Biology subjects

Reinders, M. J. T.

Publications and source records attributed to Reinders, M. J. T..

At least 19 recordsLinked to original sources

bioRxiv2026

The Quagmire of Immunogenicity-Guided Antibody Design: Labels Limit Progress

Generative sequence-based antibody design is increasingly steered by learned property predictors, so a design objective is only as well specified as the labels behind its oracle. Immunogenicity is among the most clinically consequential properties to condition on, and among the least well specified: the supervisory signal is a single anti-drug-antibody percentage (ADA%) per molecule, aggregated from published trials. To analyze that signal, we first extended and corrected the widely used 217-entry dataset to 231 therapeutics with paired sequences, spanning formats from mouse to fully human. Then, we manually reconstructed the clinical evidence for 110 of them from primary sources. We find that an antibody format lookup outperforms every sequence-based learned predictor on the 231-antibody dataset. Apparent accuracy tracks the format composition of the evaluation split rather than sequence signal. Furthermore, the labels understate their own evidence: molecules labeled at [≤]6 ADA% span up to 86% across trials. We recomputed labels from the trial-level measurements and found that the advantage of the format-based approach disappears, while the sequence-based predictors degrade less under the cohort-weighted mean. We conclude that progress is limited by the label itself, and recommend reporting ADA% at the trial level, treating the target as a distribution rather than a point value, and evaluating against a format-based baseline.

bioinformatics↗

bioRxiv2026

Multi-omic profiling of human thymic B cells reveals intrathymic Ig-class switching and differentiation into multiple memory B cell subsets

Primarily recognized as the site for T cell development, the thymus supports a complex interplay between thymic stromal cells and developing thymocytes, which is essential for T cell maturation and the establishment of central tolerance. Emerging evidence indicates that thymic B cells contribute to tolerance induction by functioning as antigen-presenting cells. However, their developmental pathways and functional roles remain poorly understood. Using tissue mass cytometry, we localized B cells in the thymus and their orientation towards other cells in the medulla. We characterized the heterogeneity of thymic B cells using single-cell RNA sequencing of cells isolated from human thymi, identifying naive, germinal center-like, plasma cell, and multiple memory B cell populations. We identified a distinct pre-B cell subset with local thymic B cell development potential, that can develop due to the inhibition of Notch signaling by Deltex1, a Notch antagonist. Using BCR repertoire analysis, we explored clonal diversity, somatic hypermutation patterns and class switch recombination of thymic B cells. Spectral flow cytometry further validated the surface phenotype of thymic B cell populations and confirmed the presence of distinct CD21-CD27- memory compartments with heterogeneous surface immunoglobulin isotype usage. In doing so we provide novel insights into the unique biology of human thymic B cells, their development, and their differentiation in the human thymus. We conclude that the human thymus supports local B cell development and differentiation into medullary memory-like populations that undergo class switching with limited somatic hypermutation, suggesting secondary lymphoid-like B cell programs adapted to central tolerance induction. One Sentence SummaryThe human thymus is not only a primary lymphoid organ for T cell development, but also a secondary site for the development and maturation of unique populations of B cells.

