Biology subjects

Brandes, N.

Publications and source records attributed to Brandes, N..

16 recordsLinked to original sources

bioRxiv2025

P-KNN: Maximizing variant classification evidence through joint calibration of multiple pathogenicity prediction tools

Clinical guidelines for Mendelian disease diagnosis require that outputs from variant pathogenicity prediction tools be converted into well-calibrated probabilities. However, the existing calibration process is only valid when pre-committing to a specific tool, preventing clinicians from using multiple tools with complementary strengths. To lift this restriction, we introduce Pathogenicity K-Nearest Neighbors (P-KNN), a simple, flexible method that jointly calibrates any set of tools. P-KNN scores each variant by the fraction of pathogenic variants among those with the most similar scores across all underlying tools. We compared P-KNN to single-tool calibration over 13 real tools, including two meta-predictors. Compared to BayesDel, the best-performing tool, P-KNN produced better-calibrated probabilities and stronger evidence strength, with mean log likelihood ratios of 2.56 vs. 2.24 for pathogenic variants, 2.29 vs. 1.56 for benign variants, and 2.28 vs. 2.07 for variants of uncertain significance. We also evaluated P-KNN at four historical time points to assess scalability and robustness, finding that it improved steadily as new tools became available, while remaining well-calibrated. It also correctly integrated computational predictions with experimental measurements, whereas guidelines-based evidence summation systematically overestimated pathogenicity. In summary, P-KNN allows full flexibility to work with any set of tools in a reliable and robust manner. It is consistent with clinical variant classification guidelines, making it well positioned to improve diagnostic yield for rare genetic diseases. P-KNN is available via command line (https://github.com/Brandes-Lab/P-KNN) and precomputed scores (Dataset: https://huggingface.co/datasets/brandeslab/P-KNN, User Interface: https://huggingface.co/spaces/brandeslab/P-KNN-Viewer).

bioinformatics↗

bioRxiv2025

Genomic heterogeneity inflates the performance of variant pathogenicity predictions

Recent studies have reported unprecedented accuracy predicting pathogenic variants across the genome, including in noncoding regions, using large AI models trained on vast genomic data. We present a comprehensive evaluation of these frontier models, showing that performance is inflated by differences in the prevalence of pathogenic variants across genomic contexts. We identify the best-performing models for each variant type and establish a benchmark to guide future progress.

bioinformatics↗

bioRxiv2025

Proteasome mutations associated with CANDLE syndrome cause altered neuronal development by dysregulating polyamine synthesis

Genetic mutations affecting proteasome function can result in multi-organ diseases, such as Chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature (CANDLE) syndrome. Neurological symptoms associated with CANDLE suggest that proteasomal mutations may impact neuronal development and/or function. We generated cerebral organoids (COs) from CANDLE patient induced pluripotent stem cells (iPSCs), which exhibited impaired neuronal development when compared to COs from healthy control iPSCs. Impaired neuronal maturation in CANDLE COs was correlated with increased polyamines, which were also elevated in CANDLE patient CSF. The proteasome-regulated Ornithine decarboxylase (ODC), a rate limiting enzyme for polyamines, was elevated in CANDLE neurons. Inhibition of ODC reversed polyamine overproduction and repaired neuronal maturation in CANDLE COs, suggesting a potential therapeutic avenue for intervention. These findings demonstrate that dysfunction of the proteasome affects neuronal development through overproduction of polyamines via dysregulation of ODC and offer insight into potential therapeutic strategies for CNS-related proteasomal dysfunction.

neuroscience↗

bioRxiv2025

Phenotypic pleiotropy of missense variants in human B cell-confinement receptor P2RY8

Missense variants can have pleiotropic effects on protein function and predicting these effects can be difficult. We performed near-saturation deep mutational scanning of P2RY8, a G-protein-coupled receptor that promotes germinal center B cell confinement. We assayed the effect of each variant on surface expression, migration, and proliferation. We delineated variants that affected both expression and function, affected function independently of expression, and discrepantly affected migration and proliferation. We also used cryo-electron microscopy to determine the structure of activated, ligand-bound P2RY8, providing structural insights into the effects of variants on ligand binding and signal transmission. We applied the deep mutational scanning results to both improve computational variant effect predictions and to characterize the phenotype of germline variants and lymphoma-associated variants. Together, our results demonstrate the power of integrating deep mutational scanning, structure determination, and in silico prediction to advance the understanding of a receptor important in human health.

