Biology subjects

Abbasi, A.

Publications and source records attributed to Abbasi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Mutagenic bypass of uracil-derived abasic sites underlies the SBS17 mutational signature

Understanding how mutations arise is central to predicting and preventing cancer, yet most of the mutational signatures found in cancer genomes still have no known mechanistic cause. Among these, single-base-substitution signature 17 (SBS17) is the dominant mutational process in gastric and oesophageal adenocarcinoma. SBS17 has been linked to oxidative damage and to the chemotherapeutic agent 5-fluorouracil (5-FU), but the molecular events that generate it are unknown. Here we demonstrate that SBS17 causes driver mutations in gastrointestinal cancer. We then combine genetics in cell lines and organoids with whole-genome sequencing to define the mechanistic aetiology of SBS17 mutagenesis. We disprove the prevailing oxidative damage hypothesis and instead show that SBS17 arises through the misincorporation of dUTP during DNA replication, followed by uracil excision by the glycosylase UNG and mutagenic bypass of the resulting abasic (AP) sites by the translesion synthesis machinery. Importantly, we reveal that the same mechanism underlies both spontaneous and chemotherapy-induced mutations. Together, our results resolve the origin of gastric mutagenesis, opening therapeutic avenues for curbing ongoing mutagenesis, tumour evolution and drug resistance in gastrointestinal cancers.

molecular biology↗

bioRxiv2025

Engineering HIV antibodies with enhanced breadth and potency of neutralization through multistate affinity maturation

Broadly neutralizing antibodies (bnAbs) against HIV hold promise as therapeutic and prophylactic agents, but realizing this potential requires antibodies that function across the antigenic heterogeneity of the HIV envelope glycoprotein (Env). Although numerous bnAbs have been isolated from infected individuals, their breadth and potency may not be sufficient to tackle global viral diversity, motivating efforts to further improve their neutralization capacity. Here, we address this challenge using a multistate antibody engineering approach integrating deep mutational scanning with combinatorial library screening across diverse Env variants. This strategy enables identification of mutation patterns that confer improved binding across antigenically distinct targets. Starting from one of the best-in-class CD4-binding site bnAbs, we performed iterative optimization to increase binding affinity across diverse Env variants. The resulting lead candidate exhibited improved breadth and up to 100-fold higher potency against pseudoviruses from large cross-clade historical and contemporary panels while maintaining biophysical and pharmacokinetic profiles conducive to clinical development. Structural and molecular dynamics analyses revealed a unique tri-tyrosine aromatic triad and reinforced electrostatic contacts that stabilized the bnAb/Env interface. These findings demonstrate that systematic in vitro engineering can generate bnAbs with enhanced breadth and potency, providing a generalizable strategy for developing therapeutic antibodies against highly diverse pathogens.

bioengineering↗

bioRxiv2025

Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer

Mutational signature analysis has greatly enhanced our understanding of the mutagenic processes found in cancer and normal tissues. As part of a recent study, we analyzed 802 treatment-naive, microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was conservatively decomposed into SBS18, a signature associated with reactive oxygen species. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from that of SBS18. Through an independent analysis of 2,616 whole-genome sequenced microsatellite-stable CRCs across three distinct cohorts, we demonstrate that SBS_D is consistently present at a similar prevalence, suggesting that this signature may have been previously overlooked. Using a naive decomposition approach, we demonstrate that the pattern of SBS_D better aligns with signatures previously associated with deficiencies in DNA polymerase delta (POLD1) proofreading and mismatch repair. However, multiple lines of evidence, including the absence of pathogenic mutations in the exonuclease domain of POLD1 or in mismatch repair-associated genes, indicate that SBS_D is not driven by canonical defects in these DNA repair pathways. Overall, this study identifies a previously unrecognized mutational signature in microsatellite-stable CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development in samples without canonical defects in DNA repair pathways.

cancer biology↗

bioRxiv2025

Competition, Mutualism, and Host Immune Control in a Cancer Microbiome

The microbiome functions as an ecological community, where diverse microbes engage in metabolically mediated interactions such as mutualism and competition. Host immune response can regulate microbial community richness and abundance, which in turn can shape the prevalence of different ecological interactions within the microbiome. Theory predicts that host immune states shift dominant interaction modes among microbes: inflammation favors competition, while immunosuppression favors mutualism. We test these theoretical predictions using the stomach cancer microbiome data through integrated genomic and metabolic analyses. We observe that tumors with high-richness and high-abundance microbiomes were associated with increased mutualistic interactions, whereas tumors with low-richness and low-abundance microbiomes had fewer mutualistic interactions. Host immune gene expression in the high-richness and abundance group was suggestive of a dysregulated or immunosuppressed tumor microenvironment, whereas in the low-richness and abundance group immune signatures were indicative of intact immune function. Notably, competitive interactions remained relatively consistent between groups, whereas mutualism varied markedly, highlighting its sensitivity to shifts in immune state. Finally, the microbiome and host immune states were linked to patient clinical outcomes, with high-richness and abundance microbiomes associated with poorer survival and elevated expression of immune markers linked to adverse prognosis. These results reveal how host immune control can covary with ecological interactions within the microbiome, and the potential consequences of these interactions for host health.

