These findings identify Etv5 as a critical output of Ras signaling in AT2 cells, contributing to both lung homeostasis and tumor initiation.

A variety of signals finely tune insulin secretion by pancreatic β cells to prevent both hyper-and hypoglycemic states. Here, we show that post-translational regulation of the transcription factors ETV1, ETV4, and ETV5 by the ubiquitin ligase COP1 (also called RFWD2) in β cells is critical for insulin secretion. Mice lacking COP1 in β cells developed diabetes due to insulin granule docking defects that were fully rescued by genetic deletion of Etv1, Etv4, and Etv5. Genes regulated by ETV1, ETV4, or ETV5 in the absence of mouse COP1 were enriched in human diabetes-associated genes, suggesting that they also influence human β-cell pathophysiology. In normal β cells, ETV4 was stabilized upon membrane depolarization and limited insulin secretion under hyperglycemic conditions. Collectively, our data reveal that ETVs negatively regulate insulin secretion for the maintenance of normoglycemia., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Unlabelled: Connections between disease phenotypes and drug effects can be made by identifying commonalities in the associated patterns of differential gene expression. Searchable databases that record the impacts of chemical or genetic perturbations on the transcriptome--here referred to as 'connectivity maps'--permit discovery of such commonalities. We describe two R packages, gCMAP and gCMAPWeb, which provide a complete framework to construct and query connectivity maps assembled from user-defined collections of differential gene expression data. Microarray or RNAseq data are processed in a standardized way, and results can be interrogated using various well-established gene set enrichment methods. The packages also feature an easy-to-deploy web application that facilitates reproducible research through automatic generation of graphical and tabular reports., Availability and Implementation: The gCMAP and gCMAPWeb R packages are freely available for UNIX, Windows and Mac OS X operating systems at Bioconductor (http://www.bioconductor.org).

Introduction: Rheumatoid arthritis (RA) is a complex and clinically heterogeneous autoimmune disease. Currently, the relationship between pathogenic molecular drivers of disease in RA and therapeutic response is poorly understood., Methods: We analyzed synovial tissue samples from two RA cohorts of 49 and 20 patients using a combination of global gene expression, histologic and cellular analyses, and analysis of gene expression data from two further publicly available RA cohorts. To identify candidate serum biomarkers that correspond to differential synovial biology and clinical response to targeted therapies, we performed pre-treatment biomarker analysis compared with therapeutic outcome at week 24 in serum samples from 198 patients from the ADACTA (ADalimumab ACTemrA) phase 4 trial of tocilizumab (anti-IL-6R) monotherapy versus adalimumab (anti-TNFα) monotherapy., Results: We documented evidence for four major phenotypes of RA synovium - lymphoid, myeloid, low inflammatory, and fibroid - each with distinct underlying gene expression signatures. We observed that baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor European league against rheumatism (EULAR) clinical response to anti-TNFα therapy at week 16 (P =0.011). We observed that high baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had differential relationships with clinical response to anti-TNFα compared with anti-IL6R treatment. sICAM1-high/CXCL13-low patients showed the highest week 24 American College of Rheumatology (ACR) 50 response rate to anti-TNFα treatment as compared with sICAM1-low/CXCL13-high patients (42% versus 13%, respectively, P =0.05) while anti-IL-6R patients showed the opposite relationship with these biomarker subgroups (ACR50 20% versus 69%, P =0.004)., Conclusions: These data demonstrate that underlying molecular and cellular heterogeneity in RA impacts clinical outcome to therapies targeting different biological pathways, with patients with the myeloid phenotype exhibiting the most robust response to anti-TNFα. These data suggest a path to identify and validate serum biomarkers that predict response to targeted therapies in rheumatoid arthritis and possibly other autoimmune diseases., Trial Registration: ClinicalTrials.gov NCT01119859

