Biology subjects

Krueger, R.

Publications and source records attributed to Krueger, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Mitochondrial CISD1 is a downstream target that mediates PINK1 and Parkin loss-of-function phenotypes

Parkinsons disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain. Familial cases of PD are often caused by mutations of PTEN-induced kinase 1 (PINK1) and the ubiquitin ligase Parkin, both pivotal in maintaining mitochondrial quality control. CISD1, a homodimeric mitochondrial iron-sulfur-binding protein, is a major target of Parkin-mediated ubiquitination. We here discovered a heightened propensity of CISD1 to form dimers in Pink1 mutant flies and in dopaminergic neurons from PINK1 mutation patients. The dimer consists of two monomers that are covalently linked by a disulfide bridge. In this conformation CISD1 cannot coordinate the iron-sulfur cofactor. Overexpressing Cisd, the Drosophila orthologue of CISD1, and a mutant Cisd incapable of binding the iron-sulfur cluster in Drosophila reduced climbing ability and lifespan. This was more pronounced with mutant Cisd and aggravated in Pink1 mutant flies. Complete loss of Cisd, in contrast, rescued all detrimental effects of Pink1 mutation on climbing ability, wing posture, dopamine levels, lifespan, and mitochondrial ultrastructure. Our results suggest that Cisd, probably iron-depleted Cisd, operates downstream of Pink1 shedding light on PD pathophysiology and implicating CISD1 as a potential therapeutic target.

neuroscience↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗

bioRxiv2023

Differentiable Partition Function Calculation for RNA

AO_SCPLOWBSTRACTC_SCPLOWRibonucleic acid (RNA) is an essential molecule in a wide range of biological functions. In 1990, McCaskill introduced a dynamic programming algorithm for computing the partition function of an RNA sequence. This forward model is widely used for understanding the thermodynamic properties of a given RNA. In this work, we introduce a generalization of McCaskills algorithm that is well-defined over continuous inputs and is differentiable. This allows us to tackle the inverse folding problem--designing a sequence with desired equilibrium thermodynamic properties--directly using gradient optimization. This has applications to creating RNA-based drugs such as mRNA vaccines. Furthermore, it allows McCaskills foundational algorithm to be incorporated into machine learning pipelines directly since we have made it end-to-end differentiable. This work highlights how principles from differentiable programming can be translated to existing physical models to develop powerful tools for machine learning. We provide a concrete example by implementing an effective and interpretable RNA design algorithm.

bioinformatics↗

bioRxiv2023

Multi-omics analysis identifies LBX1 and NHLH1 as central regulators of human midbrain dopaminergic neuron differentiation

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinsons disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) iPSC reporter line, we have generated time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicted novel central regulators of mDAN differentiation and super-enhancers were used to prioritize key TFs. We find LBX1, NHLH1 and NR2F1/2 to be necessary for mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. NHLH1 is necessary for the induction of neuronal miR-124, while LBX1 regulates cholesterol biosynthesis, possibly through mTOR signaling. Consistently, rapamycin treatment led to an inhibition of mDAN differentiation. Thus, our work reveals novel regulators of human mDAN differentiation.

genomics↗

bioRxiv2022

When Mechanical Stress Matters: Generation of Polyploid Giant Cancer Cells in Tumor-like Microcapsules

In this work, we studied the generation and rising of polyploid cancer cells as a product of mechanical stress. To this purpose, MCF7 breast cancer cells were cultured on 2D (i.e. flasks, or flat hydrogels), and in 3D milieus (i.e. Spheroids, or immobilized within alginate-gelatin microbeads, named in this work as tumor-like microcapsules), and further analyzed by biophysical and genetic methods (i.e. single-cell Traction Force Microscopy and RNA-seq respectively). Our results show that MCF7 cells preconditioned onto 2D surfaces exhibit a low number of polynucleated cells, while their culture in 3D environments triggered their progressive generation with time. Genetic studies enabled us to determine that polyploid cells found in tumor-like microcapsules are likely originated by cell-cell fusion and disrupted cytokinesis, showing most of the genetic markers for Polyploid Giant Cancer Cell, while cells cultured as spheroids seem to be likely generated by other mechanisms, such as cell cannibalisms, entosis, or emperipolesis. Our outcomes strongly suggest that both mechanical stress and confinement are required to stimulate cell polyploidy, which can be easily addressed by the immobilization of breast cancer cells in tumor-like microcapsules.