genomics↗

bioRxiv2023

Single cell sequencing as a general variant interpretation assay

The human genome contains [~]70 million possible protein-altering variants, the vast majority of which are of uncertain clinical significance. Closing this gap is essential for accurate diagnosis of disease-causing variants and understanding their mechanisms of action. Towards this goal, we developed a pooled perturbation approach combining saturation mutagenesis with single cell RNA sequencing to map the effects of every single nucleotide variant in a gene. We sequenced [~]440,000 cells expressing variants in CDKN2A (p16INK4a), TP53, and SOD1, observing almost all possible protein-coding variants, with a mean of 61 cells per variant. Using single cell gene expression signatures, we show that each gene may contain multiple types of pathogenic variants that affect distinct downstream pathways. We demonstrate that single cell expression signatures outperform existing bulk experimental assays and computational models for predicting pathogenicity, and summarize both the utility and potential limitations of single cell sequencing as a general variant interpretation assay.

genomics↗

bioRxiv2023

ADPKD-Causing Missense Variants in Polycystin-1 Disrupt Cell Surface Localization or Polycystin Channel Function

Autosomal dominant polycystic kidney disease (ADPKD) is the leading monogenic cause of kidney failure and affects millions of people worldwide. Despite the prevalence of this monogenic disorder, our limited mechanistic understanding of ADPKD has hindered therapeutic development. Here, we successfully developed bioassays that functionally classify missense variants in polycystin-1 (PC1). Strikingly, ADPKD pathogenic missense variants cluster into two major categories: 1) those that disrupt polycystin cell surface localization or 2) those that attenuate polycystin ion channel activity. We found that polycystin channels with defective surface localization could be rescued with a small molecule. We propose that small-molecule-based strategies to improve polycystin cell surface localization and channel function will be effective therapies for ADPKD patients.

cell biology↗

bioRxiv2023

Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells

CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the chromosome, including in pre-clinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells,1 dramatically reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic.

cell biology↗

bioRxiv2023

Selecting Covariates for Genome-Wide Association Studies

The choice of which covariates to include in a Genome-Wide Association Study (GWAS) is important since it affects the ability to detect true association signal of variants, to correct for confounders and avoid false positives, and the running time of the analysis. Commonly used covariates include age, sex, genotyping batches, genotyping array type, as well as an arbitrary number of Principal Components (PCs) used to adjust for population structure. Despite the importance of this issue, there is no consensus or clear guidelines for the right choice of covariates. Therefore, studies typically employ heuristics for their choice with no clear justification. Here, we explore the dependence of the GWAS analysis results on the choice of covariates for a wide range of quantitative and binary human phenotypes. We propose guidelines for covariates choice based on the phenotypes type (quantitative vs. disease), the heritability, and the disease prevalence, with the goal of maximizing the statistical power to detect true associations and fit accurate polygenic scores while avoiding spurious associations and minimizing computation time. We analyze 36 traits in the UK-Biobank dataset. We show that the genotype batch and assessment center can be safely removed as covariates, thus significantly reducing the GWAS computational burden for these traits.

genetics↗

bioRxiv2022

Genome-wide prediction of disease variants with a deep protein language model

Distinguishing between damaging and neutral missense variants is an ongoing challenge in human genetics, with profound implications for clinical diagnosis, genetic studies and protein engineering. Recently, deep-learning models have achieved state-of-the-art performance in classifying variants as pathogenic or benign. However, these models are currently unable to provide predictions over all missense variants, either because of dependency on close protein homologs or due to software limitations. Here we leveraged ESM1b, a 650M-parameter protein language model, to predict the functional impact of human coding variation at scale. To overcome existing technical limitations, we developed a modified ESM1b workflow and functionalized, for the first time, all proteins in the human genome, resulting in predictions for all [~]450M possible missense variant effects. ESM1b was able to distinguish between pathogenic and benign variants across [~]150K variants annotated in ClinVar and HGMD, outperforming existing state-of-the-art methods. ESM1b also exceeded the state of the art at predicting the experimental results of deep mutational scans. We further annotated [~]2M variants across [~]9K alternatively-spliced genes as damaging in certain protein isoforms while neutral in others, demonstrating the importance of considering all isoforms when functionalizing variant effects. The complete catalog of variant effect predictions is available at: https://huggingface.co/spaces/ntranoslab/esm_variants.

