Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation.

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.

DREAM: an R package for druggability evaluation of human complex diseases.

MOTIVATION: De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. RESULTS: In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. AVAILABILITY AND IMPLEMENTATION: DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles.

MOTIVATION: Transcriptomic data can be used to describe the mechanism of action (MOA) of a chemical compound. However, omics data tend to be complex and prone to noise, making the comparison of different datasets challenging. Often, transcriptomic profiles are compared at the level of individual gene expression values, or sets of differentially expressed genes. Such approaches can suffer from underlying technical and biological variance, such as the biological system exposed on or the machine/method used to measure gene expression data, technical errors and further neglect the relationships between the genes. We propose a network mapping approach for knowledge-driven comparison of transcriptomic profiles (KNeMAP), which combines genes into similarity groups based on multiple levels of prior information, hence adding a higher-level view onto the individual gene view. When comparing KNeMAP with fold change (expression) based and deregulated gene set-based methods, KNeMAP was able to group compounds with higher accuracy with respect to prior information as well as is less prone to noise corrupted data. RESULT: We applied KNeMAP to analyze the Connectivity Map dataset, where the gene expression changes of three cell lines were analyzed after treatment with 676 drugs as well as the Fortino et al. dataset where two cell lines with 31 nanomaterials were analyzed. Although the expression profiles across the biological systems are highly different, KNeMAP was able to identify sets of compounds that induce similar molecular responses when exposed on the same biological system. AVAILABILITY AND IMPLEMENTATION: Relevant data and the KNeMAP function is available at: https://github.com/fhaive/KNeMAP and 10.5281/zenodo.7334711.

ESPERANTO: a GLP-field sEmi-SuPERvised toxicogenomics metadAta curatioN TOol.

SUMMARY: Biological data repositories are an invaluable source of publicly available research evidence. Unfortunately, the lack of convergence of the scientific community on a common metadata annotation strategy has resulted in large amounts of data with low FAIRness (Findable, Accessible, Interoperable and Reusable). The possibility of generating high-quality insights from their integration relies on data curation, which is typically an error-prone process while also being expensive in terms of time and human labour. Here, we present ESPERANTO, an innovative framework that enables a standardized semi-supervised harmonization and integration of toxicogenomics metadata and increases their FAIRness in a Good Laboratory Practice-compliant fashion. The harmonization across metadata is guaranteed with the definition of an ad hoc vocabulary. The tool interface is designed to support the user in metadata harmonization in a user-friendly manner, regardless of the background and the type of expertise. AVAILABILITY AND IMPLEMENTATION: ESPERANTO and its user manual are freely available for academic purposes at https://github.com/fhaive/esperanto. The input and the results showcased in Supplementary File S1 are available at the same link.

Integrative network analysis suggests prioritised drugs for atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery. METHODS: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD. RESULTS: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD. CONCLUSIONS: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.

Analysis of phenotypic changes in high temperature and low pH extreme conditions of Alicyclobacillus sendaiensis PA2 related with the cell wall and sporulation genes.

The A. sendaiensis PA2 is a polyextremophile bacterium. In this study, we analyze the A. sendaiensis PA2 genome. The genome was assembled and annotated. The A. sendaiensis PA2 genome structure consists of a 2,956,928 bp long chromosome and 62.77% of G + C content. 3056 CDSs were predicted, and 2921 genes were assigned to a putative function. The ANIm and ANIb value resulted in 97.17% and 96.65%, the DDH value was 75.5%, and the value of TETRA (Z-score) was 0.98. Comparative genomic analyses indicated that three systems are enriched in A. sendaiensis PA2. This strain has phenotypic changes in cell wall during batch culture at 65 °C, pH 5.0 and without carbon and nitrogen source. The presence of unique genes of cell wall and sporulation subsystem could be related to the adaptation of A. sendaiensis PA2 to hostile conditions.

The Growing Role of Telerehabilitation and Teleassessment in the Management of Movement Disorders in Rare Neurological Diseases: A Scoping Review.

