Gap junctions are clustered channels between contacting cells through which direct intercellular communication via diffusion of ions and metabolites can occur. Two hemichannels, each built up of six connexin protein subunits in the plasma membrane of adjacent cells, can dock to each other to form conduits between cells. We have recently screened mouse and human genomic data bases and have found 19 connexin (Cx) genes in the mouse genome and 20 connexin genes in the human genome. One mouse connexin gene and two human connexin genes do not appear to have orthologs in the other genome. With three exceptions, the characterized connexin genes comprise two exons whereby the complete reading frame is located on the second exon. Targeted ablation of eleven mouse connexin genes revealed basic insights into the functional diversity of the connexin gene family. In addition, the phenotypes of human genetic disorders caused by mutated connexin genes further complement our understanding of connexin functions in the human organism. In this review we compare currently identified connexin genes in both the mouse and human genome and discuss the functions of gap junctions deduced from targeted mouse mutants and human genetic disorders.

Gap junctions serve for direct intercellular communication by docking of two hemichannels in adjacent cells thereby forming conduits between the cytoplasmic compartments of adjacent cells. Connexin genes code for subunit proteins of gap junction channels and are members of large gene families in mammals. So far, 17 connexin (Cx) genes have been described and characterized in the murine genome. For most of them, orthologues in the human genome have been found (see White and Paul 1999; Manthey et al. 1999; Teubner et al. 2001; Söhl et al. 2001). We have recently performed searches for connexin genes in murine and human gene libraries available at EMBL/Heidelberg, NCBI and the Celera company that have increased the number of identified connexins to 19 in mouse and 20 in humans. For one mouse connexin gene and two human connexin genes we did not find orthologues in the other genome. Here we present a short overview on distinct connexin genes which we found in the mouse and human genome and which may include all members of this gene family, if no further connexin gene will be discovered in the remaining non-sequenced parts (about 1-5%) of the genomes.

Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0–17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today’s therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.

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