Biology subjects

Rosenthal, J.

Publications and source records attributed to Rosenthal, J..

9 recordsLinked to original sources

bioRxiv2023

Transcript errors generate a continuous stream of amyloid and prion-like proteins in human cells

Aging is characterized by the accumulation of amyloid and prion-like proteins. However, the molecular mechanisms by which these proteins arise remain unclear. Here, we demonstrate that transcript errors generate amyloid and prion-like proteins in a wide variety of human cell types, including stem cells, brain organoids, and fully differentiated neurons. Intriguingly, some of these proteins are identical to proteins previously implicated in familial cases of amyloid diseases, raising the possibility that both familial and non-familial cases are caused by identical mutant proteins. However, transcript errors also generate amyloid proteins that have not been observed before, suggesting that aging cells are exposed to a second class of pathogenic proteins we are currently unaware of. Finally, we show that transcript errors are readily generated by DNA damage, a hallmark of human aging and a staple of multiple proteotoxic diseases, including Alzheimers disease. Together, these observations greatly expand our understanding of mutagenesis in human aging and disease and suggest a new mechanism by which amyloid diseases can develop.

molecular biology↗

bioRxiv2023

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Background Abstract Background Results Discussion Methods Data Availability Code Availability Author Contributions Competing Interests Extended Data References Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide and is comprised of several histological subtypes1. The clear cell histological subtype (ccRCC) is the most common form of RCC and accounts for the vast majority (75-80%) of metastatic cases1. In addition to highly recurrent mutations in hypoxia (VHL) and chromatin regulator genes (e.g. PBRM1, BAP1, SETD2), ccRCC exhibits extensive genomic intratumoral heterogeneity (ITH)2, which wa ...

cancer biology↗

bioRxiv2022

Multi-omics biomarkers aid prostate cancer prognostication

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

systems biology↗

bioRxiv2021

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.

cancer biology↗

bioRxiv2021

Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem

Computer vision (CV) approaches applied to digital pathology have informed biological discovery and development of tools to help inform clinical decision-making. However, batch effects in the images have the potential to introduce spurious confounders and represent a major challenge to effective analysis and interpretation of these data. Standard methods to circumvent learning such confounders include (i) application of image augmentation techniques and (ii) examination of the learning process by evaluating through external validation (e.g., unseen data coming from a comparable dataset collected at another hospital). Here, we show that the source site of a histopathology slide can be learned from the image using CV algorithms in spite of image augmentation, and we explore these source site predictions using interpretability tools. A CV model trained using Empirical Risk Minimization (ERM) risks learning this source-site signal as a spurious correlate in the weak-label regime, which we abate by using a training method with abstention. We find that a patch based classifier trained using abstention outperformed a model trained using ERM by 9.9, 10 and 19.4% F1 in the binary classification tasks of identifying tumor versus normal tissue in lung adenocarcinoma, Gleason score in prostate adenocarcinoma, and tumor tissue grade in clear cell renal cell carcinoma, respectively, at the expense of up to 80% coverage (defined as the percent of tiles not abstained on by the model). Further, by examining the areas abstained by the model, we find that the model trained using abstention is more robust to heterogeneity, artifacts and spurious correlates in the tissue. Thus, a method trained with abstention may offer novel insights into relevant areas of the tissue contributing to a particular phenotype. Together, we suggest using data augmentation methods that help mitigate a digital pathology models reliance on potentially spurious visual features, as well as selecting models that can identify features truly relevant for translational discovery and clinical decision support.

cancer biology↗

bioRxiv2019

Temporal regulation of nicotinic acetylcholine receptor subunits supports central cholinergic synapse development

Construction and maturation of the postsynaptic apparatus are crucial for synapse and dendrite development. The fundamental mechanisms underlying these processes are most often studied in glutamatergic central synapses in vertebrates. Whether the same principles apply to excitatory cholinergic synapses in the insect central nervous system (CNS) is not known. To address this question, we investigated Drosophila ventral lateral neurons (LNvs) and identified nAchR1 (D1) and nAchR6 (D6) as the main functional nicotinic acetylcholine receptor (nAchR) subunits in these cells. With morphological and calcium imaging studies, we demonstrated their distinct roles in supporting dendrite morphogenesis and synaptic transmission. Furthermore, our analyses revealed a transcriptional upregulation of D1 and downregulation of D6 during larval development, indicating a close association between the temporal regulation of nAchR subunits and synapse maturation. Together, our findings show transcriptional regulation of nAchR composition is a core element of developmental and activity-dependent regulation of central cholinergic synapses.

neuroscience↗

bioRxiv2019

AML ImmunoPET: 64Cu-DOTA-anti-CD33 PET-CT imaging of AML in Xenograft mouse model