bioinformatics↗

bioRxiv2021

ProteinBERT: A universal deep-learning model of protein sequence and function

Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme consists of masked language modeling combined with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to very large sequence lengths. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains state-of-the-art performance on multiple benchmarks covering diverse protein properties (including protein structure, post translational modifications and biophysical attributes), despite using a far smaller model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data. Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.

bioinformatics↗

bioRxiv2020

The FABRIC Cancer Portal: A Ranked Catalogue of Gene Selection in Tumors over the Human Coding Genome

We present The FABRIC Cancer Portal, a comprehensive catalogue of gene selection in cancer covering the entire human coding genome (~18,000 protein-coding genes) across 33 cancer types and pan-cancer. Genes in the portal are ranked according to the strength of evidence for selection in tumor, based on rigorous and robust statistics. Gene selection is quantified by combining genomic data with rich proteomic annotations. The portal includes a selected set of cross-references to the most relevant resources providing genomic, proteomic and cancer-related information. We showcase the portal with known and overlooked cancer genes, demonstrating the usefulness of the portal via its simple visual interface that allows to pivot between gene-centric and cancer-type views. The portal is available at fabric-cancer.huji.ac.il.

cancer biology↗

bioRxiv2020

Recessive effects in cancer predisposition exposed by genome-wide and proteome-wide association studies

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Current attempts to detect cancer predisposition genomic regions are typically based on small-scale familial studies or genome-wide association studies (GWAS) over dedicated case-control cohorts. In this study, we utilized the UK Biobank as a large-scale prospective cohort to conduct a comprehensive analysis of cancer predisposition using both GWAS and proteome-wide association study (PWAS), a method that highlights genetic associations mediated by functional alterations to protein-coding genes. We discovered 137 unique genomic loci implicated with cancer risk in the white British population across nine cancer types and pan-cancer. While most of these genomic regions are supported by external evidence, our results highlight novel loci as well. We performed a comparative analysis of cancer predisposition between cancer types, finding that most of the implicated regions are cancer-type specific. We further analyzed the role of recessive genetic effects in cancer predisposition. We found that 30 of the 137 cancer regions were recovered only by a recessive model, highlighting the importance of recessive inheritance outside of familial studies. Finally, we show that many of the cancer associations exert substantial cancer risk in the studied cohort, suggesting their clinical relevance.

genetics↗

bioRxiv2020

Evolutionary and Functional Lessons from Human-Specific Amino-Acid Substitution Matrices

The characterization of human genetic variation in coding regions is fundamental to our understanding of protein function, structure, and evolution. Amino-acid (AA) substitution matrices such as BLOSUM (BLOcks SUbstitution Matrix) and PAM (Point Accepted Mutations) encapsulate the stochastic nature of such proteomic variation and are used in studying protein families and evolutionary processes. However, these matrices were constructed from protein sequences spanning long evolutionary distances and are not designed to reflect polymorphism within species. To accurately represent proteomic variation within the human population, we constructed a set of human-centric substitution matrices derived from genetic variations by analyzing the frequencies of >4.8M single nucleotide variants (SNVs). These human-specific matrices expose short-term evolutionary trends at both codon and AA resolution and therefore present an evolutionary perspective that differs from that implicated in the traditional matrices. Specifically, our matrices consider the directionality of variants, and uncover a set of AA pairs that exhibit a strong tendency to substitute in a specific direction. We further demonstrate that the substitution rates of nucleotides only partially determine AA substitution rates. Finally, we investigate AA substitutions in post-translational modification (PTM) and ion-binding sites. We confirm a strong propensity towards conservation of the identity of the AA that participates in such functions. The empirically-derived human-specific substitution matrices expose purifying selection over a range of residue-based protein properties. The new substitution matrices provide a robust baseline for the analysis of protein variations in health and disease. The underlying methodology is available as an open-access to the biomedical community.

