Guidelines for managing and using the digital phenotypes of pluripotent stem cell lines.

Each pluripotent stem cell line has a physical entity as well as a digital phenotype, but linking the two unambiguously is confounded by poor naming practices and assumed knowledge. Registration gives each line a unique and persistent identifier that links to phenotypic data generated over the lifetime of that line. Registration is a key recommendation of the 2023 ISSCR Standards for the use of human stem cells in research. Here we consider how community adoption of stem cell line registration could facilitate the establishment of integrated digital phenotypes of specific human pluripotent stem cell (hPSC) lines.

A Standardized Nomenclature Design for Systematic Referencing and Identification of Animal Cellular Material.

The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.

The Management of Data for the Banking, Qualification, and Distribution of Induced Pluripotent Stem Cells: Lessons Learned from the European Bank for Induced Pluripotent Stem Cells.

The European Bank for induced pluripotent Stem Cells (EBiSC) was established in 2014 as a non-profit project for the banking, quality control, and distribution of human iPSC lines for research around the world. EBiSC iPSCs are deposited from diverse laboratories internationally and, hence, a key activity for EBiSC is standardising not only the iPSC lines themselves but also the data associated with them. This includes enabling unique nomenclature for the cells, as well as applying uniformity to the data provided by the cell line generator versus quality control data generated by EBiSC, and providing mechanisms to share personal data in a secure and GDPR-compliant manner. A joint approach implemented by EBiSC and the human pluripotent stem cell registry (hPSCreg®) has provided a solution that enabled hPSCreg® to improve its registration platform for iPSCs and EBiSC to have a pipeline for the import, standardisation, storage, and management of data associated with EBiSC iPSCs. In this work, we describe the experience of cell line data management for iPSC banking throughout the course of EBiSC's development as a central European banking infrastructure and present a model for how this could be implemented by other iPSC repositories to increase the FAIRness of iPSC research globally.

History and current status of clinical studies using human pluripotent stem cells.

The Human Pluripotent Stem Cell Registry established a database of clinical studies using human pluripotent stem cells (PSCs) as starting material for cell therapies. Since 2018, we have observed a switch toward human induced pluripotent stem cells (iPSCs) from human embryonic stem cells. However, rather than using iPSCs for personalized medicines, allogeneic approaches dominate. Most treatments target ophthalmopathies, and genetically modified iPSCs are used to generate tailored cells. We observe a lack of standardization and transparency about the PSCs lines used, characterization of the PSC-derived cells, and the preclinical models and assays applied to show efficacy and safety.

EBiSC best practice: How to ensure optimal generation, qualification, and distribution of iPSC lines.

Disease-relevant human induced pluripotent stem cells (iPSCs) are generated worldwide for research purposes; however, without robust and practical ethical, legal, and quality standards, there is a high risk that their true potential will not be realized. Best practices for tissue procurement, iPSC reprogramming, day-to-day cultivation, quality control, and data management aligned with an ethical and legal framework must be included into daily operations to ensure their promise is maximized. Here we discuss key learning experiences from 7 years of operating the European Bank for induced Pluripotent Stem Cells (EBiSC) and recommend how to incorporate solutions into a daily management framework.

Long-term time trend of lead exposure in young German adults - Evaluation of more than 35 Years of data of the German Environmental Specimen Bank.

Lead is a ubiquitous pollutant with well-known effects on human health. As there is no lower toxicological threshold for lead in blood and since data gaps on lead exposure still exist in many European countries, HBM data on lead is of high importance. To address this, the European Human Biomonitoring Initiative HBM4EU classified lead as a priority substance. The German Environmental Specimen Bank (German ESB) has monitored lead exposure since more than 35 years. Using data from the early 1980s to 2019 we reveal and discuss long-term trends in blood lead levels (BLLs) and current internal exposure of young adults in Germany. BLLs in young adults decreased substantially in the investigated period. As results from the ESB sampling site Muenster demonstrate, the geometric mean of BLLs of young adults decreased from 1981 (78,7 µg/L) to 2019 (10.4 µg/L) by about 87%. Trends in human exposure closely correlate with air lead levels (ALLs) provided by the European Monitoring and Evaluation Programme (EMEP). Hence, the decrease of BLLs largely reflects the drop in air lead pollution. Known associations of sex, smoking, alcohol consumption, and housing situation with BLLs are confirmed with data of the German ESB. Although internal lead exposure in Germany decreased substantially, the situation might be different in other European countries. Since 2010, BLLs of young adults in Germany levelled out at approximately 10 µg/L. The toxicity of lead even at low levels is known to cause adverse health effects especially in children following exposure of the child or the mother during pregnancy. To identify current exposure sources and to minimize future lead exposure, continuous monitoring of lead intake and exposure levels is needed.

