The global prevalence and ethnic heterogeneity of iron-refractory iron deficiency anaemia

Novel TMPRSS6 variants and their impact on iron-refractory iron deficiency anaemia in pregnancy: A North Indian genotype phenotype study

Iron-refractory iron deficiency anaemia (IRIDA) is a rare autosomal recessive disorder, distinguished by hypochromic microcytic anaemia, low transferrin levels and inappropriately elevated hepcidin (HEPC) levels. It is caused by mutations in TMPRSS6 gene. Systematic screening of 500 pregnant women with iron deficiency anaemia having moderate to severe microcytosis with no other causes of anaemia were enrolled to rule out oral iron refractoriness. It identified a final cohort of 10 (2.15% prevalence) individuals with IRIDA phenotype. Haematological and biochemical analysis revealed significant differences between iron responders and iron non-responders, with iron non-responders showing lower haemoglobin, red blood cell count, serum iron and serum ferritin levels, along with elevated HEPC (9.47 ± 2.75 ng/mL, p = 0.0009) and erythropoietin (4.58 ± 4.07 µ/mL, p = 0.0196) levels. Genetic sequencing of the TMPRSS6 gene in this final cohort identified 10 novel variants, including seven missense and three frame-shift mutations, with four missense variants showing high functional impact defining the IRIDA phenotype. Structural analysis revealed significant damage caused by two variants (p.L83R and p.S235R). This study provides valuable insights into IRIDA among pregnant women in the Indian subcontinent, unveiling its underlying causes of unresponsiveness, genetic mechanisms and prevalence. Furthermore, research collaboration is essential to validate these findings and develop effective treatments.

ATP-Binding Cassette Transporter of Clinical Significance: Sideroblastic Anemia

The ATP-binding cassette (ABC) transporters are a vast group of 48 membrane proteins, some of which are of notable physiological and clinical importance. Some ABC transporters are involved in functions such as the transport of chloride ions, bilirubin, reproductive hormones, cholesterol, and iron. Consequently, genetic or physiological disruption in these functions is manifested in various disease processes like cystic fibrosis, Tangier disease, and sideroblastic anemia. Among other etiologies, primary sideroblastic anemia results from a genetic mutation in the ATP-binding cassette-7 (ABCB7), a member of the ABC transporter family. There are not many articles specifically tackling the disease processes caused by ABC transporters in detail. Some testing methodologies previously reported in the available literature for investigating sideroblastic anemia need updating. Here, we expound on the relevance of ABCB7 as a clinically important ABC transporter and a rare participant in the disease process of Sideroblastic anemia. The other genetic and secondary etiologies of sideroblastic anemia, which do not involve mutations in the ABCB7 protein, are also described. We review the pathophysiology, clinical course, symptoms, diagnosis, and treatment of sideroblastic anemia with a focus on modern technologies for laboratory testing.

Pathogenicity classification of missense mutations based on deep generative model

Missense mutations affect the function of human proteins and are closely associated with multiple acute and chronic diseases. The identification of disease-associated missense mutations and their classification for pathogenicity can provide insights into the genetic basis of disease and protein function. This paper proposes MLAE (Method based on LSTM-Ladder AutoEncoder), a deep learning classification model for identifying disease-associated missense mutations and classifying their pathogenicity based on the Variational AutoEncoder (VAE) framework. MLAE overcomes the limitations of the VAE framework by introducing the Ladder structure, combined with LSTM networks. This reduces the loss of original information during the transmission process, thereby making the model more effective in learning. In the experiment, MLAE classified all 27572 possible missense variants of the three input proteins with an average classification AUC of 0.941. This result provides evidence that MLAE is effective in predicting pathogenicity. Additionally, MLAE provides results for multi-label classification, with an average Hamming loss of 0.196, supporting the classification of complex variants. The proposed MLAE method provides an insightful approach to effectively capture amino acid sequence information and accurately predict the pathogenicity of mutations, thereby providing an analytical basis for the study and prevention of related diseases.