bioengineering↗

bioRxiv2022

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes ( lymphonets) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.

cancer biology↗

bioRxiv2022

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

cell biology↗

bioRxiv2021

Synaptic decline precedes dopaminergic neuronal loss in human midbrain organoids harboring a triplication of the SNCA gene

1.Increased levels of the protein alpha-synuclein (-syn) are associated with the development of neurodegenerative diseases like Parkinsons disease (PD). In physiological conditions, -syn modulates synaptic plasticity, neurogenesis and neuronal survival. However, its pathogenic accumulation and aggregation results in toxicity and neurodegeneration. Here, we used a PD patient specific midbrain organoid model derived from induced pluripotent stem cells harboring a triplication in the SNCA gene to study PD-associated phenotypes. The model recapitulates the two main hallmarks of PD, which are -syn aggregation and loss of dopaminergic neurons. Additionally, impairments in astrocyte differentiation were detected. Transcriptomics data indicate that synaptic function is impaired in PD specific midbrain organoids. This is further confirmed by alterations in synapse number and electrophysiological activity. We found that synaptic decline precedes neurodegeneration. Finally, this study substantiates that patient specific midbrain organoids allow a personalized phenotyping, which make them an interesting tool for precision medicine and drug discovery.

neuroscience↗

bioRxiv2021

GDAP1 loss of function inhibits the mitochondrial pyruvate dehydrogenase complex by altering the actin cytoskeleton

Charcot-Marie-Tooth (CMT) disease 4A is an autosomal-recessive polyneuropathy caused by mutations of ganglioside-induced differentiation-associated protein 1 (GDAP1), a putative glutathione transferase, which affects mitochondrial shape and alters cellular Ca2+ homeostasis. Here, we identify the underlying mechanism. We found that patient-derived motoneurons and GDAP1 knockdown SH-SY5Y cells display two phenotypes: more tubular mitochondria and a metabolism characterized by glutamine dependence and fewer cytosolic lipid droplets. GDAP1 interacts with the actin-depolymerizing protein Cofilin-1 in a redoxdependent manner, suggesting a role for actin signaling. Consistently, GDAP1 loss causes less F-actin close to mitochondria, which restricts mitochondrial localization of the fission factor dynamin-related protein 1, instigating tubularity. Changes in the actin cytoskeleton also disrupt mitochondria-ER contact sites. This results in lower mitochondrial Ca2+ levels and inhibition of the pyruvate dehydrogenase complex, explaining the metabolic changes upon GDAP1 loss of function. Together, these findings reconcile GDAP1-associated phenotypes and implicate disrupted actin signaling in CMT4A pathophysiology.

neuroscience↗

bioRxiv2020

Deep sncRNA-seq of the PPMI cohort to study Parkinson's disease progression

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinsons diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinsons Progression Marker Initiative (PPMI) and Luxembourg Parkinsons Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.

neuroscience↗

bioRxiv2020

IPSC-derived midbrain astrocytes from Parkinson's disease patients carrying pathogenic SNCA mutations exhibit alpha-synuclein aggregation, mitochondrial fragmentation and excess calcium release