genomics↗

bioRxiv2019

PWAS: Proteome-Wide Association Study

Over the last two decades, GWAS (Genome-Wide Association Study) has become a canonical tool for exploratory genetic research, generating countless gene-phenotype associations. Despite its accomplishments, several limitations and drawbacks still hinder its success, including low statistical power and obscurity about the causality of implicated variants. We introduce PWAS (Proteome-Wide Association Study), a new method for detecting protein-coding genes associated with phenotypes through protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the proteins function using machine-learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. By collecting the genetic signal across many variants in light of their rich proteomic context, PWAS can detect subtle patterns that standard GWAS and other methods overlook. It can also capture more complex modes of heritability, including recessive inheritance. Furthermore, the discovered associations are supported by a concrete molecular model, thus reducing the gap to inferring causality. To demonstrate its applicability for a wide range of human traits, we applied PWAS on a cohort derived from the UK Biobank (~330K individuals) and evaluated it on 49 prominent phenotypes. We compared PWAS to existing methods, proving its capacity to recover causal protein-coding genes and highlighting new associations with plausible biological mechanism.

bioinformatics↗

bioRxiv2018

Substantial Batch Effects in TCGA Exome Sequences Undermine Pan-Cancer Analysis of Germline Variants

BackgroundIn recent years, research on cancer predisposition germline variants has emerged as a prominent field. The identity of somatic mutations is based on a reliable mapping of the patient germline variants. In addition, the statistics of germline variants frequencies in healthy individuals and cancer patients is the basis for seeking candidates for cancer predisposition genes. The Cancer Genome Atlas (TCGA) is one of the main sources of such data, providing a diverse collection of molecular data including deep sequencing for more than 30 types of cancer from >10,000 patients.\n\nMethodsOur hypothesis in this study is that whole exome sequences from healthy blood samples of cancer patients are not expected to show systematic differences among cancer types. To test this hypothesis, we analyzed common and rare germline variants across six cancer types, covering 2,241 samples from TCGA. In our analysis we accounted for inherent variables in the data including the different variant calling protocols, sequencing platforms, and ethnicity.\n\nResultsWe report on substantial batch effects in germline variants associated with cancer types. We attribute the effect to the specific sequencing centers that produced the data. Specifically, we measured 30% variability in the number of reported germline variants per sample across sequencing centers. The batch effect is further expressed in nucleotide composition and variant frequencies. Importantly, the batch effect causes substantial differences in germline variant distribution patterns across numerous genes, including prominent cancer predisposition genes such as BRCA1, RET, MAX, and KRAS. For most of known cancer predisposition genes, we found a distinct batch-dependent difference in germline variants.\n\nConclusionTCGA germline data is exposed to strong batch effects with substantial variabilities among TCGA sequencing centers. We claim that those batch effects are consequential for numerous TCGA pan-cancer studies. In particular, these effects may compromise the reliability and the potency to detect new cancer predisposition genes. Furthermore, interpretation of pan-cancer analyses should be revisited in view of the source of the genomic data after accounting for the reported batch effects.

cancer biology↗

bioRxiv2018

Modeling Functional Genetic Alteration in Cancer Reveals New Candidate Driver Genes

Compiling the catalogue of genes actively involved in tumorigenesis (known as cancer drivers) is an ongoing endeavor, with profound implications to the understanding of tumorigenesis and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Most methods rely on detecting genes displaying excessive mutation rates compared to some background model. This approach is susceptible to false discoveries, due to its sensitivity to the assumptions of the background model, such as the need to account for hyper-mutated samples, cancer types and genomic loci. We present a fundamentally different approach. Instead of focusing on the number of mutations, we examine their content, and their expected effects on the functions of genes. We use a machine-learning model to predict functional effect scores of somatic mutations. For each gene, we compare the distribution of observed effect scores with the distribution expected at random, and report genes showing significant bias. By applying our framework on the ~20k protein-coding human genes, we detected 593 genes showing significant bias towards harmful mutations in the context of cancer. In contrast, we found only 6 significant genes biased in the opposite direction. The list of 593 genes, constructed without any prior knowledge of their role in cancer, shows an overwhelming overlap with known cancer driver genes, but also highlights many overlooked genes. These overlooked genes are promising candidates for novel cancer drivers. Our model is generic and is not restricted to the context of cancer. Applying the same framework to data of human-population genetic variation reveals the opposite trend. Unlike cancer, which is dominated by a bias towards harmful mutations, long-term evolution in healthy individuals results a bias towards less harmful mutations. The underlying assumptions of our framework are minimal, making it ideal for analyzing genetic data in search of genes subjected to positive or negative selection. It is fully open sourced and available for installation and use. Our framework presents a substantial development towards the application of state-of-the-art machine-learning algorithms in genetic studies.

bioinformatics↗