immunology↗

bioRxiv2025

All-Atom Protein Sequence Design using Discrete Diffusion Models

Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This work explores discrete diffusion models for generating novel protein sequences using the all-atom chemical representation SELFIES. By encoding the atomic composition of each amino acid in the protein, this approach expands the design possibilities beyond standard sequence representations. Using a modified ByteNet architecture within the discrete diffusion D3PM framework, we evaluate the impact of this all-atom representation on protein quality, diversity, and novelty, compared to conventional amino acid-based models. To this end, we develop a comprehensive assessment pipeline to determine whether generated SELFIES sequences translate into valid proteins containing both canonical and non-canonical amino acids. Additionally, we examine the influence of two noise schedules within the diffusion process--uniform (random replacement of tokens) and absorbing (progressive masking)--on generation performance. While models trained on the all-atom representation struggle to consistently generate fully valid proteins, the successfully generated proteins show improved novelty and diversity compared to their amino acid-based model counterparts. Furthermore, the all-atom representation achieves structural foldability results comparable to those of amino acid-based models. Lastly, our results highlight the absorbing noise schedule as the most effective for both representations. Data and code are available at https://github.com/Intelligent-molecular-systems/All-Atom-Protein-Sequence-Generation. Scientific ContributionThis work introduces a discrete diffusion-based framework for protein sequence generation using an all-atom representation, enabling the incorporation of non-canonical amino acids and post-translational modifications. Additionally, it provides a comprehensive evaluation pipeline to assess the validity of generated proteins, demonstrating how noise schedules within the diffusion process impact sequence novelty, diversity, and structural foldability.

bioinformatics↗

bioRxiv2025

scAGG: Sample-level embedding and classification of Alzheimer's disease from single-nucleus data

Identifying key cell types and genes in Alzheimers Disease (AD) is crucial for understanding its pathogenesis and discovering therapeutic targets. Single-cell RNA sequencing technology (scRNAseq) has provided unprecedented opportunities to study the molecular mechanisms that underlie AD at the cellular level. In this study, we address the problem of sample-level classification of AD using scRNAseq data, where we predict the disease status of entire samples from the gene expression profiles of their cells, which are not necessarily all affected by the disease. We introduce scAGG (single-cell AGGregation), a sample-level classification model that uses a sample-level pooling mechanism to aggregate single-cell embeddings, and show that it can accurately classify AD individuals and healthy controls. We then investigate the latent space learnt by the model and find that the model learns an ordering of the cells corresponding to disease severity. Genes associated with this ordering are enriched in AD-linked pathways, including cytokine signalling, apoptosis, and metal ion response. We also evaluate two attention-based models that perform on par with scAGG, but entropy analysis of their attention scores reveals limited interpretability value. As scRNAseq is increasingly applied to large cohorts and cell-level disease association annotations do not exist, our approach provides a way to classify phenotypes from single-cell measurements. The yielded cell- and sample-level severity scores may enable identification of AD-associated cell subtypes, paving the way for targeted drug development and personalized treatment strategies in AD. Code is available at: http://github.com/timoverlaan/scAGG

bioinformatics↗

bioRxiv2023

Guiding diffusion models for antibody sequence and structure co-design with developability properties

Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a novel approach that integrates property guidance into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-conditioned diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications. Code is available at https://github.com/amelvim/antibody-diffusion-properties.

bioinformatics↗

bioRxiv2023

Cell type matching across species using protein embeddings and transfer learning

MotivationKnowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. ResultsIn this work, we present a model to Transfer and Align Cell Types in Cross-Species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterwards, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, at a high resolution, our model outperforms the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better matches than BLAST, both in our model and SAMap. Availabilityhttps://github.com/kbiharie/TACTiCS Contacta.mahfouz@lumc.nl

bioinformatics↗

bioRxiv2022

The rise of sparser single-cell RNAseq datasets; consequences and opportunities

There is an exponential increase in the number of cells measured in single-cell RNA sequencing (scRNAseq) datasets. Concurrently, scRNA-seq datasets become increasingly sparser as more zero counts are measured for many genes. We discuss that with increasing sparsity the binarized representation of gene expression becomes as informative as count-based expression. We show that downstream analyses based on binarized gene expressions give similar results to analyses based on count-based expressions. Moreover, a binarized representation scales to 17-fold more cells that can be analyzed using the same amount of computational resources. Based on these observations, we recommend the development of specialized tools for bit-aware implementations for downstream analyses tasks, creating opportunities to get a more fine-grained resolution of biological heterogeneity.

bioinformatics↗

bioRxiv2022

Identifying commonalities between cell lines and tumors at the single cell level using Sobolev Alignment of deep generative models

