Elif Everest

Objective: Deficiency of adenosine deaminase type 2 (DADA2) is an autosomal recessive autoinflammatory disease caused by biallelic pathogenic variations in the adenosine deaminase type 2 (ADA2) gene. The authors aimed to measure adenosine deaminase (ADA) activity in the patients with ADA2 variants and determine the consequences of the identified variants on protein stability. Methods: The authors measured the total ADA activity in the peripheral blood mononuclear cells of 8 patients with ADA2 gene variants and 5 healthy controls using an ADA Activity Assay Kit. Additionally, the authors conducted a computational study involving stability analyses, 100 ns molecular dynamics simulations, and molecular mechanics Poisson–Boltzmann surface area analyses. Results: Five patients had the homozygous NM_001282225.2:c.139G>A (p.Gly47Arg) variant, 2 patients had 2 heterozygous variants NM_001282225.2:c.139G>A(;)962G>A (p.Gly47Arg(;) Gly321Glu), and 1 of the patients had a novel heterozygous variant NM_001282225.2:c.467C>T (p.Ser156Leu) in the ADA2 gene. Total ADA activity was significantly lower in patients than in healthy controls (P = .0009). Moreover, ADA activity was tended to be lower in patients possessing 2 heterozygous variants c.139G>A(;)962G>A (p.Gly47Arg(;)Gly321Glu), than in patients with homozygous c.139G>A (p.Gly47Arg) (statistically not significant). The computational analysis investigating the combined impact of c.139G>A (p.Gly47Arg) and c.962G>A (p.Gly321Glu) on the catalytic activity of ADA2 showed that these variants, particularly when identified in the same patient, can interfere with dimerization of ADA2 protein. Conclusion: Altogether, these results suggest that although both variants contribute altered ADA2 activity, c.962G>A (p.Gly321Glu) variant has a more prominent effect on the catalytic activity of ADA2 and in the pathophysiology of DADA2. Further, distinct ADA2 genotypes and overlapping phenotypic features highlight the complexity of genotype–phenotype correlations in DADA2.

Background: Multiple sclerosis (MS) has a complex pathophysiology, variable clinical presentation, and unpredictable prognosis; understanding the underlying mechanisms requires combinatorial approaches that warrant the integration of diverse molecular omics data. Methods: Here, we combined genomic and proteomic data of the same individuals among a Turkish MS patient group to search for biologically important networks. We previously identified differentially-expressed proteins by cerebrospinal fluid proteome analysis of 179 MS patients and 42 non-MS controls. Among this study group, 11 unrelated MS patients and 60 independent, healthy controls were subjected to whole-genome SNP genotyping, and genome-wide associations were assessed. Pathway enrichment analyses of MS-associated SNPs and differentially-expressed proteins were conducted using the functional enrichment tool, PANOGA. Results: Nine shared pathways were detected between the genomic and proteomic datasets after merging and clustering the enriched pathways. Complement and coagulation cascade was the most significantly associated pathway (hsa04610, P = 6.96 × 10⁻³⁰). Other pathways involved in neurological or immunological mechanisms included adherens junctions (hsa04520, P = 6.64 × 10⁻²⁵), pathogenic Escherichia coli infection (hsa05130, P = 9.03 × 10⁻¹⁴), prion diseases (hsa05020, P = 5.13 × 10⁻¹³). Conclusion: We conclude that integrating multiple datasets of the same patients helps reducing false negative and positive results of genome-wide SNP associations and highlights the most prominent cellular players among the complex pathophysiological mechanisms.

The second affiliation of the corresponding author Eda Tahir Turanlı was incorrectly published as İstanbul Medeniyet University instead of Istanbul Technical University.