ecology↗

bioRxiv2024

A Comprehensive Engineering Strategy Improves Potency and Manufacturability of a Near Pan-neutralizing Antibody Against HIV

Anti-HIV envelope broadly neutralizing antibodies (bnAbs) are alternatives to conventional antiretrovirals with the potential to prevent and treat infection, reduce latent reservoirs, and/or mediate a functional cure. Clinical trials with "first generation" bnAbs used alone or in combination show promising antiviral effects but also highlight that additional engineering of "enhanced" antibodies will be required for optimal clinical utility, while preserving or enhancing cGMP manufacturing capability. Here we report the engineering of an anti-CD4 binding-site (CD4bs) bnAb, N49P9.3, purified from the plasma of an HIV elite-neutralizer. Through a series of rational modifications we produced a variant that demonstrates: enhanced potency; superior antiviral activity in combination with other bnAbs; low polyreactivity; and longer circulating half-life. Additional engineering for manufacturing produced a final variant, eN49P9, with properties conducive to cGMP production. Overall, these efforts demonstrate the feasibility of developing enhanced anti-CD4bs bnAbs with greatly improved antiviral properties as well as potential translational value.

bioengineering↗

bioRxiv2023

A scalable, low-cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations

Tuber size, shape, colorimetric characteristics, and defect susceptibility are all factors that influence the acceptance of new potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is substantially limited by our inability to precisely measure these features quantitatively on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we developed a low-cost, semi-automated workflow to capture data and measure each of these characteristics using machine vision. This workflow was applied to assess the phenotypic variation present within 189 F1 progeny of the A08241 breeding population. Our results provide an example of quantitative measurements acquired using machine vision methods that are reliable, heritable, and can be used to understand and select upon multiple traits simultaneously in structured potato breeding populations.

plant biology↗

bioRxiv2023

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

BackgroundAll cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events ResultsHere, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. ConclusionsThe new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

bioinformatics↗

bioRxiv2022

Improving Protein Subcellular Localization Prediction with Structural Prediction & Graph Neural Networks

The majority of biological functions are carried out by proteins. Proteins perform their roles only upon arrival to their target location in the cell, hence elucidating protein subcellular localization is essential for better understanding their function. The exponential growth in genomic information and the high cost of experimental validation of protein localization call for the development of predictive methods. We present a method that improves subcellular localization prediction for proteins based on their sequence by leveraging structure prediction and Graph Neural Networks. We demonstrate how Language Models, trained on protein sequences, and Graph Neural Networks, trained on proteins 3D structures, are both efficient approaches for this task. They both learn meaningful, yet different representations of proteins; hence, ensembling them outperforms the reigning state of the art method. Our architecture improves the localization prediction performance while being lighter and more cost-effective.

bioinformatics↗

bioRxiv2022

Dissecting the contributions of tumor heterogeneity on metastasis at single-cell resolution

Metastasis is the leading cause of cancer-related deaths, but metastasis research is challenged by limited access to patient material and a lack of experimental models that appropriately recapitulate tumor heterogeneity. Here, we analyzed single-cell transcriptomes of matched primary tumor and metastasis from patient-derived xenograft models of breast cancer, demonstrating that primary tumor and metastatic cells show profound transcriptional differences across heterogeneous tumors. While primary tumor cells upregulated several metabolic genes, metastatic cells displayed a motility phenotype in micrometastatic lesions and increased stress response signaling during metastatic progression. Additionally, we identified gene signatures that are associated with the metastatic potential and correlated with patient outcomes. Poorly metastatic primary tumors showed increased immune-regulatory control that may prevent metastasis, whereas highly metastatic primary tumors upregulated markers of epithelial-mesenchymal transition (EMT). We found that intra-tumor heterogeneity is dominated by epithelial-mesenchymal plasticity (EMP) which presented as a dynamic continuum with intermediate cell states that were characterized by novel, specific markers. These intermediate EMP markers correlated with worse patient outcomes and could serve as potential new therapeutic targets to block metastatic development.