Preclinical models are essential to cancer research, however, key biological differences with patient tumors result in reduced translatability to the clinic and high attrition rates in drug development. Variability among and between patients, preclinical models, and individual cells obscures commonalities which could otherwise be exploited therapeutically. To discover the shared biological processes between cell line models and clinical tumors we developed Sobolev Alignment, a computational framework which uses deep generative models to capture non-linear processes in single-cell RNA sequencing data and kernel methods to align and interpret these processes. We show that our approach faithfully captures shared processes on a set of three synthetic datasets. Exploiting two large panels of untreated non-small cell lung cancer cell lines and patients, we identify the similarities between cell lines and tumors and show the conservation of key mitotic and immune-related pathways. Employing our approach on a large in-vitro perturbation screen, we show that processes captured by our method faithfully recapitulate the known modes of action of clinically approved drugs and allow investigation into the mode of action of an uncharacterized drug.

bioinformatics↗

bioRxiv2022

Metabolic predictors of phenotypic traits can replace and complement measured clinical variables in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) can provide valuable insights into biological and disease-underlying mechanisms. For studying clinical effects, availability of (confounding) phenotypic traits is essential. The (re)use of RNA-seq or other omics data can be limited by missing, incomplete, or inaccurate phenotypic information. A possible solution are molecular predictors inferring clinical or behavioral phenotypic traits. Such predictors have been developed based on different omics data types and are being applied in various studies. In this study, we applied 17 metabolic predictors to infer various traits, including diabetes status or exposure to lipid medication. We evaluated whether these metabolic surrogates can be used as an alternative to reported information for studying the respective phenotypes using TWAS. Our results revealed that in most cases, the use of metabolic surrogates yields similar results compared to using reported information, making them suitable substitutes for such studies. The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets, especially in situations where availability of clinical metadata is limited. Missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Studies would likely also benefit from the use of such surrogates to correct for potential biological confounding. This should be further investigated. Author summaryTranscriptome-wide association studies (TWAS) can be used to associate gene expression levels with phenotypic traits. These associations can provide insights into biological mechanisms including those that underlie diseases. Such studies require molecular profiling data from a large number of individuals as well as information on the phenotypic trait of interest. Biobanks that collect samples and corresponding molecular data, also collect information on phenotypic traits or clinical information. However, this information can be heterogeneous and/or incomplete, or a certain piece of information could be missing entirely. In this study, we apply metabolic predictors to infer various traits, including diabetes status or exposure to lipid medication. We evaluate whether these metabolic surrogates can be used as an alternative to reported information for studying the respective phenotypes using TWAS. Our results reveal that in many cases, the use of metabolic surrogates yields similar results compared to using reported information, making them suitable substitutes for such studies. The possibility of using these surrogate outcomes can thus increase the size of data sets for studies where phenotypic information is incomplete or missing.

bioinformatics↗

bioRxiv2021

Longitudinal dynamics of human B-cell response at single-cell level in response to Tdap vaccination

Adaptation of the immune system to mount an adequate immune response against pathogens is a crucial function of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B cell repertoire e.g. regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, however, certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid. Collectively, we developed a workflow which allows description of key features of an ongoing immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure.

immunology↗

bioRxiv2021

SIRV: Spatial inference of RNA velocity at the single-cell resolution

RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.

bioinformatics↗

bioRxiv2021

A combined lifestyle intervention induces a sensitization of the blood transcriptomic response to a nutrient challenge