Background: People with rare neurological diseases (RNDs) often experience symptoms related to movement disorders, requiring a multidisciplinary approach, including rehabilitation. Telemedicine applied to rehabilitation and symptom monitoring may be suitable to ensure treatment consistency and personalized intervention. The objective of this scoping review aimed to emphasize the potential role of telerehabilitation and teleassessment in managing movement disorders within RNDs. By providing a systematic overview of the available literature, we sought to highlight potential interventions, outcomes, and critical issues. Methods: A literature search was conducted on PubMed, Google Scholar, IEEE, and Scopus up to March 2024. Two inclusion criteria were followed: (1) papers focusing on telerehabilitation and teleassessment and (2) papers dealing with movement disorders in RNDs. Results: Eighteen papers fulfilled the inclusion criteria. The main interventions were home-based software and training programs, exergames, wearable sensors, smartphone applications, virtual reality and digital music players for telerehabilitation; wearable sensors, mobile applications, and patient home video for teleassessment. Key findings revealed positive outcomes in gait, balance, limb disability, and in remote monitoring. Limitations include small sample sizes, short intervention durations, and the lack of standardized protocols. Conclusion: This review highlighted the potential of telerehabilitation and teleassessment in addressing movement disorders within RNDs. Data indicate that these modalities may play a major role in supporting conventional programs. Addressing limitations through multicenter studies, longer-term follow-ups, and standardized protocols is essential. These measures are essential for improving remote rehabilitation and assessment, contributing to an improved quality of life for people with RNDs.

Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype.

BACKGROUND: B-raf inhibitors (BRAFi) are effective for BRAF-mutated papillary (PTC) and anaplastic (ATC) thyroid carcinomas, although acquired resistance impairs tumour cells' sensitivity and/or limits drug efficacy. Targeting metabolic vulnerabilities is emerging as powerful approach in cancer. METHODS: In silico analyses identified metabolic gene signatures and Hif-1α as glycolysis regulator in PTC. BRAF-mutated PTC, ATC and control thyroid cell lines were exposed to HIF1A siRNAs or chemical/drug treatments (CoCl2, EGF, HGF, BRAFi, MEKi and diclofenac). Genes/proteins expression, glucose uptake, lactate quantification and viability assays were used to investigate the metabolic vulnerability of BRAF-mutated cells. RESULTS: A specific metabolic gene signature was identified as a hallmark of BRAF-mutated tumours, which display a glycolytic phenotype, characterised by enhanced glucose uptake, lactate efflux and increased expression of Hif-1α-modulated glycolytic genes. Indeed, Hif-1α stabilisation counteracts the inhibitory effects of BRAFi on these genes and on cell viability. Interestingly, targeting metabolic routes with BRAFi and diclofenac combination we could restrain the glycolytic phenotype and synergistically reduce tumour cells' viability. CONCLUSION: The identification of a metabolic vulnerability of BRAF-mutated carcinomas and the capacity BRAFi and diclofenac combination to target metabolism open new therapeutic perspectives in maximising drug efficacy and reducing the onset of secondary resistance and drug-related toxicity.

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis.

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

VOLTA: adVanced mOLecular neTwork Analysis.

MOTIVATION: Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. RESULTS: We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a 'plug and play' system is provided. AVAILABILITY AND IMPLEMENTATION: The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prokaryotic diversity across a pH gradient in the "El Chichón" crater-lake: a naturally thermo-acidic environment.

The "El Chichón" crater-lake in Mexico is a thermo-acidic environment whose microorganisms have been scarcely studied. In this study, we surveyed the prokaryotic communities by amplicon sequencing of the 16S rRNA gene considering samples of sediment and water collected within a pH/temperature gradient (pH 1.9-5.1, 38-89 °C). Further, we interpreted these results within a physicochemical context. The composition of the microbial assemblage differed significantly between the sediments and water. Sediment communities were different in the site with the highest temperature and lower pH value compared to the other ones sampled, while those in the water were relatively similar at all points. Most of the genera found were related to Alicyclobacillus, Acinetobacter, Bacillus, Mesoaciditoga, Methanothermobacter, Desulfitobacterium, Therminicanus, Kyrpidia, Paenibacillus, Thermoanaerobacterium, and Gelria. Some of these genera are known by their thermo-acidic tolerant capacities with flexible metabolisms to use diverse electron donor/acceptors (S or Fe), while others are thermo(acid)philes that mainly occur in the most extreme sites of the lake. These results show the presence of a microbial community adapted to the changing conditions of a very dynamic crater-lake, that include chemoorganotrophs and chemolithotrophs.