cancer biology↗

bioRxiv2022

Cortico-cerebellar coordination facilitates neuroprosthetic learning

Temporal coordination among neurons and development of functional neuronal assemblies is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence about how functional coordination emerges in task-related neuronal assemblies in cortical and subcortical regions that are related to the control of functional output. We investigated emergent neural dynamics between primary motor cortex (M1) and the contralateral cerebellar cortex as rats learned a neuroprosthetic/ brain machine interface (BMI) task. BMIs offer a powerful tool to causally test how distributed neural networks achieve specific neural activation. During neuroprosthetic learning, actuator movements are causally linked to primary motor cortex (M1) neurons, i.e., "direct" neurons, that drive the decoder and whose firing is required to successfully perform the task. However, it is unknown how task-related M1 activity interacts with cerebellar activity. We observed a striking 3-6 Hz coherence that emerged between these regions local-field potentials (LFPs) with neuroprosthetic learning which also modulated task-related spiking. We found a robust task-related indirect modulation in the cerebellum, and we found that this activity developed a preferential relationship with M1 task-related direct and indirect activity but not with M1 task unrelated activity with learning. We also performed optogenetic inhibition of cerebellar activity (in the cerebellar cortex and its deep nuclei) and found that this led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that coordinated neural dynamics emerge in cortico-cerebellar regions during neuroprosthetic learning which supports task-relevant activity in M1 neuronal populations, and further, that cerebellar influence is necessary for M1-driven neuroprosthetic control.

neuroscience↗

bioRxiv2022

Emergent low-frequency activity in cortico-cerebellar networks with motor skill learning

The motor cortex controls skilled arm movement by recruiting a variety of targets in the nervous system, and it is important to understand the emergent activity in these regions as refinement of a motor skill occurs. One fundamental projection of the motor cortex is to the cerebellum. However, the emergent activity in the motor cortex and the cerebellum that appears as a dexterous motor skill is consolidated is incompletely understood. Here, we report on low-frequency oscillatory (LFO) activity that emerges in cortico-cerebellar networks with learning the reach-to-grasp motor skill. We chronically recorded the motor and the cerebellar cortices in rats which revealed the emergence of coordinated movement-related activity in the local-field potentials (LFPs) as the reaching skill consolidated. We found that the local and cross-area spiking activity was coordinated with LFOs. Finally, we also found that these neural dynamics were more prominently expressed during accurate behavior. This work furthers our understanding on emergent dynamics in the cortico-cerebellar loop that underlie learning and execution of precise skilled movement.

neuroscience↗

bioRxiv2022

Identification of novel leaf rust seedling resistance loci in Iranian bread wheat germplasm using genome-wide association mapping

Leaf or brown rust caused by Puccinia triticina Eriks. (Pt) is a major biotic constraint threatening bread wheat production worldwide. The continued evolution of new races of Pt necessitates a constant search for the identification of new resistance genes, or QTLs, to enhance the resistance durability of bread varieties. On a panel of 320 bread wheat accessions, we used a genome-wide association study (GWAS) technique to map loci associated with Pt resistance using single-nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). The panel was tested with five Pt races gathered from different regions of IRAN to identify loci associated with seedling resistance. After estimating genetic relatedness and population structure among accessions, GWAS discovered a total of 19 SNPs on chromosomes 1B, 2B, 3A, 3B, 4A, 5B, 5D, 6A, 6B, 6D, 7B, and 7D that were significantly associated with seedling stage resistance. The three SNP markers rs12954, rs34220, and rs42447 on chromosomes 5D, 6A, and 7D, respectively, associated with resistance to Pt race PKTTS expressing potential new loci for leaf rust resistance. Overall, this research gives an integrated perspective of leaf rust resistance resources in Iranian bread wheat and recognizes new resistance loci that will be valuable to expand the set of resistance genes available to control this serious disease.

plant biology↗

bioRxiv2021

A deep learning algorithm for potato tuber hollowheart classification

A novel deep learning algorithm is proposed for hollow heart detection which is an internal tuber defect. Hollow heart is one of many internal defects that decrease the market value of potatoes in the fresh market and food processing sectors. Susceptibility to internal defects like the hollow heart is influenced by genetic and environmental factors so elimination of defect-prone material in potato breeding programs is important. Current methods of evaluation utilize human scoring which is limiting (only collects binary data) relative to the data collection capacity afforded by computer vision or are based upon X-ray transmission techniques that are both expensive and can be hazardous. Automation of defect classification (e.g. hollow heart) from data sets collected using inexpensive, consumer-grade hardware has the potential to increase throughput and reduce bias in public breeding programs. The proposed algorithm consists of ResNet50 as the backbone of the model followed by a shallow fully connected network (FCN). A simple augmentation technique is performed to increase the number of images in the data set. The performance of the proposed algorithm is validated by investigating metrics such as precision and the area under the curve (AUC).