Objective: Personalised healthcare is contingent on the identification of biomarkers that represent disease relevant pathways and predict drug response. The authors aimed to develop a gene expression signature in synovial tissue that could enrich clinical response of rheumatoid arthritis (RA) patients to rituximab., Methods: The authors studied synovial gene expression using high-throughput quantitative real-time-PCR in 20 RA patients who underwent arthroscopy before and after treatment with rituximab. Several objective approaches were used to explore patterns in the data and to find genes associated with changes in disease activity due to treatment., Results: This analysis revealed two patient populations associated with distinct clinical, laboratory and histological features and, importantly, showed enrichment for response (60% non-responders vs 90% responders). A composite baseline gene score (GS) correlated with change in disease activity score (ΔDAS) between baseline and month 3 (r=0.74, p=0.0002), but also with ΔDAS at later time-points (month 9, r=0.54, p=0.016; month 15, r=0.45, p=0.06; month 21, r=0.72, p=0.003). Notably, the GS significantly correlated with baseline erythrocyte sedimentation rate (r=0.69, p=0.0008), but not with other DAS components. The GS genes represented T cell, macrophage, remodelling and interferon-α biology. Responders demonstrated higher expression of macrophage and T cell genes, while non-responders showed higher expression of interferon-α and remodelling genes., Conclusions: This study reveals a baseline synovial GS that correlates with early and late clinical responses to rituximab. The GS biology suggests that T cells and macrophages are important for response to B cell depleting therapy, while expression of remodelling and interferon-α genes correlates with poor response.

Background: Domains are the building blocks of proteins. During evolution, they have been duplicated, fused and recombined, to produce proteins with novel structures and functions. Structural and genome-scale studies have shown that pairs or groups of domains observed together in a protein are almost always found in only one N to C terminal order and are the result of a single recombination event that has been propagated by duplication of the multi-domain unit. Previous studies of domain organisation have used graph theory to represent the co-occurrence of domains within proteins. We build on this approach by adding directionality to the graphs and connecting nodes based on their relative order in the protein. Most of the time, the linear order of domains is conserved. However, using the directed graph representation we have identified non-linear features of domain organization that are over-represented in genomes. Recognising these patterns and unravelling how they have arisen may allow us to understand the functional relationships between domains and understand how the protein repertoire has evolved., Results: We identify groups of domains that are not linearly conserved, but instead have been shuffled during evolution so that they occur in multiple different orders. We consider 192 genomes across all three kingdoms of life and use domain and protein annotation to understand their functional significance. To identify these features and assess their statistical significance, we represent the linear order of domains in proteins as a directed graph and apply graph theoretical methods. We describe two higher-order patterns of domain organisation: clusters and bi-directionally associated domain pairs and explore their functional importance and phylogenetic conservation., Conclusion: Taking into account the order of domains, we have derived a novel picture of global protein organization. We found that all genomes have a higher than expected degree of clustering and more domain pairs in forward and reverse orientation in different proteins relative to random graphs with identical degree distributions. While these features were statistically over-represented, they are still fairly rare. Looking in detail at the proteins involved, we found strong functional relationships within each cluster. In addition, the domains tended to be involved in protein-protein interaction and are able to function as independent structural units. A particularly striking example was the human Jak-STAT signalling pathway which makes use of a set of domains in a range of orders and orientations to provide nuanced signaling functionality. This illustrated the importance of functional and structural constraints (or lack thereof) on domain organisation.

Advances in techniques for the study of protein-protein interactions have dramatically improved our understanding of the interactome. However, we know little about the dynamics of this complex system. To better understand the dynamics of the interactome, it is important to consider what happens when single proteins are perturbed. Changes in protein abundance and post-translational modification can function as switches in the interactome, affecting protein-complex assembly and function. Changes in protein sequence or a dramatic increase in abundance might cause a promiscuous gain of interactions. These effects are not identical for all proteins and will differ depending on the number and type of interaction partners that a protein has.

DNA-binding domain (DBD) is a database of predicted sequence-specific DNA-binding transcription factors (TFs) for all publicly available proteomes. The proteomes have increased from 150 in the initial version of DBD to over 700 in the current version. All predicted TFs must contain a significant match to a hidden Markov model representing a sequence-specific DNA-binding domain family. Access to TF predictions is provided through http://transcriptionfactor.org, where new search options are now provided such as searching by gene names in model organisms, searching for all proteins in a particular DBD family and specific organism. We illustrate the application of this type of search facility by contrasting trends of DBD family occurrence throughout the tree of life, highlighting the clear partition between eukaryotic and prokaryotic DBD expansions. The website content has been expanded to include dedicated pages for each TF containing domain assignment details, gene names, links to external databases and links to TFs with similar domain arrangements. We compare the increase in number of predicted TFs with proteome size in eukaryotes and prokaryotes. Eukaryotes follow a slower rate of increase in TFs than prokaryotes, which could be due to the presence of splice variants or an increase in combinatorial control.