Towards Harmonized Biobanking for Biomonitoring: A Comparison of Human Biomonitoring-Related and Clinical Biorepositories.

Human biomonitoring (HBM) depends on high-quality human samples to identify status and trends in exposure and ensure comparability of results. In this context, much effort has been put into the development of standardized processes and quality assurance for sampling and chemical analysis, while effects of sample storage and shipment on sample quality have been less thoroughly addressed. To characterize the currently applied storage and shipment procedures within the consortium of the European Human Biomonitoring Initiative (HBM4EU), which aims at harmonization of HBM in Europe, a requirement analysis based on data from an online survey was conducted. In addition, the online survey was addressed to professionals in clinical biobanking represented by members of the European, Middle Eastern and African Society for Biopreservation and Biobanking (ESBB) to identify the current state-of-the-art in terms of sample storage and shipment. Results of this survey conducted in these two networks were compared to detect processes with potential for optimization and harmonization. In general, many similarities exist in sample storage and shipment procedures applied by ESBB members and HBM4EU partners and many requirements for ensuring sample quality are already met also by HBM4EU partners. Nevertheless, a need for improvement was identified for individual steps in sample storage, shipment, and related data management with potential impact on sample and data quality for HBM purposes. Based on these findings, recommendations for crucial first steps to further strengthen sample quality, and thus foster advancement in HBM on a pan-European level are given.

Poly(amidoamine)-alginate hydrogels: directing the behavior of mesenchymal stem cells with charged hydrogel surfaces.

The surface charge of a biomaterial represents a promising tool to direct cellular behavior, which is crucial for therapeutic approaches in regenerative medicine. To expand the understanding of how the material surface charge affects protein adsorption and mesenchymal stem cell behavior, differently charged surfaces with zeta potentials spanning from -25 mV to +15 mV were fabricated by the conjugation of poly(amidoamine) to alginate-based hydrogels. We showed that the increase of the biomaterials surface charge resulted in enhanced quantities of biologically available, surface-attached proteins. Since different surface charges were equalized after protein adsorption, mesenchymal stem cells interacted rather with diverse protein compositions instead of different surface features. Besides an enhanced cell attachment to increasingly positively charged surfaces, the cell spreading area and the expression of adhesion-related genes integrin α5 and tensin 1 were found to be increased after adhesion. Moreover, first results indicate a potential impact of the surface charge on mesenchymal stem cell differentiation towards bone and fat cells. The improved understanding of surface charge-related cell behavior has significant impact on the design of biomedical devices and artificial organs.

BALL-SNPgp-from genetic variants toward computational diagnostics.

UNLABELLED: In medical research, it is crucial to understand the functional consequences of genetic alterations, for example, non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a three-dimensional visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites. AVAILABILITY AND IMPLEMENTATION: BALL-SNPgp is based on the comprehensive C ++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp CONTACT: ballsnp@milaman.cs.uni-saarland.de.

From Single Variants to Protein Cascades: MULTISCALE MODELING OF SINGLE NUCLEOTIDE VARIANT SETS IN GENETIC DISORDERS.

Understanding the role of genetics in disease has become a central part of medical research. Non-synonymous single nucleotide variants (nsSNVs) in coding regions of human genes frequently lead to pathological phenotypes. Beyond single variations, the individual combination of nsSNVs may add to pathogenic processes. We developed a multiscale pipeline to systematically analyze the existence of quantitative effects of multiple nsSNVs and gene combinations in single individuals on pathogenicity. Based on this pipeline, we detected in a data set of 842 nsSNVs discovered in 76 genes related to cardiomyopathies, associated nsSNV combinations in seven genes present in at least 70% of all 639 patient samples, but not in a control cohort of healthy humans. Structural analyses of these revealed primarily an influence on the protein stability. For amino acid substitutions located at the protein surface, we generally observed a proximity to putative binding pockets. To computationally analyze cumulative effects and their impact, pathogenicity methods are currently being developed. Our approach supports this process, as shown on the example of a cardiac phenotype but can be likewise applied to other diseases such as cancer.

Influence of next-generation sequencing and storage conditions on miRNA patterns generated from PAXgene blood.