European Academy of Neurology guidance for developing and reporting clinical practice guidelines on rare neurological diseases.

BACKGROUND AND PURPOSE: Rare diseases affect up to 29 million people in the European Union, and almost 50% of them affect the nervous system or muscles. Delays in diagnosis and treatment onset and insufficient treatment choices are common. Clinical practice guidelines (CPGs) may improve the diagnosis and treatment of patients and optimize care pathways, delivering the best scientific evidence to all clinicians treating these patients. Recommendations are set for developing and reporting high-quality CPGs on rare neurological diseases (RNDs) within the European Academy of Neurology (EAN), through a consensus procedure. METHODS: A group of 27 experts generated an initial list of items that were evaluated through a two-step Delphi consensus procedure and a face-to-face meeting. The final list of items was reviewed by an external review group of 58 members. RESULTS: The consensus procedure yielded 63 final items. Items are listed according to the domains of the AGREE instruments and concern scope and purpose, stakeholder involvement, rigour of development, and applicability. Additional items consider reporting and ethical issues. Recommendations are supported by practical examples derived from published guidelines and are presented in two tables: (1) items specific to RND CPGs, and general guideline items of special importance for RNDs, or often neglected; (2) items for guideline development within the EAN. CONCLUSIONS: This guidance aims to provide solutions to the issues specific to RNDs. This consensus document, produced by many experts in various fields, is considered to serve as a starting point for further harmonization and for increasing the quality of CPGs in the field of RNDs.

Neurological music therapy during the COVID-19 outbreak: updates and future challenges.

BACKGROUND: In the last few years, several studies confirmed the effectiveness of music therapy (MT) for the rehabilitative management of patients with neurological disorders. AIM: Here we discuss the feasibility and disadvantages of tele-neurological MT (tele-NMT) compared to the traditional MT programmes. METHODS: We selected all the articles registered in the Web of Knowledge, PubMed, Google Scholar and ScienceDirect from March 2020 to November 2021 concerning tele-NMT during the COVID-19 outbreak, collecting same examples and experiences. RESULTS: With the advent of the COVID-19, several music-based interventions (MBIs) have been adapted from "in person" to a "remote and virtual" mode (through the telemedicine). DISCUSSION: Tele-NMT could represent a promising option to provide constant care and support to people with neurological diseases during the pandemic.

A case of spastic paraplegia type 11 mimicking a GM2-gangliosidosis.

INTRODUCTION: Spastic paraplegia type 11 (SPG11) is the most frequent autosomal recessive HSP. Studies on SPG11 patients' fibroblasts, post-mortem brains, and mouse models revealed endolysosomal system dysfunction and lipid accumulation, especially gangliosides. We report a patient with early clinical findings mimicking a GM2-gangliosidosis. METHODS: A clinical, biochemical, and metabolic characterization was performed. Electron microscopy analysis was completed on rectal mucosa and skin biopsy specimens. A NGS panel of genes associated to neuronal ceroid lipofuscinosis and HSP was analyzed. RESULTS: The patient presented with worsening walking difficulty and psychomotor slowdown since childhood; to exclude a neurometabolic storage disease, skin and rectal biopsies were performed: enteric neurons showed lipofuscin-like intracellular inclusions, thus suggesting a possible GM2-gangliosidosis. However, further analysis did not allow to confirm such hypothesis. In adulthood we detected flaccid paraplegia, nystagmus, axonal motor neuropathy, carpus callosum atrophy, and colon atony. Surprisingly, the NGS panel detected two already reported SPG11 mutations in compound heterozygosity. CONCLUSIONS: We describe for the first time pathological hallmarks of SPG11 in enteric neuron from a rectal mucosa biopsy. The report illustrates the possible overlap between SPG11 and GM2-gangliosidosis, especially in the first disease phases and helps to improve our knowledge about SPG11 physiopathology.

POLR3A variants in hereditary spastic paraparesis and ataxia: clinical, genetic, and neuroradiological findings in a cohort of Italian patients.