We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues. Some tissues displayed large transcriptional differences in old mice, suggesting that these tissues may contribute strongly to organismal decline. Other tissues showed few or no changes in expression with age, indicating strong levels of homeostasis throughout life. Based on the pattern of age-related transcriptional changes, we found that tissues could be classified into one of three aging processes: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for steroid-responsive tissues. We observed that different tissues age in a coordinated fashion in individual mice, such that certain mice exhibit rapid aging, whereas others exhibit slow aging for multiple tissues. Finally, we compared the transcriptional profiles for aging in mice to those from humans, flies, and worms. We found that genes involved in the electron transport chain show common age regulation in all four species, indicating that these genes may be exceptionally good markers of aging. However, we saw no overall correlation of age regulation between mice and humans, suggesting that aging processes in mice and humans may be fundamentally different., Competing Interests: Competing interests. The authors have declared that no competing interests exist.

Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of 'transcription factor'. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD (www.transcriptionfactor.org) consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

Several recent studies indicate that mammals and other organisms produce large numbers of RNA transcripts that do not correspond to known genes. It has been suggested that these transcripts do not encode proteins, but may instead function as RNAs. However, discrimination of coding and non-coding transcripts is not straightforward, and different laboratories have used different methods, whose ability to perform this discrimination is unclear. In this study, we examine ten bioinformatic methods that assess protein-coding potential and compare their ability and congruency in the discrimination of non-coding from coding sequences, based on four underlying principles: open reading frame size, sequence similarity to known proteins or protein domains, statistical models of protein-coding sequence, and synonymous versus non-synonymous substitution rates. Despite these different approaches, the methods show broad concordance, suggesting that coding and non-coding transcripts can, in general, be reliably discriminated, and that many of the recently discovered extra-genic transcripts are indeed non-coding. Comparison of the methods indicates reasons for unreliable predictions, and approaches to increase confidence further. Conversely and surprisingly, our analyses also provide evidence that as much as approximately 10% of entries in the manually curated protein database Swiss-Prot are erroneous translations of actually non-coding transcripts.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

A comparison of gene content and genome architecture of Trypanosoma brucei, Trypanosoma cruzi, and Leishmania major, three related pathogens with different life cycles and disease pathology, revealed a conserved core proteome of about 6200 genes in large syntenic polycistronic gene clusters. Many species-specific genes, especially large surface antigen families, occur at nonsyntenic chromosome-internal and subtelomeric regions. Retroelements, structural RNAs, and gene family expansion are often associated with syntenic discontinuities that-along with gene divergence, acquisition and loss, and rearrangement within the syntenic regions-have shaped the genomes of each parasite. Contrary to recent reports, our analyses reveal no evidence that these species are descended from an ancestor that contained a photosynthetic endosymbiont.

Proteins are composed of domains, which are conserved evolutionary units that often also correspond to functional units and can frequently be detected with reasonable reliability using computational methods. Most proteins consist of two or more domains, giving rise to a variety of combinations of domains. Another level of complexity arises because proteins themselves can form complexes with small molecules, nucleic acids and other proteins. The networks of both domain combinations and protein interactions can be conceptualised as graphs, and these graphs can be analysed conveniently by computational methods. In this review we summarise facts and hypotheses about the evolution of domains in multi-domain proteins and protein complexes, and the tools and data resources available to study them.

During evolution genes can produce more complex proteins by gene fusion or less complex proteins by gene fission. Considering proteins from 131 completely sequenced genomes from all three kingdoms of life, we identified 2869 groups of multi-domain proteins as a single protein in certain organisms and as two or more smaller proteins with equivalent domain architectures in other organisms. We found that fusion events are approximately four times more common than fission events, and we established that, in most cases, any particular fusion or fission event only occurred once during the course of evolution.