Whole blood derived miRNA signatures determined by Next-Generation Sequencing (NGS) offer themselves as future minimally invasive biomarkers for various human diseases. The PAXgene system is a commonly used blood storage system for miRNA analysis. Central to all miRNA analyses that aim to identify disease specific miRNA signatures, is the question of stability and variability of the miRNA profiles that are generated by NGS. We characterized the influence of five different conditions on the genome wide miRNA expression pattern of human blood isolated in PAXgene RNA tubes. In detail, we analyzed 15 miRNomes from three individuals. The blood was subjected to different numbers of freeze/thaw cycles and analyzed for the influence of storage at -80 or 8 °C. We also determined the influence of blood collection and NGS preparations on the miRNA pattern isolated from a single individual, which has been sequenced 10 times. Here, five PAXGene tubes were consecutively collected that have been split in two replicates, representing two experimental batches. All samples were analyzed by Illumina NGS. For each sample, approximately 20 million NGS reads have been generated. Hierarchical clustering and Principal Component Analysis (PCA) showed an influence of the different conditions on the miRNA patterns. The effects of the different conditions on miRNA abundance are, however, smaller than the differences that are due to interindividual variability. We also found evidence for an influence of the NGS measurement on the miRNA pattern. Specifically, hsa-miR-1271-5p and hsa-miR-182-5p showed coefficients of variation above 100% indicating a strong influence of the NGS protocol on the abundance of these miRNAs.

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms.

BACKGROUND: High-throughput genetic testing is increasingly applied in clinics. Next-Generation Sequencing (NGS) data analysis however still remains a great challenge. The interpretation of pathogenicity of single variants or combinations of variants is crucial to provide accurate diagnostic information or guide therapies. METHODS: To facilitate the interpretation of variants and the selection of candidate non-synonymous polymorphisms (nsSNPs) for further clinical studies, we developed BALL-SNP. Starting from genetic variants in variant call format (VCF) files or tabular input, our tool, first, visualizes the three-dimensional (3D) structure of the respective proteins from the Protein Data Bank (PDB) and highlights mutated residues, automatically. Second, a hierarchical bottom up clustering on the nsSNPs within the 3D structure is performed to identify nsSNPs, which are close to each other. The modular and flexible implementation allows for straightforward integration of different databases for pathogenic and benign variants, but also enables the integration of pathogenicity prediction tools. The collected background information of all variants is presented below the 3D structure in an easily interpretable table format. RESULTS: First, we integrated different data resources into BALL-SNP, including databases containing information on genetic variants such as ClinVar or HUMSAVAR; third party tools that predict stability or pathogenicity in silico such as I-Mutant2.0; and additional information derived from the 3D structure such as a prediction of binding pockets. We then explored the applicability of BALL-SNP on the example of patients suffering from cardiomyopathies. Here, the analysis highlighted accumulation of variations in the genes JUP, VCL, and SMYD2. CONCLUSION: Software solutions for analyzing high-throughput genomics data are important to support diagnosis and therapy selection. Our tool BALL-SNP, which is freely available at http://www.ccb.uni-saarland.de/BALL-SNP, combines genetic information with an easily interpretable and interactive, graphical representation of amino acid changes in proteins. Thereby relevant information from databases and computational tools is presented. Beyond this, proximity to functional sites or accumulations of mutations with a potential collective effect can be discovered.

Posttranscriptional deregulation of signaling pathways in meningioma subtypes by differential expression of miRNAs.

BACKGROUND: Micro (mi)RNAs are key regulators of gene expression and offer themselves as biomarkers for cancer development and progression. Meningioma is one of the most frequent primary intracranial tumors. As of yet, there are limited data on the role of miRNAs in meningioma of different histological subtypes and the affected signaling pathways. METHODS: In this study, we compared expression of 1205 miRNAs in different meningioma grades and histological subtypes using microarrays and independently validated deregulation of selected miRNAs with quantitative real-time PCR. Clinical utility of a subset of miRNAs as biomarkers for World Health Organization (WHO) grade II meningioma based on quantitative real-time data was tested. Potential targets of deregulated miRNAs were discovered with an in silico analysis. RESULTS: We identified 13 miRNAs deregulated between different subtypes of benign meningiomas, and 52 miRNAs deregulated in anaplastic meningioma compared with benign meningiomas. Known and putative target genes of deregulated miRNAs include genes involved in epithelial-to-mesenchymal transition for benign meningiomas, and Wnt, transforming growth factor-ß, and vascular endothelial growth factor signaling for higher-grade meningiomas. Furthermore, a 4-miRNA signature (miR-222, -34a*, -136, and -497) shows promise as a biomarker differentiating WHO grade II from grade I meningiomas with an area under the curve of 0.75. CONCLUSIONS: Our data provide novel insights into the contribution of miRNAs to the phenotypic spectrum in benign meningiomas. By deregulating translation of genes belonging to signaling pathways known to be important for meningioma genesis and progression, miRNAs provide a second in line amplification of growth promoting cellular signals. MiRNAs as biomarkers for diagnosis of aggressive meningiomas might prove useful and should be explored further in a prospective manner.

