Biology subjects

Koller, D.

Publications and source records attributed to Koller, D..

12 recordsLinked to original sources

bioRxiv2024

Blockade of Interleukin-6 (IL-6) Signaling in Dedifferentiated Liposarcoma (DDLPS) Decreases Mouse Double Minute 2 (MDM2) Oncogenicity via Alternative Splicing

Effective therapies for retroperitoneal (RP) dedifferentiated liposarcoma (DDLPS) remain unavailable. Loco-regional recurrence occurs in >80% of cases; 5-year disease-specific survival is only 20%. DDLPS is especially prevalent in the retroperitoneum and abdomen; evaluation of the DDLPS microenvironment in these high-fat compartments appears pertinent. Adipose is a main supplier of interleukin-6 (IL6); excessive activation of IL6 signal transducer glycoprotein 130 (GP130) underlies the development of some diseases. The role of GP130 pathway activation remains unstudied in DDLPS, so we examined the role of microenvironment fat cell activation of the IL6/GP130 signaling cascade in DDLPS. All DDLPS tumors and cell lines studied expressed elevated levels of the GP130-encoding gene IL6ST and GP130 protein compared to normal tissue and cell line controls. IL6 increased DDLPS cell growth and migration, possibly through increased signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) activation, and upregulated mouse double minute 2 (MDM2). GP130 loss conveyed opposite effects; pharmacological blockade of GP130 by SC144 produced the MDM2 splice variant MDM2-ALT1, known to inhibit full length MDM2 (MDM2-FL). Although genomic MDM2 amplification is pathognomonic for DDLPS, mechanisms driving MDM2 expression, regulation, and function beyond the MDM2:p53 negative feedback loop are poorly understood. Our findings suggest a novel preadipocyte DDLPS-promoting role due to IL6 release, via upregulation of DDLPS MDM2 expression. Pharmacological GP130 blockade reduced the IL6-induced increase in DDLPS MDM2 mRNA and protein levels, possibly through enhanced expression of MDM2-ALT1, a possibly targetable pathway with potential as future DDLPS patient therapy.

molecular biology↗

bioRxiv2024

Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes

Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

neuroscience↗

bioRxiv2023

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

Machine learning (ML)-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the mean{chi} 2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits, and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEMs utility by evaluating embeddings and multivariate traits in two separate datasets: i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance, and ii) a real data from the UK Biobank including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

bioinformatics↗

bioRxiv2023

Pitfalls in performing genome-wide association studies on ratio traits

Genome-wide association studies (GWAS) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator-driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

genetics↗

bioRxiv2023

A Pooled Cell Painting CRISPR Screening Platform Enables de novo Inference of Gene Function by Self-supervised Deep Learning

Pooled CRISPR screening has emerged as a powerful method of mapping gene functions thanks to its scalability, affordability, and robustness against well or plate-specific confounders present in array-based screening 1-6. Most pooled CRISPR screens assay for low dimensional phenotypes (e.g. fitness, fluorescent markers). Higher-dimensional assays such as perturb-seq are available but costly and only applicable to transcriptomics readouts 7-11. Recently, pooled optical screening, which combines pooled CRISPR screening and microscopy-based assays, has been demonstrated in the studies of the NFkB pathway, essential human genes, cytoskeletal organization and antiviral response 12-15. While the pooled optical screening methodology is scalable and information-rich, the applications thus far employ hypothesis-specific assays. Here, we enable hypothesis-free reverse genetic screening for generic morphological phenotypes by re-engineering the Cell Painting 16 technique to provide compatibility with pooled optical screening. We validated this technique using well-defined morphological genesets (124 genes), compared classical image analysis and self-supervised learning methods using a mechanism-of-action (MoA) library (300 genes), and performed discovery screening with a druggable genome library (1640 genes) 17. Across these three experiments we show that the combination of rich morphological data and deep learning allows gene networks to emerge without the need for target-specific biomarkers, leading to better discovery of gene functions.

genomics↗

bioRxiv2022

An allelic series rare variant association test for candidate gene discovery

Allelic series are of candidate therapeutic interest due to the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a gene in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and develop a gene-based rare variant association test specifically targeted for the identification of allelic series. Building on the well-known burden and sequence kernel association (SKAT) tests, we specify a variety of association models, covering different genetic architectures, and integrate these into a COding-variant Allelic Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic series for 4 circulating lipid traits and 5 cell count traits among 145,735 subjects with available whole exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni significant associations with circulating lipid traits, on average, and 82% more with cell count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, N = 400K), or from common variant associations in the GWAS catalog. In addition to detecting many gene-trait associations present in Genebass using only a fraction (36.9%) of the sample, COAST detects associations, such as ANGPTL4 with triglycerides, that are absent from Genebass but which have clear common variant support.

genetics↗

bioRxiv2022

The enrichment of self-domestication and neural crest function loci in the heritability of neurodevelopmental disorders is not independent of genomic regulatory functions

Self-domestication could play an important role in contributing to shape the biology of human brain and the predisposition to neurodevelopmental disorders. Leveraging genome-wide data from the Psychiatric Genomics Consortium, we tested the enrichment of self-domestication and neural crest function loci with respect to the heritability of autism spectrum disorder, schizophrenia (SCZ in East Asian and European ancestries, EAS and EUR, respectively), attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, and Tourettes syndrome (TS). Considering only self-domestication and neural-crest-function annotations in the linkage disequilibrium score regression (LDSC) model, our partitioned heritability analysis revealed statistically significant enrichments across all disorders investigated. The estimates of the heritability enrichments for self-domestication loci were similar across neurodevelopmental disorders, ranging from 0.902 (EAS SCZ, p=4.55x10-20) to 1.577 (TS, p=5.85x10-5). Conversely, a wider spectrum of heritability enrichment estimates was present for neural crest function with the highest enrichment observed for TS (enrichment=3.453, p=2.88x10-3) and the lowest for EAS SCZ (enrichment=1.971, p=3.8lx10-3). Although these estimates appear to be strong, the enrichments for self-domestication and neural crest function were null once we included additional annotations related to different genomic features. This indicates that the effect of self-domestication on the polygenic architecture of neurodevelopmental disorders is not independent of other functions of human genome.

