ATP13A2 missense variant in Australian Cattle Dogs with late onset neuronal ceroid lipofuscinosis.

The neuronal ceroid lipofuscinoses (NCLs) are lysosomal storage disorders characterized by progressive neurodegeneration and declines in neurological functions. Pathogenic sequence variants in at least 13 genes underlie different forms of NCL, almost all of which are recessively inherited. To date 13 sequence variants in 8 canine orthologs of human NCL genes have been found to occur in 11 dog breeds in which they result in progressive neurological disorders similar to human NCLs. Canine NCLs can serve as models for preclinical evaluation of therapeutic interventions for these disorders. In most NCLs, the onset of neurological signs occurs in childhood, but some forms have adult onsets. Among these is CLN12 disease, also known as Kufor-Rakeb syndrome, PARK9, and spastic paraplegia78. These disorders result from variants in ATP13A2 which encodes a putative transmembrane ion transporter important for lysosomal function. Three Australian Cattle Dogs (a female and two of her offspring) were identified with a progressive neurological disorder with an onset of clinical signs at approximately 6 years of age. The affected dogs exhibited clinical courses and histopathology characteristic of the NCLs. Whole genome sequence analysis of one of these dogs revealed a homozygous c.1118C > T variant in ATP13A2 that predicts a nonconservative p.(Thr373Ile) amino acid substitution. All 3 affected dogs were homozygous for this variant, which was heterozygous in 42 of 394 unaffected Australian Cattle Dogs, the remainder of which were homozygous for the c.1118C allele. The high frequency of the mutant allele in this breed suggests that further screening for this variant should identify additional homozygous dogs and indicates that it would be advisable to perform such screening prior to breeding Australian Cattle Dogs.

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists' NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists' estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

Genomic, biochemical and expressional properties reveal strong conservation of the CLCA2 gene in birds and mammals.

Recent studies have revealed the dynamic and complex evolution of CLCA1 gene homologues in and between mammals and birds with a particularly high diversity in mammals. In contrast, CLCA2 has only been found as a single copy gene in mammals, to date. Furthermore, CLCA2 has only been investigated in few mammalian species but not in birds. Here, we established core genomic, protein biochemical and expressional properties of CLCA2 in several bird species and compared them with mammalian CLCA2. Chicken, turkey, quail and ostrich CLCA2 were compared to their mammalian orthologues using in silico, biochemical and expressional analyses. CLCA2 was found highly conserved not only at the level of genomic and exon architecture but also in terms of the canonical CLCA2 protein domain organization. The putatively prototypical galline CLCA2 (gCLCA2) was cloned and immunoblotting as well as immunofluorescence analyses of heterologously expressed gCLCA2 revealed protein cleavage, glycosylation patterns and anchoring in the plasma membrane similar to those of most mammalian CLCA2 orthologues. Immunohistochemistry found highly conserved CLCA2 expression in epidermal keratinocytes in all birds and mammals investigated. Our results suggest a highly conserved and likely evolutionarily indispensable role of CLCA2 in keratinocyte function. Its high degree of conservation on the genomic, biochemical and expressional levels stands in contrast to the dynamic structural complexities and proposed functional diversifications between mammalian and avian CLCA1 homologues, insinuating a significant degree of negative selection of CLCA2 orthologues among birds and mammals. Finally, and again in contrast to CLCA1, the high conservation of CLCA2 makes it a strong candidate for studying basic properties of the functionally still widely unresolved CLCA gene family.

Evolutionarily conserved properties of CLCA proteins 1, 3 and 4, as revealed by phylogenetic and biochemical studies in avian homologues.

Species-specific diversities are particular features of mammalian chloride channel regulator, calcium activated (CLCA) genes. In contrast to four complex gene clusters in mammals, only two CLCA genes appear to exist in chickens. CLCA2 is conserved in both, while only the galline CLCA1 (gCLCA1) displays close genetic distance to mammalian clusters 1, 3 and 4. In this study, sequence analyses and biochemical characterizations revealed that gCLCA1 as a putative avian prototype shares common protein domains and processing features with all mammalian CLCA homologues. It has a transmembrane (TM) domain in the carboxy terminal region and its mRNA and protein were detected in the alimentary canal, where the protein was localized in the apical membrane of enterocytes, similar to CLCA4. Both mammals and birds seem to have at least one TM domain containing CLCA protein with complex glycosylation in the apical membrane of enterocytes. However, some characteristic features of mammalian CLCA1 and 3 including entire protein secretion and expression in cell types other than enterocytes seem to be dispensable for chicken. Phylogenetic analyses including twelve bird species revealed that avian CLCA1 and mammalian CLCA3 form clades separate from a major branch containing mammalian CLCA1 and 4. Overall, our data suggest that gCLCA1 and mammalian CLCA clusters 1, 3 and 4 stem from a common ancestor which underwent complex gene diversification in mammals but not in birds.

Clinical, pathological and virological aspects of fatal West Nile virus infections in ten free-ranging goshawks (Accipiter gentilis) in Germany.

West Nile virus (WNV), a zoonotic arbovirus, is a new epizootic disease in Germany and caused increasing avian and equine mortality since its first detection in 2018. The northern goshawk (Accipiter gentilis) is highly susceptible to fatal WNV disease and thus is considered as an indicator species for WNV emergence in European countries. Therefore, information regarding clinical presentation and pathological findings is important for identifying suspect cases and initiating further virological diagnostics. Between July and September 2019, ten free-ranging goshawks were admitted to the Small Animal Clinic of the Freie Universität Berlin with later confirmed WNV infection. Clinical, pathological and virological findings are summarized in this report. All birds were presented obtunded and in poor to cachectic body condition. Most of the birds were juveniles (8/10) and females (9/10). Neurologic abnormalities were observed in all birds and included stupor (3/10), seizures (3/10), head tremor (2/10), head tilt (2/10), ataxia (2/10) and monoplegia (2/10). Concurrent diseases like aerosacculitis/pneumonia (7/10), clinical infections with Eucoleus spp. and Trichomonas spp. (3/10), trauma-related injuries (3/10) and myiasis (2/10) were found. Blood analysis results were unspecific considering concurrent diseases. Median time of survival was two days. The most common pathological findings were meningoencephalitis (9/10), myocarditis (8/10), iridocyclitis (8/8) and myositis (7/10). WNV infection was diagnosed by real-time quantitative reverse transcription polymerase chain reaction and confirmed by serology and immunohistochemistry.

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region.

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963-0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss' kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.