PurposeAcute myeloid leukemia (AML) is a highly aggressive form of leukemia that results in poor survival outcomes. Currently, diagnosis and prognosis are based on invasive single-point bone marrow biopsies (iliac crest). There is currently no AML-specific non-invasive imaging method to assess disease in the whole body, including in extramedullary organs, representing an unmet clinical need. About 85-90% of human myeloid leukemia cells express CD33 (an accepted biomarker for AML) cell surface receptors, highlighting CD33 as an ideal candidate for AML immunoPET.\n\nExperimental DesignIn this study, we evaluated if 64Cu-DOTA-anti-CD33 murine monoclonal antibody (mAb) can be used for ImmunoPET-CT imaging of AML in preclinical model. For translational purpose, a humanized anti-CD33 antibody was produced, and after confirming its anti-CD33 PET-CT imaging ability we further explored its therapeutic potential.\n\nResults64Cu-DOTA-anti-CD33 based PET-CT imaging detected CD33+ AML in mice with high sensitivity (95.65%) and specificity (100%). In the entire skeletal system, CD33+ PET activity was significantly high in the bones of AML bearing mice over non-leukemic control mice; femur (p<0.00001), tibia (p=0.0001), humerus (p=0.0014), and lumber spine (p<0.00001). Interestingly, the imaging showed CD33+ PET activity prevelant in epiphysis/diaphysis of the bones, indicating spatial heterogeneity. Anti-CD33 therapy using newly developed humanized anti-CD33 mAb as an ADC (p=0.02) and 225Ac-anti-CD33-RIT (p<0.00001), significantly reduced disease burden over that of respective controls.\n\nConclusionWe have successfully developed a novel anti-CD33 immunoPET-CT based non-invasive imaging tool for diagnosis of AML and its spatial distribution, which indicates a preferential skeletal niche.\n\nTranslational RelevanceAcute myeloid leukemia (AML) exhibits extensive heterogeneity in therapeutic response, even in patients sharing similar characteristics, indicating the inadequacy of currently used biopsy-based prognostic tools. There is a critical need for non-invasive quantitative imaging modality specific to AML, which is able to assess this disease in the whole body, including in extramedullary organs and to longitudinally monitor treatment response. CD33, an accepted AML biomarker is expressed on more than 85-90% of blast cells and Mylotarg (anti-CD33-ADC) is FDA approved immunotherapy for AML. In this preclinical study using anti-human CD33 mAb (64Cu-DOTA-anti-CD33), a CD33 PET-CT imaging modality was developed that detected AML with high sensitivity and specificity. The approach also indicated spatial heterogeneity in leukemic disease, dependent on disease burden, which warrants caution in analyzing results from single point bone marrow biopsies. Therefore, this imaging modality can be used to non-invasively detect AML in humans and also monitor treatment response, an unmet clinical need.

cancer biology↗

bioRxiv2019

Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

genomics↗

bioRxiv2016

Resolving the Functional Significance of BRCA1 RING Domain Missense Substitutions

Part 1Development and calibration of suitably accurate functional assays for BRCA1 RING domain and BRCT domain missense substitutions could dramatically accelerate clinical classification of rare missense substitutions observed in that gene. Leveraging data from 68,000 full sequence tests of BRCA1 and BRCA2, plus data from the limited number of already classified BRCA1 RING domain missense substitutions, we used logistic regression and related techniques to evaluate three BRCA1 RING domain assays. These were recently described high throughput yeast 2-hybrid and E3 ubiquitin ligase assays, plus a newly developed mammalian 2-hybrid assay. While there were concerns about the accuracy of the yeast 2-hybrid assay and the indirect nature of the ubiquitin ligase assay, the mammalian 2-hybrid assay had excellent correlation with existing missense substitution classifications. After calibration, this assay contributed to classification of one newly reported BRCA1 missense substitution. In principal, the mammalian 2-hybrid assay could be converted to a high-throughput format that would likely retain suitable accuracy. Part 2How does one achieve clinically applicable classification of the vast majority of all possible sequence variants in disease susceptibility genes? BRCA1 is a high-risk susceptibility gene for breast and ovarian cancer. Pathogenic protein truncating variants are scattered across the open reading frame, but all known missense substitutions that are pathogenic because of missense dysfunction are located in either the amino-terminal RING domain or the carboxy-terminal BRCT domain. Heterodimerization of the BRCA1 and BARD1 RING domains is a molecularly defined obligate activity. Hence, we tested every BRCA1 RING domain missense substitution that can be created by a single nucleotide change for heterodimerization with BARD1 in a Mammalian 2-hybrid (M2H) assay. Downstream of the M2H laboratory assay, we addressed three additional challenges: assay calibration, validation thereof, and integration of the calibrated results with other available data such as computational evidence and patient/population observational data to achieve clinically applicable classification. Overall, we found that about 20% of BRCA1 RING domain missense substitutions are pathogenic. Using a Bayesian point system for data integration and variant classification, we achieved clinical classification of about 89% of observed missense substitutions. Moreover, among missense substitutions not present in the human observational data used here, we find an additional 47 with concordant computational and functional assay evidence in favor of pathogenicity; these are particularly likely to be classified as Likely Pathogenic once human observational data become available.

genetics↗