Pathogenicity prediction of non-synonymous single nucleotide variants in dilated cardiomyopathy.

Non-synonymous single nucleotide variants (nsSNVs) in coding DNA regions can result in phenotypic differences between individuals; however, only some nsSNVs are causative for a certain disease. As just a fraction of respective nsSNVs is annotated in databases, computational biology tools are applied to predict the pathogenicity in silico. In addition to applications in oncology, novel molecular diagnostic tests have been developed for cardiovascular disorders as a leading cause of morbidity and mortality in industrialized nations. We explored the concordance and performance of 13 nsSNV pathogenicity prediction tools on panel sequencing results of dilated cardiomyopathy. The analyzed data set from the INHERITANCE study contained 842 nsSNVs discovered in 639 patients, screened for the full sequence of 76 genes related to cardiomyopathies. The single tools prediction revealed a surprisingly high heterogeneity and discordance based on the implemented prediction method. Known disease associations were not reported by the tools, limiting usability in clinics. Because different tools have different advantages, we combined their results. By clustering of correlated methods using similar prediction strategies and calculating a majority vote-based consensus, we found that the prediction accuracy and sensitivity can be further improved. Although challenges remain, different in silico tools bear the potential to predict the malignancy of nsSNVs, especially if different algorithms are combined. Most tools rely mainly on sequence features; beyond these, structural information is important to analyze the relationship of nsSNVs with disease phenotypes. Likewise, current tools consider single nsSNVs, which may, however, show a cumulative effect and turn neutral mutations in an ensemble into pathogenic variants.

New insights into the genetics of glioblastoma multiforme by familial exome sequencing.

Glioblastoma multiforme (GBM) is the most aggressive and malignant subtype of human brain tumors. While a family clustering of GBM has long been acknowledged, relevant hereditary factors still remained elusive. Exome sequencing of families offers the option to discover respective genetic factors.We sequenced blood samples of one of the rare affected families: while both parents were healthy, both children were diagnosed with GBM. We report 85 homozygous non-synonymous single nucleotide variations (SNVs) in both siblings that were heterozygous in the parents. Beyond known key players for GBM such as ERBB2, PMS2, or CHI3L1, we identified over 50 genes that have not been associated to GBM so far. We also discovered three accumulative effects potentially adding to the tumorigenesis in the siblings: a clustering of multiple variants in single genes (e.g., PTPRB, CROCC), the aggregation of affected genes on specific molecular pathways (e.g., Focal adhesion or ECM receptor interaction) and genomic proximity (e.g., chr22.q12.2, chr1.p36.33). We found a striking accumulation of SNVs in specific genes for the daughter, who developed not only a GBM at the age of 12 years but was subsequently diagnosed with a pilocytic astrocytoma, a common acute lymphatic leukemia and a diffuse pontine glioma.The reported variants underline the relevance of genetic predisposition and cancer development in this family and demonstrate that GBM has a complex and heterogeneous genetic background. Sequencing of other affected families will help to further narrow down the driving genetic causes for this disease.

miRNAs can be generally associated with human pathologies as exemplified for miR-144.