Mutations in POLR3A are characterized by high phenotypic heterogeneity, with manifestations ranging from severe childhood-onset hypomyelinating leukodystrophic syndromes to milder and later-onset gait disorders with central hypomyelination, with or without additional non-neurological signs. Recently, a milder phenotype consisting of late-onset spastic ataxia without hypomyelinating leukodystrophy has been suggested to be specific to the intronic c.1909 + 22G > A mutation in POLR3A. Here, we present 10 patients from 8 unrelated families with POLR3A-related late-onset spastic ataxia, all harboring the c.1909 + 22G > A variant. Most of them showed an ataxic-spastic picture, two a "pure" cerebellar phenotype, and one a "pure" spastic presentation. The non-neurological findings typically associated with POLR3A mutations were absent in all the patients. The main findings on brain MRI were bilateral hyperintensity along the superior cerebellar peduncles on FLAIR sequences, observed in most of the patients, and cerebellar and/or spinal cord atrophy, found in half of the patients. Only one patient exhibited central hypomyelination. The POLR3A mutations present in this cohort were the c.1909 + 22G > A splice site variant found in compound heterozygosity with six additional variants (three missense, two nonsense, one splice) and, in one patient, with a novel large deletion involving exons 14-18. Interestingly, this patient had the most "complex" presentation among those observed in our cohort; it included some neurological and non-neurological features, such as seizures, neurosensory deafness, and lipomas, that have not previously been reported in association with late-onset POLR3A-related disorders, and therefore further expand the phenotype.

Volatile Compounds in Tofu Obtained by Soy Milk Fermentation with Lactobacillus plantarum BAL-03-ITTG and Lactobacillus fermentum BAL-21-ITTG.

Tofu is one of the main foods made with soybeans. The aim of this work was to evaluate the effect of L. plantarum and L. fermentum on the volatile compounds and sensorial profile of fermented tofu during ripening. The soy milk was fermented separately with two native strains (L. plantarum or L. fermentum) until reaching a pH of 5.5, and the fermented tofu was obtained. The tofu obtained by acidification with lactic acid was used as a control and was characterized by microbial survival (L. plantarum, L. fermentum, and P. freudenreichii) for 0, 20, and 40 days of storage at 15 °C. Moreover, the lactic and acetic acid content was determined by high-performance liquid chromatography (HPLC), and the volatile compounds were evaluated by gas. Chromatography-mass spectrometry (GC-MS). The results were analyzed by an ANOVA test (P < 0.05). After storage, the lactic acid bacteria (LAB) survived in the fermented tofu at a concentration higher than 8.0 log CFU/g after 40 days of storage. The shelf life of fermented tofu obtained by acidification was fewer than 20 days because of the presence of fungi and yeasts. The hexanal content was reduced by approximately 96% (P < 0.05) in the tofu obtained by fermentation compared with the control. This process for fermented tofu production employing two native strains could be used for industrial purposes.

CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events.

BACKGROUND: The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. RESULTS: Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. CONCLUSIONS: CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs .

Expanding the clinical and genetic spectrum of pathogenic variants in STIM1.

INTRODUCTION/AIMS: Stromal interaction molecule 1 (STIM1) is a reticular Ca2+ sensor composed of a luminal and a cytosolic domain. Autosomal dominant mutations in STIM1 cause tubular aggregate myopathy and Stormorken syndrome or its variant York platelet syndrome. In this study we aimed to expand the features related to new variants in STIM1. METHODS: We performed a cross-sectional study of individuals harboring monoallelic STIM1 variants recruited at five tertiary centers involved in a study of inherited myopathies analyzed with a multigene-targeted panel. RESULTS: We identified seven individuals (age range, 26-57 years) harboring variants in STIM1, including five novel changes: three located in the EF-hand domain, one in the sterile α motif (SAM) domain, and one in the cytoplasmatic region of the protein. Functional evaluation of the pathogenic variants using a heterologous expression system and measuring store-operated calcium entry demonstrated their causative role and suggested a link of new variants with the clinical phenotype. Muscle contractures, found in three individuals, showed variability in body distribution and in the number of joints involved. Three patients showed cardiac and respiratory involvement. Short stature, hyposplenism, sensorineural hearing loss, hypothyroidism, and Gilbert syndrome were variably observed among the patients. Laboratory tests revealed hyperCKemia in six patients, thrombocytopenia in two patients, and hypocalcemia in one patient. Muscle biopsy showed the presence of tubular aggregates in three patients, type I fiber atrophy in one patient, and nonspecific myopathic changes in two patients. DISCUSSION: Our clinical, histological, and molecular data expand the genetic and clinical spectrum of STIM1-related diseases.