The SUPERFAMILY database provides structural assignments to protein sequences and a framework for analysis of the results. At the core of the database is a library of profile Hidden Markov Models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent an entire superfamily. We have applied the library to predicted proteins from all completely sequenced genomes (currently 154), the Swiss-Prot and TrEMBL databases and other sequence collections. Close to 60% of all proteins have at least one match, and one half of all residues are covered by assignments. All models and full results are available for download and online browsing at http://supfam.org. Users can study the distribution of their superfamily of interest across all completely sequenced genomes, investigate with which other superfamilies it combines and retrieve proteins in which it occurs. Alternatively, concentrating on a particular genome as a whole, it is possible first, to find out its superfamily composition, and secondly, to compare it with that of other genomes to detect superfamilies that are over- or under-represented. In addition, the webserver provides the following standard services: sequence search; keyword search for genomes, superfamilies and sequence identifiers; and multiple alignment of genomic, PDB and custom sequences.

Previous mapping studies examinig the distribution and pattern of staining for connexin-43 expression (the major ventricular gap junction protein) across the ventricular wall have yielded variable findings. The aim of this study was to determine if variations in the distribution of connexin-43 were due to histochemical detection problems, i.e. cross-linking of antigenic sites as a consequence of aldehyde fixation and/or due to low levels of protein expression within the epicardial or endocardial regions of the heart. Immunoperoxidase staining of connexin-43 using the ABC method was carried out in crosssections of rabbit hearts at the level of the papillary muscle. The following treatments were examined: the antibody (Ab) only, Ab with 1/2 Tyramide Signal Amplification (TSA) or full TSA; antibody with microwave antigen retrieval (AR); Ab + 1/2 TSA + AR and finally Ab + TSA + AR. Under light microscopy and using computerized image analysis the percentages of ventricular cross-sectional transmural staining for the different treatment groups were calculated: Ab amounted to only 55%; Ab + 1/2 TSA 63%; Ab + TSA 78%; Ab + AR 72%; Ab + AR + 1/2 TSA 72% and Ab + AR + TSA 88%. The percentages of transumural connexin-43 staining in both TSA + Ab and Ab + TSA + AR groups when compared to Ab only were significantly greater p < 0.01. The antigenic cross-linking due to aldehyde fixation and low levels expression of connexin-43 are contributing factors that influence the immunohistochemical detection of connexin-43 in the mammalian heart. Methodological enhancement for the detection of connexin-43 in this study was derived primarily from amplification of low background levels of connexin-43 being expressed using the TSA protocol. This is supported by the significant differences encountered when TSA was utilized in the protocol and compared with antibody treatment only.

Intragenic duplication is an evolutionary process where segments of a gene become duplicated. While there has been much research into whole-gene or domain duplication, there have been very few studies of non-tandem intragenic duplication. The identification of intragenically replicated sequences may provide insight into the evolution of proteins, helping to link sequence data with structure and function. This paper describes a tool for autonomously modelling intragenic duplication. AMID provides: identification of modularly repetitive genes; an algorithm for identifying repeated modules; and a scoring system for evaluating the modules' similarity. An evaluation of the algorithms and use cases are presented.

Previous studies have suggested that the jaggedness of the healed or healing infarct edge influences cardiac electrical stability. However, these findings have been based on histological observations rather than quantitative measurements. The aim of this study was to assess infarct jaggedness by calculating its fractal dimension and to examine how this influences cardiac electrical stability during late infarct healing in the rabbit. Using programmed electrical stimulation, it was found that the fractal dimension did not differ significantly in 19 rabbits that had inducible ventricular tachycardia and 16 that did not. We conclude from these studies in the mature rabbit that infarct edge jaggedness does not influence the ease with which ventricular tachycardia is induced during late myocardial infarct healing.

This study describes the use of a shareware software package available from the National Institutes of Health for computing the fractal dimension. Specifically, when fractal analysis is used in its correct context it provides for a quantitative description of the space filling properties of two-dimensional objects. A rabbit model of post myocardial infarction is described where the cross-sectional infarct edge is reconstructed and its jaggedness determined by calculating its fractal dimension via the pixel dilation method. The fractal dimensions of the anterior and posterior lateral infarct edges were calculated to have a mean of 1.16 and 1.29, respectively. In conclusion, the fractal technique can be used to describe the complex jaggedness of the infarct edge. This case study also illustrates the fact that the complexity of an infarcted area is not uniform across the scar. For example, we found that the space filling properties of the anterior and posterior borders of a myocardial infarct can differ by more than 2-fold.