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Acute kidney injury (AKI) is a relevant complication following thoracoabdominal aortic aneurysm repair (TAAA). Biomarkers, such as secretory leucocyte peptidase inhibitor (SLPI), may enable a more accurate diagnosis. In this study, we tested if SLPI measured in serum is an appropriate biomarker of AKI after TAAA repair. In a prospective observational single-center study including 33 patients (51.5% women, mean age 63.0 ± 16.2 years) undergoing open and endovascular aortic aneurysm repair in 2017, SLPI was measured peri-operatively (until 72 h after surgery). After surgery, the postoperative complications AKI, as defined according to the KDIGO diagnostic criteria, sepsis, death, MACE (major cardiovascular events) and, pneumonia were assessed. In a subgroup analysis, patients with preexisting kidney disease were excluded. Of 33 patients, 51.5% ( n = 17) of patients developed AKI. Twelve hours after admission to the intensive care unit (ICU), SLPI serum levels were significantly increased in patients who developed AKI. Multivariable logistic regression revealed a significant association between SLPI 12 hours after admission to ICU and AKI ( P = 0.0181, OR = 1.055, 95% CI = 1.009–1.103). The sensitivity of SLPI for AKI prediction was 76.47% (95% CI = 50.1–93.2) and the specificity was 87.5% (95% CI = 61.7–98.4) with an AUC = 0.838 (95% CI = 0.7–0.976) for an optimal cut-off 70.03 ng/ml 12 hours after surgery. In patients without pre-existing impaired renal function, an improved diagnostic quality of SLPI for AKI was observed (Sensitivities of 45.45–91.67%, Specificities of 77.7–100%, AUC = 0.716–0.932). There was no association between perioperative SLPI and the incidence of sepsis, death, MACE (major cardiovascular events), pneumonia. This study suggests that SLPI might be a post-operative biomarker of AKI after TAAA repair, with a superior diagnostic accuracy for patients without preexisting impaired renal function.

Pathogenic variants in aminoacyl-tRNA synthetases (ARS1) cause a diverse spectrum of autosomal recessive disorders. Tyrosyl tRNA synthetase (TyrRS) is encoded by YARS1 (cytosolic, OMIM*603,623) and is responsible of coupling tyrosine to its specific tRNA. Next to the enzymatic domain, TyrRS has two additional functional domains (N-Terminal TyrRSMini and C-terminal EMAP-II-like domain) which confer cytokine-like functions. Mutations in YARS1 have been associated with autosomal-dominant Charcot-Marie-Tooth (CMT) neuropathy type C and a heterogenous group of autosomal recessive, multisystem diseases. We identified 12 individuals from 6 families with the recurrent homozygous missense variant c.1099C > T;p.(Arg367Trp) (NM_003680.3) in YARS1. This variant causes a multisystem disorder with developmental delay, microcephaly, failure to thrive, short stature, muscular hypotonia, ataxia, brain anomalies, microcytic anemia, hepatomegaly, and hypothyroidism. In silico analyses show that the p.(Arg367Trp) does not affect the catalytic domain responsible of enzymatic coupling, but destabilizes the cytokine-like C-terminal domain. The phenotype associated with p.(Arg367Trp) is distinct from the other biallelic pathogenic variants that reside in different functional domains of TyrRS which all show some common, but also divergent clinical signs [(e.g., p.(Phe269Ser)—retinal anomalies, p.(Pro213Leu)/p.(Gly525Arg)—mild ID, p.(Pro167Thr)—high fatality)]. The diverse clinical spectrum of ARS1-associated disorders is related to mutations affecting the various non-canonical domains of ARS1, and impaired protein translation is likely not the exclusive disease-causing mechanism of YARS1- and ARS1-associated neurodevelopmental disorders.Key messagesThe missense variant p.(Arg367Trp) in YARS1 causes a distinct multisystem disorder.p.(Arg367Trp) affects a non-canonical domain with cytokine-like functions.Phenotypic heterogeneity associates with the different affected YARS1 domains.Impaired protein translation is likely not the exclusive mechanism of ARS1-associated disorders.

D‐dopachrome tautomerase (D‐DT/MIF‐2) is a member of the macrophage migration inhibitory factor (MIF) cytokine superfamily, and a close structural homolog of MIF. MIF and D‐DT have been reported to be involved in obesity, but there is little known about the regulation of D‐DT in adipose tissue inflammation and wound healing. Subcutaneous adipose tissue was collected from 54 healthy donors and 28 donors with acutely inflamed wounds undergoing wound debridement. In addition, epididymal fat pads of mice were injected with lipopolysaccharide to study receptor expression and cell migration in vivo. D‐DT protein levels and mRNA expression were significantly decreased in subcutaneous adipose tissue adjacent to acutely inflamed wounds. D‐DT improved fibroblast viability and increased proliferation in vitro. While D‐DT alone did not have a significant effect on in vitro fibroblast wound healing, simultaneous addition of neutralizing MIF antibody resulted in a significant improvement of fibroblast wound healing. Interestingly, expression of the MIF and D‐DT receptor CD74 was down‐regulated while the MIF receptors CXCR2 and CXCR4 were up‐regulated primarily on macrophages indicating that the MIF‐CXCR2/4 axis may promote recruitment of inflammatory cells into adipose tissue. Our results describe a reciprocal role of D‐DT to MIF in inflamed adipose tissue, and indicate that D‐DT may be beneficial in wound repair by improving fibroblast survival and proliferation.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this ‘supervised’ approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes. GestaltMatcher uses a deep convolutional neural network to improve recognition of rare disorders based on facial morphology. The framework detects similarities among patients with previously unseen syndromes, aiding discovery of new disease genes.