BACKGROUND: miRNA profiles are promising biomarker candidates for a manifold of human pathologies, opening new avenues for diagnosis and prognosis. Beyond studies that describe miRNAs frequently as markers for specific traits, we asked whether a general pattern for miRNAs across many diseases exists. METHODS: We evaluated genome-wide circulating profiles of 1,049 patients suffering from 19 different cancer and non-cancer diseases as well as unaffected controls. The results were validated on 319 individuals using qRT-PCR. RESULTS: We discovered 34 miRNAs with strong disease association. Among those, we found substantially decreased levels of hsa-miR-144* and hsa-miR-20b with AUC of 0.751 (95% CI: 0.703-0.799), respectively. We also discovered a set of miRNAs, including hsa-miR-155*, as rather stable markers, offering reasonable control miRNAs for future studies. The strong downregulation of hsa-miR-144* and the less variable pattern of hsa-miR-155* has been validated in a cohort of 319 samples in three different centers. Here, breast cancer as an additional disease phenotype not included in the screening phase has been included as the 20th trait. CONCLUSIONS: Our study on 1,368 patients including 1,049 genome-wide miRNA profiles and 319 qRT-PCR validations further underscores the high potential of specific blood-borne miRNA patterns as molecular biomarkers. Importantly, we highlight 34 miRNAs that are generally dysregulated in human pathologies. Although these markers are not specific to certain diseases they may add to the diagnosis in combination with other markers, building a specific signature. Besides these dysregulated miRNAs, we propose a set of constant miRNAs that may be used as control markers.

SKINK: a web server for string kernel based kink prediction in α-helices.

MOTIVATION: The reasons for distortions from optimal α-helical geometry are widely unknown, but their influences on structural changes of proteins are significant. Hence, their prediction is a crucial problem in structural bioinformatics. Here, we present a new web server, called SKINK, for string kernel based kink prediction. Extending our previous study, we also annotate the most probable kink position in a given α-helix sequence. AVAILABILITY AND IMPLEMENTATION: The SKINK web server is freely accessible at http://biows-inf.zdv.uni-mainz.de/skink. Moreover, SKINK is a module of the BALL software, also freely available at www.ballview.org.

A blood based 12-miRNA signature of Alzheimer disease patients.

BACKGROUND: Alzheimer disease (AD) is the most common form of dementia but the identification of reliable, early and non-invasive biomarkers remains a major challenge. We present a novel miRNA-based signature for detecting AD from blood samples. RESULTS: We apply next-generation sequencing to miRNAs from blood samples of 48 AD patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression levels. Of these, 82 have higher and 58 have lower abundance in AD patient samples. We selected a panel of 12 miRNAs for an RT-qPCR analysis on a larger cohort of 202 samples, comprising not only AD patients and healthy controls but also patients with other CNS illnesses. These included mild cognitive impairment, which is assumed to represent a transitional period before the development of AD, as well as multiple sclerosis, Parkinson disease, major depression, bipolar disorder and schizophrenia. miRNA target enrichment analysis of the selected 12 miRNAs indicates an involvement of miRNAs in nervous system development, neuron projection, neuron projection development and neuron projection morphogenesis. Using this 12-miRNA signature, we differentiate between AD and controls with an accuracy of 93%, a specificity of 95% and a sensitivity of 92%. The differentiation of AD from other neurological diseases is possible with accuracies between 74% and 78%. The differentiation of the other CNS disorders from controls yields even higher accuracies. CONCLUSIONS: The data indicate that deregulated miRNAs in blood might be used as biomarkers in the diagnosis of AD or other neurological diseases.

String kernels and high-quality data set for improved prediction of kinked helices in α-helical membrane proteins.

The reasons for distortions from optimal α-helical geometry are widely unknown, but their influences on structural changes of proteins are significant. Hence, their prediction is a crucial problem in structural bioinformatics. For the particular case of kink prediction, we generated a data set of 132 membrane proteins containing 1014 manually labeled helices and examined the environment of kinks. Our sequence analysis confirms the great relevance of proline and reveals disproportionately high occurrences of glycine and serine at kink positions. The structural analysis shows significantly different solvent accessible surface area mean values for kinked and nonkinked helices. More important, we used this data set to validate string kernels for support vector machines as a new kink prediction method. Applying the new predictor, about 80% of all helices could be correctly predicted as kinked or nonkinked even when focusing on small helical fragments. The results exceed recently reported accuracies of alternative approaches and are a consequence of both the method and the data set.

BALL--biochemical algorithms library 1.3.

BACKGROUND: The Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements. RESULTS: Here, we discuss BALL's current functionality and highlight the key additions and improvements: support for additional file formats, molecular edit-functionality, new molecular mechanics force fields, novel energy minimization techniques, docking algorithms, and support for cheminformatics. CONCLUSIONS: BALL is available for all major operating systems, including Linux, Windows, and MacOS X. It is available free of charge under the Lesser GNU Public License (LPGL). Parts of the code are distributed under the GNU Public License (GPL). BALL is available as source code and binary packages from the project web site at http://www.ball-project.org. Recently, it has been accepted into the debian project; integration into further distributions is currently pursued.