Challenges in Hypophosphatasia: Suspicion, Diagnosis, Genetics, Management, and Follow-Up.

BACKGROUND: Hypophosphatasia (HPP) is a rare genetic disorder caused by loss-of-function variants in the ALPL gene, leading to deficient tissue-nonspecific alkaline phosphatase (ALP) activity. This results in a distinctive biochemical profile marked by low serum ALP levels and elevated pyridoxal-5-phosphate (PLP). The clinical spectrum of HPP ranges from perinatal lethality to asymptomatic cases, presenting significant diagnostic and therapeutic challenges. SUMMARY: Diagnosis of HPP relies on identifying the characteristic biochemical signature (low ALP, high PLP), concomitant with skeletal (osteomalacia, rickets, pseudofracture) or extraskeletal (muscle weakness, musculoskeletal pain, dental) manifestations. Current diagnostic frameworks lack uniformity, highlighting the imperative for a standardized diagnostic approach. Molecular genetic testing plays a pivotal role in making the diagnosis of HPP, but difficulties persist in diagnosing milder cases and correlating genotypes with phenotypes. Comprehensive multidisciplinary care is indispensable, with enzyme replacement therapy (ERT) proving efficacious in severe cases and more nuanced management approaches for milder presentations. Overcoming challenges in ERT initiation, treatment response assessment, dose titrations, and long-term surveillance necessitates further refinement of management guidelines. KEY MESSAGE: Mild forms of HPP and asymptomatic carriers of pathogenic ALPL variants pose substantial diagnosis and management challenges. Developing consensus-driven guidelines is crucial to enhance clinical outcomes and patient care.

The Global ALPL gene variant classification project: Dedicated to deciphering variants.

BACKGROUND: Hypophosphatasia (HPP) is an inherited multisystem disorder predominantly affecting the mineralization of bones and teeth. HPP is caused by pathogenic variants in ALPL, which encodes tissue non-specific alkaline phosphatase (TNSALP). Variants of uncertain significance (VUS) cause diagnostic delay and uncertainty amongst patients and health care providers. RESULTS: The ALPL gene variant database (https://alplmutationdatabase.jku.at/) is an open-access archive for interpretation of the clinical significance of variants reported in ALPL. The database contains coding and non-coding variants, including single nucleotide variants, insertions/deletions and structural variants affecting coding or non-coding sequences of ALPL. Each variant in the database is displayed with details explaining the corresponding pathogenicity, and all reported genotypes and phenotypes, including references. In 2021, the ALPL gene variant classification project was established to reclassify VUS and continuously assess and update genetic, phenotypic, and functional variant information in the database. For this purpose, the database provides a unique submission system for clinicians, geneticists, genetic counselors, and researchers to submit VUS within ALPL for classification. An international, multidisciplinary consortium of HPP experts has been established to reclassify the submitted VUS using a multi-step process adhering to the stringent ACMG/AMP variant classification guidelines. These steps include a clinical phenotype assessment, deep literature research including artificial intelligence technology, molecular genetic assessment, and in-vitro functional testing of variants in a co-transfection model to measure ALP residual activity. CONCLUSION: This classification project and the ALPL gene variant database will serve the global medical community, widen the genotypic and phenotypic HPP spectrum by reporting and characterizing new ALPL variants based on ACMG/AMP criteria and thus facilitate improved genetic counseling and medical decision-making for affected patients and families. The project may also serve as a gold standard framework for multidisciplinary collaboration for variant interpretation in other rare diseases.

Avirulent phenotype promotes Bordetella pertussis adaptation to the intramacrophage environment.

Bordetella pertussis, the causative agent of whooping cough, is an extracellular, strictly human pathogen. However, it has been shown that B. pertussis cells can escape phagocytic killing and survive in macrophages upon internalization. Our time-resolved RNA-seq data suggest that B. pertussis efficiently adapts to the intramacrophage environment and responds to host bactericidal activities. We show that this adaptive response is multifaceted and, surprisingly, related to the BvgAS two-component system, a master regulator of virulence. Our results show that the expression of this regulatory circuit is downregulated upon internalization. Moreover, we demonstrate that the switch to the avirulent Bvg- phase augments a very complex process based on the adjustment of central and energy metabolism, cell wall reinforcement, maintenance of appropriate redox and metal homeostasis, and repair of damaged macromolecules. Nevertheless, not all observed effects could be simply attributed to the transition to Bvg- phase, suggesting that additional regulators are involved in the adaptation to the intramacrophage environment. Interestingly, a large number of genes required for the metabolism of sulphur were strongly modulated within macrophages. In particular, the mutant lacking two genes encoding cysteine dioxygenases displayed strongly attenuated cytotoxicity toward THP-1 cells. Collectively, our results suggest that intracellular B. pertussis cells have adopted the Bvg- mode to acclimate to the intramacrophage environment and respond to antimicrobial activities elicited by THP-1 cells. Therefore, we hypothesize that the avirulent phase represents an authentic phenotype of internalized B. pertussis cells.

Transcriptional profiling of human macrophages during infection with Bordetella pertussis.

Bordetella pertussis, a strictly human re-emerging pathogen and the causative agent of whooping cough, exploits a broad variety of virulence factors to establish efficient infection. Here, we used RNA sequencing to analyse the changes in gene expression profiles of human THP-1 macrophages resulting from B. pertussis infection. In parallel, we attempted to determine the changes in intracellular B. pertussis-specific transcriptomic profiles resulting from interaction with macrophages. Our analysis revealed that global gene expression profiles in THP-1 macrophages are extensively rewired 6 h post-infection. Among the highly expressed genes, we identified those encoding cytokines, chemokines, and transcription regulators involved in the induction of the M1 and M2 macrophage polarization programmes. Notably, several host genes involved in the control of apoptosis and inflammation which are known to be hijacked by intracellular bacterial pathogens were overexpressed upon infection. Furthermore, in silico analyses identified large temporal changes in expression of specific gene subsets involved in signalling and metabolic pathways. Despite limited numbers of the bacterial reads, we observed reduced expression of majority of virulence factors and upregulation of several transcriptional regulators during infection suggesting that intracellular B. pertussis cells switch from virulent to avirulent phase and actively adapt to intracellular environment, respectively.

MSF: Modulated Sub-graph Finder.

High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterizing of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and most of the methods rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. To this end, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information and identify the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https://github.com/Modulated-Subgraph-Finder/MSF.