Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. AVAILABILITY AND IMPLEMENTATION: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

CTCF and Its Multi-Partner Network for Chromatin Regulation.

Architectural proteins are essential epigenetic regulators that play a critical role in organizing chromatin and controlling gene expression. CTCF (CCCTC-binding factor) is a key architectural protein responsible for maintaining the intricate 3D structure of chromatin. Because of its multivalent properties and plasticity to bind various sequences, CTCF is similar to a Swiss knife for genome organization. Despite the importance of this protein, its mechanisms of action are not fully elucidated. It has been hypothesized that its versatility is achieved through interaction with multiple partners, forming a complex network that regulates chromatin folding within the nucleus. In this review, we delve into CTCF's interactions with other molecules involved in epigenetic processes, particularly histone and DNA demethylases, as well as several long non-coding RNAs (lncRNAs) that are able to recruit CTCF. Our review highlights the importance of CTCF partners to shed light on chromatin regulation and pave the way for future exploration of the mechanisms that enable the finely-tuned role of CTCF as a master regulator of chromatin.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.

BACKGROUND: Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. OBJECTIVES: However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. METHOD: This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. CONCLUSION: As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.

Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink .

Flimma: a federated and privacy-aware tool for differential gene expression analysis.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.

Human gestational N-methyl-d-aspartate receptor autoantibodies impair neonatal murine brain function.

OBJECTIVE: Maternal autoantibodies are a risk factor for impaired brain development in offspring. Antibodies (ABs) against the NR1 (GluN1) subunit of the N-methyl-d-aspartate receptor (NMDAR) are among the most frequently diagnosed anti-neuronal surface ABs, yet little is known about effects on fetal development during pregnancy. METHODS: We established a murine model of in utero exposure to human recombinant NR1 and isotype-matched nonreactive control ABs. Pregnant C57BL/6J mice were intraperitoneally injected on embryonic days 13 and 17 each with 240µg of human monoclonal ABs. Offspring were investigated for acute and chronic effects on NMDAR function, brain development, and behavior. RESULTS: Transferred NR1 ABs enriched in the fetus and bound to synaptic structures in the fetal brain. Density of NMDAR was considerably reduced (up to -49.2%) and electrophysiological properties were altered, reflected by decreased amplitudes of spontaneous excitatory postsynaptic currents in young neonates (-34.4%). NR1 AB-treated animals displayed increased early postnatal mortality (+27.2%), impaired neurodevelopmental reflexes, altered blood pH, and reduced bodyweight. During adolescence and adulthood, animals showed hyperactivity (+27.8% median activity over 14 days), lower anxiety, and impaired sensorimotor gating. NR1 ABs caused long-lasting neuropathological effects also in aged mice (10 months), such as reduced volumes of cerebellum, midbrain, and brainstem. INTERPRETATION: The data collectively support a model in which asymptomatic mothers can harbor low-level pathogenic human NR1 ABs that are diaplacentally transferred, causing neurotoxic effects on neonatal development. Thus, AB-mediated network changes may represent a potentially treatable neurodevelopmental congenital brain disorder contributing to lifelong neuropsychiatric morbidity in affected children. ANN NEUROL 2019;86:656-670.

N-methyl-D-aspartate receptor dysfunction by unmutated human antibodies against the NR1 subunit.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis related to autoantibody-mediated synaptic dysfunction. Cerebrospinal fluid-derived human monoclonal NR1 autoantibodies showed low numbers of somatic hypermutations or were unmutated. These unexpected germline-configured antibodies showed weaker binding to the NMDAR than matured antibodies from the same patient. In primary hippocampal neurons, germline NR1 autoantibodies strongly and specifically reduced total and synaptic NMDAR currents in a dose- and time-dependent manner. The findings suggest that functional NMDAR antibodies are part of the human naïve B cell repertoire. Given their effects on synaptic function, they might contribute to a broad spectrum of neuropsychiatric symptoms. Ann Neurol 2019;85:771-776.