Parkinsons disease (PD) is characterized by the loss of A9 midbrain dopaminergic neurons and the accumulation of alpha-synuclein aggregates in remaining neurons. Many studies of the molecular and cellular basis of neurodegeneration in PD have made use of iPSC-derived neurons from patients with familial PD mutations. However, approximately half of the cells in the brain are glia, and their role facilitating neurodegeneration is unclear. We developed a novel serum-free protocol to generate midbrain astrocytes from patient-derived iPSCs harbouring the pathogenic p.A30P, p.A53T mutations in SNCA, as well as duplication and triplication of the SNCA locus. In our cellular model, aggregates of alpha-synuclein occurred only within the GFAP+ astrocytes carrying the pathogenic SNCA mutations. Assessment of spontaneous cytosolic calcium (Ca2+) release using Fluo4 revealed that SNCA mutant astrocytes released excess Ca2+ compared to controls. Unbiased evaluation of 3D mitochondrial morphometric parameters showed that these SNCA mutant astrocytes had increased mitochondrial fragmentation and decreased mitochondrial connectivity compared to controls, and reduced mitochondrial bioenergetic function. This comprehensive assessment of different pathogenic SNCA mutations derived from PD patients using the same cellular model enabled assessment of the mutation effect, showing that p.A53T and triplication astrocytes were the most severely affected. Together, our results indicate that astrocytes harbouring the familial PD mutations in SNCA are dysfunctional, suggesting a contributory role for dysfunctional astrocytes in the disease mechanism and pathogenesis of PD. Table of Contents Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/053470v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@974d72org.highwire.dtl.DTLVardef@15d9da0org.highwire.dtl.DTLVardef@1177d4eorg.highwire.dtl.DTLVardef@123c0c2_HPS_FORMAT_FIGEXP M_FIG C_FIG Main PointsO_LIWe used a novel serum-free protocol to generate midbrain-specific functional astrocytes from Parkinsons disease patients carrying pathological mutations in SNCA C_LIO_LIPatient-derived astrocytes show morphological and functional impairments C_LI

neuroscience↗

bioRxiv2020

Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser

The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or "digital docents" that allow experts to help make complex images intelligible. We illustrate this idea using Minerva software and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.

scientific communication and education↗

bioRxiv2020

Mitochondria-mitochondria interaction networks show altered topological patterns in Parkinson's disease

Mitochondrial dysfunction is linked to pathogenesis of Parkinsons disease (PD). However, individual-mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria-mitochondria interaction networks (MINs). Here we showed that MINs form non-classical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients, however, altered topological patterns are observed in PD patients. These patterns highly correlate with PD clinical scores and a machine-learning approach based on the MIN features accurately distinguish between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also display specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reverses the changes in MINs of mDANs. Our MIN network analysis opens a new dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.

neuroscience↗

bioRxiv2019

PARK7/DJ-1 promotes pyruvate dehydrogenase activity and maintains Treg homeostasis

Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme into the tricarboxylic acid (TCA) cycle. Here we show that PARK7/DJ-1, a key familial Parkinsons disease (PD) gene, is a pacemaker controlling PDH activity in CD4 regulatory T cells (Tregs). DJ-1 bound to PDH-E1 beta (PDHB), inhibiting the phosphorylation of PDH-E1 alpha (PDHA), thus promoting PDH activity and oxidative phosphorylation (OXPHOS). Dj-1 depletion impaired Treg proliferation and cellularity maintenance in older mice, increasing the severity during the remission phase of experimental autoimmune encephalomyelitis (EAE). The compromised proliferation and differentiation of Tregs in Dj-1 knockout mice were caused via regulating PDH activity. These findings provide novel insight into the already complicated regulatory machinery of the PDH complex and demonstrate that the DJ-1-PDHB axis represents a potent target to maintain Treg homeostasis, which is dysregulated in many complex diseases.

biochemistry↗

bioRxiv2019

Subthreshold psychosis symptoms associated with molecular genetic risk in a population-based cohort: Findings from Generation Scotland