The global population is growing older. As age is a primary risk factor of (multi)morbidity, there is a need for novel indicators to predict, track, treat and prevent the development of disease. Lifestyle interventions have shown promising results in improving the health of participants and reducing the risk for disease, but in the elderly population, such interventions often show less reliable or subtle effects on health outcomes. This is further complicated by a poor understanding of the homeodynamics and the molecular effects of lifestyle interventions, by which their effects of a lifestyle intervention remain obscured. In the Growing Old Together (GOTO) study, we examined the responses of 164 healthy, elderly men and women to a 13-week combined physical and dietary lifestyle intervention. In addition to collecting blood samples at a fasted state, we sampled blood also 30 minutes following a standardized meal. This allows us to investigate an intervention response not only in the traditional fasted state, but also in the blood metabolic and cellular responses to a nutrient challenge. We investigated the transcriptomic and metabolomic responses to this nutrient challenge, how these responses relate to each other, and how this response is affected by the lifestyle intervention. We find that the intervention has very little effect on the fasted blood transcriptome, but that the nutrient challenge induces a large translational inhibition, and an innate immune activation, which together comprise a cellular stress response that is stimulated by the intervention. A sex-specific analysis reveals that although the same set of genes respond in the same direction in both males and females, the magnitude of these effects differ, and are modulated differently by the intervention. On the other hand, the metabolomic response to the nutrient challenge is largely unaffected by the intervention, and the correlation between the metabolomic nutrient response and transcriptomic modules indicates that the change in transcriptomic response to the nutrient challenge is independent from a change in cellular metabolomic environment. This work constitutes a glance at the acute transcriptomic stress response to nutrient intake in blood, and how a lifestyle intervention affects this response in healthy elderly, and may lead to the development of novel biomarkers to capture the phenotypic flexibility of health.

molecular biology↗

bioRxiv2021

Differential dropout analysis captures biological variation in single-cell RNA sequencing data

Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose differential dropout analysis (DDA) to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that DDA accurately detects biological variation and can assess the relative abundance of transcripts more robustly than methods relying on counts. DDA is available at https://github.com/gbouland/DDA.

bioinformatics↗

bioRxiv2020

VNTR prediction on sequence characteristics using long-read annotation and validation by short-read pileup

Tandem repeats (TRs) are contiguously repetitive sequences with a high mutation rate. Several human diseases have been associated with an expansion of TR, a mutation which constitutes a change in their number of repetitions. Nevertheless, these Variable Number Tandem Repeats (VNTRs) have not been included in many genome-wide studies. The reason is that VNTR genotyping is inaccurate using short-read sequencing while new technology like long-read sequencing is expensive and lacks throughput. Here, we propose a sequence based random forest classifier that is able to predict variable expansion of TR regions, given by incomplete VNTR annotation from long-read sequencing of 5 haplotypes. The classifier mainly predicted VNTRs using the features TR length. The second most used feature is a novel finding: the Mfold predicted likelihood of self-folding for which more stable foldings are correlated with VNTRs. We validated VNTR candidates predicted by this classifier by clustering short-read pileup patterns compared across 17 genomes. TRs labeled VNTR by the classifier showed similar local variance in their pileup profiles. Contactdiederik.cvb@gmail.com Supplementary informationSupplementary data are available at bioRxiv

bioinformatics↗

bioRxiv2020

PRECISE+ predicts drug response in patients by non-linear subspace-based transfer from cell lines and PDX models

Pre-clinical models have been the workhorse of cancer research for decades. While powerful, these models do not fully recapitulate the complexity of human tumors. Consequently, translating biomarkers of drug response from pre-clinical models to human tumors has been particularly challenging. To explicitly take these differences into account and enable an efficient exploitation of the vast pre-clinical drug response resources, we developed TRANSACT, a novel computational framework for clinical drug response prediction. First, TRANSACT employs non-linear manifold learning to capture biological processes active in pre-clinical models and human tumors. Then, TRANSACT builds predictors on cell line response only and transfers these to Patient-Derived Xenografts (PDXs) and human tumors. TRANSACT outperforms four competing approaches, including Deep Learning approaches, for a set of 15 drugs on PDXs, TCGA cohorts and 226 metastatic tumors from the Hartwig Medical Foundation data. For only four drugs Deep Learning outperforms TRANSACT. We further derived an algorithmic approach to interpret TRANSACT and used it to validate the approach by identifying known biomarkers to targeted therapies and we propose novel putative biomarkers of resistance to Paclitaxel and Gemcitabine.