Affinities of human NMDA receptor autoantibodies: implications for disease mechanisms and clinical diagnostics.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a common autoimmune encephalitis presenting with psychosis, dyskinesias, autonomic dysfunction and seizures. The underlying autoantibodies against the NR1 subunit are directly pathogenic by disrupting synaptic NMDAR currents. However, antibody titers correlate only partially with the clinical outcome, suggesting the relevance of other factors such as antibody affinity. We thus determined the binding curves of human monoclonal autoantibodies and patients' cerebrospinal fluid (CSF) against NR1-expressing HEK293 cells using flow cytometry. Antibody affinity was highly variable with binding constants (half-maximal concentration, c50) ranging from 1 to 74 µg/ml for monoclonal antibodies. Comparing values of individual monoclonal antibodies with human CSF samples suggested that the CSF signal is predominantly represented by higher-affinity antibodies, potentially in a concentration range of NR1 antibodies between 0.1 and 5 µg/ml, roughly reflecting 1-10% of total CSF IgG in NMDAR encephalitis. Binding curves further depended on the CSF composition which must be considered when interpreting established clinical routine assays. Normalization of measurements using reference samples allowed high reproducibility. Accurate and reproducible measurement of NR1 antibody binding suggested that biophysical properties of the antibody might contribute to disease severity. Normalization of the data can be an elegant way to allow comparable inter-laboratory quantification of CSF NR1 antibody titers in autoimmune encephalitis patients, a prerequisite for use as surrogate markers in clinical trials. Based on our calculations, low-affinity antibodies can easily remain undetected in routine cell-based assays, indicating that their relation to clinical symptoms should be analyzed in future studies.

Wnt signaling positively regulates endothelial cell fate specification in the Fli1a-positive progenitor population via Lef1.

During vertebrate embryogenesis, vascular endothelial cells (ECs) and primitive erythrocytes become specified within close proximity in the posterior lateral plate mesoderm (LPM) from a common progenitor. However, the signaling cascades regulating the specification into either lineage remain largely elusive. Here, we analyze the contribution of ß-catenin dependent Wnt signaling to EC and erythrocyte specification during zebrafish embryogenesis. We generated novel ß-catenin dependent Wnt signaling reporters which, by using destabilized fluorophores (Venus-Pest, dGFP), specifically allow us to detect Wnt signaling responses in narrow time windows as well as in spatially restricted domains, defined by Cre recombinase expression (Tg(axin2BAC:Venus-Pest)mu288; Tg(14TCF:loxP-STOP-loxP-dGFP)mu202). We therefore can detect ß-catenin dependent Wnt signaling activity in a subset of the Fli1a-positive progenitor population. Additionally, we show that mesodermal Wnt3a-mediated signaling via the transcription factor Lef1 positively regulates EC specification (defined by kdrl expression) at the expense of primitive erythrocyte specification (defined by gata1 expression) in zebrafish embryos. Using mesoderm derived from human embryonic stem cells, we identified the same principle of Wnt signaling dependent EC specification in conjunction with auto-upregulation of LEF1. Our data indicate a novel role of ß-catenin dependent Wnt signaling in regulating EC specification during vasculogenesis.

Human cerebrospinal fluid monoclonal N-methyl-D-aspartate receptor autoantibodies are sufficient for encephalitis pathogenesis.

SEE ZEKERIDOU AND LENNON DOI101093/AWW213 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a recently discovered autoimmune syndrome associated with psychosis, dyskinesias, and seizures. Little is known about the cerebrospinal fluid autoantibody repertoire. Antibodies against the NR1 subunit of the NMDAR are thought to be pathogenic; however, direct proof is lacking as previous experiments could not distinguish the contribution of further anti-neuronal antibodies. Using single cell cloning of full-length immunoglobulin heavy and light chain genes, we generated a panel of recombinant monoclonal NR1 antibodies from cerebrospinal fluid memory B cells and antibody secreting cells of NMDAR encephalitis patients. Cells typically carried somatically mutated immunoglobulin genes and had undergone class-switching to immunoglobulin G, clonally expanded cells carried identical somatic hypermutation patterns. A fraction of NR1 antibodies were non-mutated, thus resembling 'naturally occurring antibodies' and indicating that tolerance induction against NMDAR was incomplete and somatic hypermutation not essential for functional antibodies. However, only a small percentage of cerebrospinal fluid-derived antibodies reacted against NR1. Instead, nearly all further antibodies bound specifically to diverse brain-expressed epitopes including neuronal surfaces, suggesting that a broad repertoire of antibody-secreting cells enrich in the central nervous system during encephalitis. Our functional data using primary hippocampal neurons indicate that human cerebrospinal fluid-derived monoclonal NR1 antibodies alone are sufficient to cause neuronal surface receptor downregulation and subsequent impairment of NMDAR-mediated currents, thus providing ultimate proof of antibody pathogenicity. The observed formation of immunological memory might be relevant for clinical relapses.