ImportanceSubthreshold psychosis symptoms in the general population may be associated with genetic risk for schizophrenia. In this analysis, empirically-derived symptom factor scores led to a detection of significant and robust polygenic signal.\n\nObjectiveThis study sought to optimize genetic association with data-driven symptom factor scores, accounting for cohort factor structure and sex differences.\n\nDesignEFA-derived symptom factor scores were regressed onto PRS for schizophrenia in models accounting for age and genetic ancestry principal components. Follow-up examination of symptom factor score associations with other related genetic risks included ADHD, autism, bipolar disorder, major depression, and neuroticism.\n\nParticipantsThis study examined the newly expanded symptom dataset from the Northern European ancestry cohort, Generation Scotland: Scottish Family Health Study (N = 9,105 individuals 18-65 years of age) comprising common variant and subthreshold psychosis symptom data. A total of 5,391 females and 3,713 males with age M[SD] = 45.2 [13] were included in the final analyses.\n\nMain Outcome and MeasureSubthreshold psychosis symptoms were measured using the Schizotypal Personality Questionnaire-Brief (SPQ-B). Primary phenotypic factor scores and genome-wide polygenic risk scores (PRS) reflected weighted sum scores and were examined as continuous measures. Polygenic risk scores were calculated from genome-wide association summary statistics using 7,358,674 imputed common genetic variants passing quality control.\n\nResultsIn males, symptom factor scores were positively associated with polygenic risk for schizophrenia alone and implicated primarily interpersonal/negative symptoms. In females, symptom factor scores were positively associated with polygenic risks for ADHD and autism but not schizophrenia. Scores were robustly associated with genetic risk for neuroticism across both males and females.\n\nConclusions and RelevanceThis study detected a significant association of subthreshold psychosis symptoms with genetic risk for schizophrenia and neuroticism in a population-based sample. Furthermore, important sex differences suggest a need for better understanding of schizophrenia risk assessment in females.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat molecular genetic risks are associated with subthreshold psychosis symptoms in the general population?\n\nFindingsIn a large population-based cohort (N = 9,084), significant associations of polygenic risks with symptoms were observed. Symptoms were associated with genetic risk for schizophrenia in males, for ADHD and autism spectrum disorder in females, and for neuroticism across both males and females.\n\nMeaningAssociations of genetic risk with symptoms in the general population are highly significant and suggest important sex differences.

genetics↗

bioRxiv2019

PINK1 Regulates Dopamine and Lipids at Mitochondria to Maintain Synapses and Neuronal Function

Mitochondrial dysfunction contributes to the pathogenesis of Parkinsons disease but it is not clear why inherent mitochondrial defects lead specifically to the death of dopaminergic neurons of the mid brain. PINK1 is mitochondrial kinase and PINK1 mutations cause early onset Parkinsons disease.\n\nWe found that in neuronal progenitors, PINK1 regulates mitochondrial morphology, mitochondrial contact to the endoplasmic reticulum (ER) and the phosphorylation of Miro1. A compensatory metabolic shift towards lipid synthesis provides mitochondria with the components needed for membrane renewal and oxidative phosphorylation, maintaining the mitochondrial network once mature.\n\nCholesterol is increased by loss of PINK1, promoting overall membrane rigidity. This alters the distribution of phosphorylated DAT at synapses and impairs dopamine uptake. PINK1 is required for the phosphorylation of tyrosine hydroxylase at Ser19, dopamine and calcium homeostasis and dopaminergic pacemaking.\n\nWe suggest a novel mechanism for PINK1 pathogenicity in Parkinsons disease in addition to but not exclusive of mitophagy. We also provide a basis for potential therapeutics by showing that low doses of the cholesterol depleting drug {beta}-cyclodextrin reverse PINK1-specific phenotypes.

neuroscience↗

bioRxiv2019

Machine learning-assisted neurotoxicity prediction in human midbrain organoids

A major challenge in the field of neurodegenerative diseases is the poor translation of pre-clinical models to clinical applications. The human brain is an immensely complex structure, which makes it difficult to recapitulate its development, function and disorders. In the recent years, brain organoids derived from human induced pluripotent stem cells have risen as novel tools to study neurodegenerative diseases such as Parkinsons disease (PD). PD is a multifactorial disorder, with aging, genetics and environmental factors as key etiological elements. The majority of the PD cases are idiopathic and proposed to result from a complex interaction between genetic predisposition and environmental exposure. Consequently, the identification of potentially disease causing environmental factors is of critical importance. Organoids, as complex multi-cellular tissue proxies, are an ideal tool to study cellular response to environmental changes. However, with increasing complexity of the system, usage of quantitative tools becomes challenging. This led us to develop an automated high-content image analysis pipeline for image-based cell profiling in the organoid system. Here, we introduce a midbrain organoid system that recapitulates features of neurotoxin-induced PD, representing a platform for machine-learning-assisted prediction of neurotoxicity in high-content imaging data. This model is a valuable tool for advanced in vitro PD modeling and for the screening of putative neurotoxic compounds.

pharmacology and toxicology↗

bioRxiv2019

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

systems biology↗