plant biology↗

bioRxiv2021

Signatures of copy number alterations in human cancer

The gains and losses of DNA that emerge as a consequence of mitotic errors and chromosomal instability are prevalent in cancer. These copy number alterations contribute to cancer initiaition, progression and therapeutic resistance. Here, we present a conceptual framework for examining the patterns of copy number alterations in human cancer using whole-genome sequencing, whole-exome sequencing, and SNP6 microarray data making it widely applicable to diverse datasets. Deploying this framework to 9,873 cancers representing 33 human cancer types from the TCGA project revealed a set of 19 copy number signatures that explain the copy number patterns of 93% of TCGA samples. 15 copy number signatures were attributed to biological processes of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, and chromothripsis. The aetiology of four copy number signatures are unexplained and some cancer types have unique patterns of amplicon signatures associated with extrachromosomal DNA, disease-specific survival, and gains of proto-oncogenes such as MDM2. In contrast to base-scale mutational signatures, no copy number signature associated with known cancer risk factors. The results provide a foundation for exploring patterns of copy number changes in cancer genomes and synthesise the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes giving rise to copy number changes.

cancer biology↗

bioRxiv2021

APOBEC3A drives acquired resistance to targeted therapies in non-small cell lung cancer

Acquired drug resistance to even the most effective anti-cancer targeted therapies remains an unsolved clinical problem. Although many drivers of acquired drug resistance have been identified1-6, the underlying molecular mechanisms shaping tumor evolution during treatment are incompletely understood. The extent to which therapy actively drives tumor evolution by promoting mutagenic processes7 or simply provides the selective pressure necessary for the outgrowth of drug-resistant clones8 remains an open question. Here, we report that lung cancer targeted therapies commonly used in the clinic induce the expression of cytidine deaminase APOBEC3A (A3A), leading to sustained mutagenesis in drug-tolerant cancer cells persisting during therapy. Induction of A3A facilitated the formation of double-strand DNA breaks (DSBs) in cycling drug-treated cells, and fully resistant clones that evolved from drug-tolerant intermediates exhibited an elevated burden of chromosomal aberrations such as copy number alterations and structural variations. Preventing therapy-induced A3A mutagenesis either by gene deletion or RNAi-mediated suppression delayed the emergence of drug resistance. Finally, we observed accumulation of A3A mutations in lung cancer patients who developed drug resistance after treatment with sequential targeted therapies. These data suggest that induction of A3A mutagenesis in response to targeted therapy treatment may facilitate the development of acquired resistance in non-small-cell lung cancer. Thus, suppressing expression or enzymatic activity of A3A may represent a potential therapeutic strategy to prevent or delay acquired resistance to lung cancer targeted therapy.

cancer biology↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗

bioRxiv2019

Sensorimotor neuronal learning requires cortical topography

Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics. TeaserTargeting a brain-machine interface feedback to a cortical map reveals unexpected constraints.

neuroscience↗

bioRxiv2018

Antiviral Functions of ARGONAUTE Proteins During Turnip Crinkle Virus Infection Revealed by Image-based Trait Analysis in Arabidopsis

RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted of by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green and blue (RGB) imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent non-additive, anti-TCV effectors, while AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.\n\nAuthor SummaryPlant viruses caused substantial losses in crop production and quality worldwide. Precisely measuring plant health is critical for better understanding the mechanisms underlying plant virus and host interactions. Advances in high-resolution imaging technologies and deep-learning tools have made acquiring and analyzing \"big data\" of disease traits possible. In this study, we have developed a high-throughput, image-based trait phenotyping pipeline to quantify disease severity in Arabidopsis thaliana infected by Turnip Crinkle Virus (TCV). Our aim is to understand how the antiviral RNA silencing machinery is tuned to protect the host from invading virus infection. We focused on ARGONAUTE proteins, which are the effectors in the RNA silencing pathway. A mutant line of TCV with a dysfunctional silencing suppressor (P38) was used to investigate which ago mutation could compensate for the dysfunctional silencing suppressor and facilitate the development of disease symptoms. We demonstrated that specific AGO proteins contribute to protecting leaves from TCV infection in a non-additive manner. Our results also implied that distinct AGOs are required to function collectively to silence TCV in inflorescence tissues. More evidence is still needed to further understand how these antiviral AGOs interact with suppressor proteins molecularly during TCV infection.

plant biology↗