Systemic autoinflammatory diseases (sAIDs) are a heterogeneous group of disorders, having monogenic inherited forms with overlapping clinical manifestations. More than half of patients do not carry any pathogenic variant in formerly associated disease genes. Here, we report a cross-sectional study on targeted Next-Generation Sequencing (NGS) screening in patients with suspected sAIDs to determine the diagnostic utility of genetic screening. Fifteen autoinflammation/immune-related genes (ADA2-CARD14-IL10RA-LPIN2-MEFV-MVK-NLRC4-NLRP12-NLRP3-NOD2-PLCG2-PSTPIP1-SLC29A3-TMEM173-TNFRSF1A) were used to screen 196 subjects from adult/pediatric clinics, each with an initial clinical suspicion of one or more sAID diagnosis with the exclusion of typical familial Mediterranean fever (FMF) patients. Following the genetic screening, 140 patients (71.4%) were clinically followed-up and re-evaluated. Fifty rare variants in 41 patients (20.9%) were classified as pathogenic or likely pathogenic and 32 of those variants were located on the MEFV gene. We detected pathogenic or likely pathogenic variants compatible with the final diagnoses and inheritance patterns in 14/140 (10%) of patients for the following sAIDs: familial Mediterranean fever (n = 7), deficiency of adenosine deaminase 2 (n = 2), mevalonate kinase deficiency (n = 2), Muckle–Wells syndrome (n = 1), Majeed syndrome (n = 1), and STING-associated vasculopathy with onset in infancy (n = 1). Targeted NGS panels have impact on diagnosing rare monogenic sAIDs for a group of patients. We suggest that MEFV gene screening should be first-tier genetic testing especially in regions with high carrier rates. Clinical utility of multi-gene testing in sAIDs was as low as expected, but extensive genome-wide familial analyses in combination with exome screening would enlighten additional genetic factors causing disease.

Juvenile idiopathic arthritis [JIA; Mendelian Inheritance in Man (MIM) 604302] is the most common chronic childhood arthritis, characterized by chronic articular findings of unknown origin, with heterogeneity in disease course and systemic involvement1,2. Epidemiologic studies based on different diagnostic criteria showed varying prevalence from 0.07 to 4.01 per 1000 children across populations, an increased risk for European descendants, and different subtype distributions among ethnic groups3,4. JIA is generally known as a complex genetic trait with non-Mendelian inheritance pattern possibly resulting from interactions of multiple genetic loci and environmental factors5. However, several studies have reported causative variants in the Laccase (multicopper oxidoreductase) domain-containing 1 ( LACC1 ; MIM 613409) gene in rare familial forms6,7,8. We present 17 patients (10 males and 7 females) from 7 families (5 with known parental consanguinity), each with 2–4 affected members diagnosed with JIA. This study was approved by the Institutional Review Board of Istanbul Technical University (MBG.22/2014) and carried out in compliance with the Declaration Helsinki. … Address correspondence to Dr. E. Tahir Turanlı, Dr. Orhan Öcalgiray Molecular Biology-Biotechnology and Genetics Research Centre, Istanbul Technical University, Ayazağa Campus, 34469 Maslak, Istanbul, Turkey. E-mail: turanlie{at}itu.edu.tr

Pediatric rheumatology includes autoinflammatory monogenic diseases, autoinflammatory multifactorial diseases with complex inheritance, and diseases with uncertain clinical diagnosis or undefined conditions, even though they show signs of autoinflammation. Most of these diseases are systemic; it is important to diagnose patients promptly and definitively and to select proper treatment options based on the diagnoses. Clinical observation and acute-phase responses are usually sufficient for diagnosis; however, genetic analyses can provide supportive data for definite diagnosis and treatment, especially for rare monogenic diseases. As for multifactorial autoinflammatory diseases, susceptibility genes, and factors involved in the etiopathogenesis have not been fully identified. It is possible to identify disease genes and novel diseases, and lead to new treatment options by gene mapping studies and high-throughput screening strategies for multifactorial diseases and conditions with uncertain clinical characteristics. In this review, we discuss the three groups of autoinflammatory diseases and role of genetics in their diagnosis.

