Biology subjects

Rahimi, F.

Publications and source records attributed to Rahimi, F..

4 recordsLinked to original sources

bioRxiv2026

POME: Graph-based embeddings for partially observed mixed-type data

Partially observed mixed-type (POM) data, as often encountered in clinical, epidemiological, and phenotypic datasets, are very common in biomedical research. Yet, advanced data analysis and machine learning based on POM data is complicated by their heterogeneous nature and often substantial fractions of missing values. While one promising way to overcome these issues is to compute vector-valued embeddings for POM datasets which can then be used for downstream analyses, existing embedding methods are mostly not designed for POM data. To address this gap, we developed POME (partially observed mixed-type data embeddings), a self-supervised model that yields low-dimensional representations of both samples and variables, using shared concept learning and a bipartite graph representation of the underlying POM data. We validated POME through extensive experiments on three real-world biomedical datasets, with diverse downstream tasks and objectives: POME achieves state-of-the-art imputation performance and produces high-quality patient representations that support not only unsupervised discovery of well-separated and clinically meaningful patients subgroups but also supervised predictive modeling and zero-shot representation space mining for use cases such as adjuvant therapy modality recommendation. POME is available as a Python package on GitHub (https://github.com/bionetslab/POME) and PyPI (https://pypi.org/project/pome-py).

bioinformatics↗

bioRxiv2026

Genetic background shapes AI-predicted variant effects

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

genomics↗

bioRxiv2025

Evolution of the rate, spectrum, and fitness effects of mutation under minimal selection in Caenorhabditis elegans

The rate, molecular spectrum, and fitness effects of mutations vary at all levels of the biological hierarchy, from within individual genomes to among taxonomic domains. Understanding the evolutionary factors underpinning that variation is of fundamental importance to biology. Accurate quantification of the properties of mutations requires that other evolutionary forces, especially natural selection, be minimized as much as possible. To investigate the evolution of the mutational process in C. elegans, we propagated a set of 100 "first order" mutation accumulation (O1MA) lines under minimal selection for [~]150 generations, divided each O1MA line into two "second order" MA (O2MA) lines and propagated them for another [~]150 generations, at which time the genome of each O2MA line was sequenced, and a subset of 50 O1MA families was assayed for competitive fitness. Over the course of the experiment, the mean nucleotide substitution mutation rate did not change, but the variance increased. In contrast, the indel mutation rate increased significantly. The two types of mutations fulfill the predictions of different theoretical models for the evolution of mutation rate. These results reinforce previous findings that the rate of indels is more sensitive to endogenous stress than the rate of nucleotide substitutions. Several evolutionary quandaries could be resolved if deleterious mutations interact synergistically (negative epistasis). Evidence for synergistic epistasis is famously inconclusive, although there is reason to think it may be more detectable under competitive conditions. However, a model of constant mutational effects on competitive fitness explains the results significantly better than a model including epistasis.

evolutionary biology↗

bioRxiv2019

Host porphobilinogen deaminase deficiency confers malaria resistance in Plasmodium chabaudi but not in Plasmodium falciparum or Plasmodium berghei during intraerythrocytic growth

An important component in host resistance to malaria infection are inherited mutations that give rise to abnormalities and deficiencies in erythrocyte proteins and enzymes. Understanding how such mutations confer protection against the disease may be useful for developing new treatment strategies. A mouse ENU-induced mutagenesis screen for novel malaria resistance-conferring mutations identified a novel nonsense mutation in the gene encoding porphobilinogen deaminase (PBGD) in mice, denoted here as PbgdMRI58155. Heterozygote PbgdMRI58155 mice exhibited approximately 50% reduction in cellular PBGD activity in both mature erythrocytes and reticulocytes, although enzyme activity was approximately 10 times higher in reticulocytes than erythrocytes. When challenged with blood-stage P. chabaudi, which preferentially infects erythrocytes, heterozygote mice showed a modest but significant resistance to infection, including reduced parasite growth. A series of assays conducted to investigate the mechanism of resistance indicated that mutant erythrocyte invasion by P. chabaudi was normal, but that following intraerythrocytic establishment a significantly greater proportions of parasites died and therefore affected their ability to propagate. The Plasmodium resistance phenotype was not recapitulated in Pbgd-deficient mice infected with P. berghei, which prefers reticulocytes, or when P. falciparum was cultured in erythrocytes from patients with acute intermittent porphyria (AIP), which had modest (20-50%) reduced levels of PBGD. Furthermore, the growth of Pbgd-null P. falciparum and Pbgd-null P. berghei parasites, which grew at the same rate as their wild-type counterparts in normal cells, were not affected by the PBGD-deficient background of the AIP erythrocytes or Pbgd-deficient mice. Our results confirm the dispensability of parasite PBGD for P. berghei infection and intraerythrocytic growth of P. falciparum, but for the first time identify a requirement for host erythrocyte PBGD by P. chabaudi during in vivo blood stage infection. IMPORTANCEThe causative agent of malaria, Plasmodium, adopts a parasitic lifestyle during erythrocyte infection, and as such relies on host cell factors for its survival and growth. Host-encoded mutations that alter the availability of these factors confer disease resistance, including several well-known genetic erythrocyte abnormalities that have arisen due to the historical evolutionary pressure of malaria. This study identified in mice a novel malaria resistance-conferring host mutation in the heme biosynthesis enzyme, porphobilinogen deaminase (PBGD), and compared the relative requirements by Plasmodium for the host versus parasite-encoded forms of PBGD in both in vivo and in vitro settings. The findings demonstrated that parasite PBGD was dispensable, but that the host enzyme was important specifically during in vivo infection by P. chabaudi, and collectively suggest that Plasmodium requires a certain threshold of the enzyme to sustain its intraerythrocytic growth. Plasmodium may therefore be vulnerable to other interventions that limit host PBGD activity.

microbiology↗