bioinformatics↗

bioRxiv2020

Population matched (PM) germline allelic variants of immunoglobulin (IG) loci: New pmIG database to better understand IG repertoire and selection processes in disease and vaccination

At the population level, immunoglobulin (IG) loci harbor inter-individual allelic variants in the many different germline IG variable (V), Diversity (D) and Joining (J) genes of the IG heavy (IGH), IG kappa (IGK) and IG lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These inter-individual allelic variants can be shared between or be specific to human populations. The current IG databases IMGT, VBASE2 and IgPdb hold information about germline alleles, most of which are partial sequences, obtained from a mixture of human (B-cell) samples, many with sequence errors and/or acquired (non-germline) IG variations, induced by somatic hypermutation (SHM) during antigen-specific B-cell responses. We systematically identified true germline alleles (without SHM) from 26 different human populations around the world, profiled by the "1000 Genomes data". Our resource is uniquely enriched with complete IG allele sequences and their frequencies across human populations. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes (= minimum of four individuals), after removal of potential false-positives, based on using other genomic databases, i.e. ENSEMBL, TopMed, ExAC, ProjectMine. Remarkably, the positions of the identified variant nucleotides of the different alleles are not at random (as observed in case of SHM), but show striking patterns, restricted to limited nucleotide positions, the same as found in other IG data bases, suggesting over-time evolutionary selection processes. The identification of these specific patterns provides extra evidence that the identified variant nucleotides are not sequencing errors, but genuine allelic variants. The diversity of germline allelic variants in IGH and IGL loci is the highest in Africans, while the IGK locus is most diverse in Europeans. We also report on the presence of recombination signal sequences (RSS) in V pseudogenes, explaining their usage in V(D)J rearrangements. We propose that this new set of genuine germline IG sequences can serve as a new population-matched IG (pmIG) database for better understanding B-cell repertoire and B-cell receptor selection processes in disease and vaccination within and between different human populations. The database in format of fasta is available via GitHub (https://github.com/InduKhatri/pmIG). Contribution to the Field StatementWe present a catalogue of immunoglobulin (IG) germline-alleles of unprecedented completeness and accuracy from 26 different human populations belonging to five different large ethnicities (Source: 1000 Genomes). We identified the population distribution of several known germline alleles and identified multiple new alleles, especially in African populations, indicative of high allelic diversity of IG genes in Africa. Strikingly, the identified variant nucleotides of the different alleles are not at random, but show striking patterns, restricted to limited nucleotide positions, the same as found in other IG databases, suggesting over-time evolutionary selection processes. Furthermore, we identified recombination signal sequences in pseudogenes (previously not known). We provide an overview of IG germline alleles shared with and between known databases and also point to potential sources of non-germline variation and incompleteness of the existing IG databases. More importantly, we believe that this information can serve as a novel population-matched IG (pmIG) database, highly valuable for the research community in supporting the dissection and understanding of differences in effectiveness of antibody-based immune responses in infectious diseases, other (immune) diseases and vaccination within and between human populations. Such knowledge might be used in developing population-specific vaccination strategies e.g. for currently ongoing SARS-CoV2 pandemic.

immunology↗

bioRxiv2020

SCHNEL: Scalable clustering of high dimensional single-cell data

MotivationSingle cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets. ResultsWe developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. Availability and ImplementationImplementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering) Contacta.mahfouz@lumc.nl Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Cholinergic circuit genes in the healthy brain are differentially expressed in regions that exhibit gray matter loss in Parkinson's disease

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance. In Parkinsons disease, the posterior cingulate network and anterior cingulate network showed decreased gray matter and therefore we examined the underlying molecular processes of these anatomical networks in the healthy brain. Whole brain transcriptomics from post-mortem samples from healthy adults, revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS signaling pathways in these PD-related regions. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a protective role. Furthermore, both networks were associated with memory and neuropsychiatric disorders that overlap with Parkinsons disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinsons disease.

bioinformatics↗