BackgroundFetal akinesia (FA) results in variable clinical presentations and has been associated with more than 166 different disease loci. However, the underlying molecular cause remains unclear in many individuals. We aimed to further define the set of genes involved.MethodsWe performed in-depth clinical characterisation and exome sequencing on a cohort of 23 FA index cases sharing arthrogryposis as a common feature.ResultsWe identified likely pathogenic or pathogenic variants in 12 different established disease genes explaining the disease phenotype in 13 index cases and report 12 novel variants. In the unsolved families, a search for recessive-type variants affecting the same gene was performed; and in five affected fetuses of two unrelated families, a homozygous loss-of-function variant in the kinesin family member 21A gene (KIF21A) was found.ConclusionOur study underlines the broad locus heterogeneity of FA with well-established and atypical genotype–phenotype associations. We describe KIF21A as a new factor implicated in the pathogenesis of severe neurogenic FA sequence with arthrogryposis of multiple joints, pulmonary hypoplasia and facial dysmorphisms. This hypothesis is further corroborated by a recent report on overlapping phenotypes observed in Kif21a null piglets.

Acute kidney injury (AKI) is one of the most frequent complications after cardiac surgery and is associated with poor outcomes. Biomarkers of AKI are crucial for the early diagnosis of this condition. Secretory leukocyte protease inhibitor (SLPI) is an alarm anti-protease that has been implicated in the pathogenesis of AKI but has not yet been studied as a diagnostic biomarker of AKI. Using two independent cohorts (development cohort (DC), n = 60; validation cohort (VC), n = 148), we investigated the performance of SLPI as a diagnostic marker of AKI after cardiac surgery. Serum and urinary levels of SLPI were quantified by ELISA. SLPI was significantly elevated in AKI patients compared with non-AKI patients (6 h, DC: 102.1 vs. 64.9 ng/mL, p < 0.001). The area under the receiver operating characteristic curve of serum SLPI 6 h after surgery was 0.87 ((0.76–0.97); DC). The addition of SLPI to standard clinical predictors significantly improved the predictive accuracy of AKI (24 h, VC: odds ratio (OR) = 3.91 (1.44–12.13)). In a subgroup, the increase in serum SLPI was evident before AKI was diagnosed on the basis of serum creatinine or urine output (24 h, VC: OR = 4.89 (1.54–19.92)). In this study, SLPI was identified as a novel candidate biomarker for the early diagnosis of AKI after cardiac surgery.

Many monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 17,560 patients with 1,115 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism, as well as enable the delineation of novel phenotypes.

Background: Macrophage Migration Inhibitory Factor (MIF) is highly elevated after cardiac surgery and impacts the postoperative inflammation. The aim of this study was to analyze whether the polymorphisms CATT5–7 (rs5844572/rs3063368,“-794”) and G>C single-nucleotide polymorphism (rs755622,-173) in the MIF gene promoter are related to postoperative outcome. Methods: In 1116 patients undergoing cardiac surgery, the MIF gene polymorphisms were analyzed and serum MIF was measured by ELISA in 100 patients. Results: Patients with at least one extended repeat allele (CATT7) had a significantly higher risk of acute kidney injury (AKI) compared to others (23% vs. 13%; OR 2.01 (1.40–2.88), p = 0.0001). Carriers of CATT7 were also at higher risk of death (1.8% vs. 0.4%; OR 5.12 (0.99–33.14), p = 0.026). The GC genotype was associated with AKI (20% vs. GG/CC:13%, OR 1.71 (1.20–2.43), p = 0.003). Multivariate analyses identified CATT7 predictive for AKI (OR 2.13 (1.46–3.09), p < 0.001) and death (OR 5.58 (1.29–24.04), p = 0.021). CATT7 was associated with higher serum MIF before surgery (79.2 vs. 50.4 ng/mL, p = 0.008). Conclusion: The CATT7 allele associates with a higher risk of AKI and death after cardiac surgery, which might be related to chronically elevated serum MIF. Polymorphisms in the MIF gene may constitute a predisposition for postoperative complications and the assessment may improve risk stratification and therapeutic guidance.