The function of gene body DNA methylation in alternative splicing, and its relation to disease pathogenesis is not fully elucidated. The gene for familial Mediterranean fever (MEFV) encodes the pyrin protein and contains a 998 bp CpG island, covering the second exon, which is differentially methylated in FMF patients compared to healthy controls. Our further observation of increased exon 2-spliced MEFV transcript in leukocytes of FMF patients provoked us to test the role of exon methylation in alternative splicing using inflammatory cell culture models. First, in vitro exon methylation triggered an increased level of exon 2 exclusion using a splicing cassette in a promyelocytic leukemia cell line (HL-60). HL-60 cells subjected to methylating and demethylating agents, as well as cells differentiated to neutrophil-like cells, exhibited different levels of spliced/unspliced transcripts. We observed increased levels of spliced transcripts in neutrophil-like (p = 0.0005), activated (p = 0.0034) and methylated cells (p < 0.0001), whereas decreased levels in demethylated cells (p = 0.0126) compared to control untreated HL-60 cells. We also showed that the protein isoform of pyrin lacking the exon 2 has an adverse subcellular localization in neutrophil-like cells. Therefore, it remains in the cytoplasm rather than the nucleus. This may point to an epigenetic involvement in an important inflammatory gene.

Genome-wide association studies have identified shared genetic risk loci between schizophrenia (SCZ) and multiple sclerosis (MS), suggesting overlapping biological mechanisms despite distinct clinical presentations. Here, we aimed to characterize the biological plausibility of KIF21B as a shared genetic risk gene for SCZ and MS, two disorders with overlapping genetic risk loci despite distinct clinical presentations. We performed bioinformatics analyses of KIF21B using publicly available data from GTEx, STRING, and the GWAS Catalog, including tissue-specific expression profiling, protein interaction network analysis, transcriptome-wide co-expression analysis, and phenome-wide association analysis. KIF21B expression was significantly enriched in both brain and immune tissues compared to other tissue types. Protein interaction network analysis demonstrated that KIF21B functions within microtubule-based transport machinery essential for axonal function. Co-expression analysis identified significant correlation with immune-related genes, including the established MS susceptibility gene ANKRD55, with pathway enrichment for T cell activation and lymphocyte diferentiation. Phenome-wide association analysis confirmed KIF21B pleiotropy across multiple autoimmune and psychiatric conditions. These findings support KIF21B as a biologically plausible shared risk gene whose dual neural and immune functions may contribute to susceptibility for both neuropsychiatric and autoimmune diseases of the central nervous system.

This narrative review examines clinical, genetic, and epidemiological evidence on schizophrenia (SCZ) to identify predictors of self-harm and violence, summarize genetic contributors to SCZ including pleiotropy and links between polygenic burden and negative/disorganized symptoms, and evaluate implications for early identification and predictive tools. We synthesized findings from large-scale genome-wide association studies, rare variant and polygenic risk research, gene–environment interplay, epidemiological studies on suicide and violence, and clinical trials of early-intervention programs for SCZ. SCZ involves a complex genetic architecture that includes hundreds of common risk variants and rarer coding and structural mutations. These genetic factors interact with environmental exposures, particularly childhood adversity and substance use, to shape vulnerability trajectories. Suicidality is a leading cause of excess mortality in SCZ, especially in early disease phases. Violence risk is modest overall but elevated in individuals with untreated psychosis or comorbid substance misuse. Emerging findings suggest that polygenic burden is linked to negative and disorganized symptoms, which in turn associate with worse clinical outcomes. Early, sustained, multicomponent intervention improves symptoms, functioning, and treatment adherence and may reduce self-harm and indirectly mitigate violence by addressing modifiable risk factors. Emerging machine learning models, though not yet widely validated, show promise in identifying individuals at higher risk of suicide or aggression by integrating clinical, demographic, and biological features. Integrating genetic, clinical, and environmental data can enhance risk stratification and support precision prevention in SCZ. Polygenic risk scores are not yet clinically predictive alone but may add value when combined with symptom profiles and early-life adversity for early identification and detecting susceptibility to negative/disorganized symptoms. Near-term priorities include externally validating prediction tools, standardizing screening for trauma exposure and substance use within early-psychosis services to guide monitoring intensity and relapse-prevention planning, and scaling coordinated specialty care.