genetics↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program

The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic change over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). The MVP was divided into five birth cohorts (N-range=123,888 [born from 1943-1947] to 136,699 [born from 1948-1953]). Groups of participants were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP+HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010<Cohens d<0.259, p<7.80x10-4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept=1.08{+/-}0.042). The 1kGP+HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction=0.045{+/-}0.007, p<0.05) confounding in the GWAS statistics. This study provides a comprehensive characterization of ancestry diversity of the MVP cohort over time and highlights that more refined modeling of genetic diversity (e.g., the 1kGP+HGDP-based ancestry assignment) can more accurately capture the polygenic architecture of traits and diseases that could be affected by population stratification.

genomics↗

bioRxiv2021

The impact of evolutionary processes in shaping the genetics of complex traits in East Asia and Europe: a specific contribution from Denisovan and Neanderthal introgression

Evidence of how human evolution shaped the polygenicity of human traits and diseases has been extensively studied in populations of European descent. However, limited information is currently available about its impact on other ancestry groups. Here, we investigated how different evolutionary processes affected the common variant heritability of traits and diseases in East Asians. Leveraging genome-wide association statistics from the Biobank Japan (up to 158,284 participants), we assessed natural selection (negative and positive), archaic introgression from Neanderthal and Denisova, and several genomic functional categories with respect to the heritability of physiological and pathological conditions. Similar to reports in European descent populations, the heritability estimates for East Asian traits were ubiquitously enriched for negative selection annotations (false discovery rate, FDR q<0.05). Enrichment of Denisovan introgression was identified in coronary artery disease (1.69-fold enrichment, p=0.003). We followed up these enrichments by conducting a phenome-wide association study (PheWAS) of Denisovan and Neanderthal alleles in participants of six ancestral backgrounds from the UK Biobank. In East Asians, Denisovan-inherited alleles were associated with 22 phenotypes, including metabolic, immunological, cardiovascular, endocrine, and dermatological traits. The strongest association was observed for the Denisovan-inherited locus rs59185462 with rheumatoid arthritis (beta=0.82, p=1.91x10-105). In summary, our study provides the first evidence regarding the impact of evolutionary processes on the genetics of complex traits in worldwide populations, highlighting the specific contribution of Denisovan introgression in East Asian populations.

genetics↗

bioRxiv2020

Metabolic effects of aripiprazole and olanzapine multiple-dose treatment in healthy volunteers. Association with pharmacogenetics.

BackgroundAripiprazole and olanzapine are atypical antipsychotics. Both drugs can induce metabolic changes, however, the metabolic side effects produced by aripiprazole are more benign. ObjectivesTo evaluate if aripiprazole and olanzapine alter prolactin levels, lipid and glucose metabolism and hepatic, hematological, thyroid and renal function. MethodsTwenty-four healthy volunteers received 5 daily oral doses of 10 mg aripiprazole and 5 mg olanzapine in a crossover randomized clinical trial and were genotyped for 51 polymorphisms in 17 genes by qPCR. Drug plasma concentrations were measured by LC-MS. The biochemical and hematological analyses were performed by enzymatic methods. ResultsOlanzapine induced hyperprolactinemia but not aripiprazole. DRD3 Ser/Gly and ABCB1 rs10280101, rs12720067 and rs11983225 polymorphisms and CYP3A phenotype had an impact on plasma prolactin levels. C-peptide concentrations were higher after aripiprazole administration and were influenced by COMT rs4680 and rs13306278 polymorphisms. Olanzapine and the UGT1A1 rs887829 polymorphism were associated with elevated glucose levels. CYP3A poor metabolizers had increased insulin levels. Triglyceride concentrations were decreased due to olanzapine and aripiprazole treatment and were variable based on CYP3A phenotypes and the APOC3 rs4520 genotype. Cholesterol levels were also decreased and depended on HTR2A rs6314 polymorphism. All hepatic enzymes, platelet and albumin levels and prothrombin time were altered during both treatments. Additionally, olanzapine reduced the leucocyte count, aripiprazole increased free T4 and both decreased uric acid concentrations. ConclusionsShort term treatment with aripiprazole and olanzapine had a significant influence on the metabolic parameters. However, it seems that aripiprazole provokes less severe metabolic changes.

pharmacology and toxicology↗

bioRxiv2018

Conservation and divergence in modules of the transcriptional programs of the human and mouse immune systems

Studies in mouse have shed important light on human hematopoietic differentiation and disease. However, substantial differences between the two species often limit the translation of findings from mouse to human.\n\nHere, we compare previously defined modules of co-expressed genes in human and mouse immune cells based on compendia of genome-wide profiles. We show that the overall modular organization of the transcriptional program is conserved. We highlight modules of co-expressed genes in one species that dissolve or split in the other species. Many of the associated regulatory mechanisms - as reflected by computationally inferred trans regulators, or enriched cis-regulatory elements - are conserved between the species. Nevertheless, the degree of conservation in regulatory mechanism is lower than that of expression, suggesting that distinct regulation may underlie some of the conserved transcriptional responses.

bioinformatics↗