Biology subjects

Hosseini, R.

Publications and source records attributed to Hosseini, R..

13 recordsLinked to original sources

bioRxiv2025

RobPicker: A Meta Learning Framework for Robust Identification of Macromolecules in Cryo-Electron Tomograms

Accurate particle picking of macromolecules of all kinds, shapes, and sizes in cryogenic electron tomograms (cryo-ET) is critical for understanding the molecular architecture of biological systems in their native state. Current deep learning methods have shown potential in identifying macromolecules from tomograms, but they are vulnerable to issues of noise in the training dataset obtained through human or automatic labeling and imbalanced distribution of macromolecule species. To address these limitations, we developed RobPicker, a meta-learning framework that effectively mitigates these issues by automatically learning deep neural networks to correct label errors and give greater emphasis to underrepresented macromolecule species. In evaluations across diverse cryo-ET datasets with noisy labels and imbalanced species distributions, RobPicker substantially outperforms state-of-the-art methods, particularly in identifying small and rare macromolecules. The efficiency and robustness of RobPicker can also be used for rapid fine-tuning of the tilt-series alignment, leading to improved tomogram reconstruction and enabling high-resolution cellular structural biology analysis.

bioinformatics↗

bioRxiv2025

A scalable approach to investigating sequence-to-expression prediction from personal genomes

Sequence-to-function (S2F) models hold the promise of evaluating arbitrary DNA sequences, providing a powerful framework for linking genotype to phenotype. Yet, despite strong performance across genomic loci, these models often struggle to capture inter-individual variation in gene expression. To address this, we propose personal genome training--training models to make genotype-specific predictions at a single locus. We introduce SAGE-net, a scalable framework and software package for training and evaluating S2F models using personal genomes. Using SAGE-net, we systematically explore model architectures and training regimes, showing that personal genome training improves gene expression prediction accuracy for held-out individuals. However, performance gains arise primarily from identifying predictive variants, rather than learning a cis-regulatory grammar that generalizes across loci. This lack of generalization persists across a wide range of hyperparameters. In contrast, when applied to DNA methylation (DNAm), personal genome training enables improved generalization to unseen individuals in unseen genomic regions. This suggests that S2F models may more readily capture the sequence-level determinants of inter-individual variation in epigenomic traits. These findings highlight the need for further exploration to unlock the full potential of S2F models in decoding the regulatory grammar of personal genomes. Scalable software and infrastructure development will be critical to this progress.

genomics↗

bioRxiv2024

Engineered Nanotopographies Induce Transient Openings in the Nuclear Membrane

Materials with engineered nano-scale surface topographies, such as nanopillars, nanoneedles, and nanowires, mimic natural structures like viral spike proteins, enabling them to bypass biological barriers like the plasma membrane. These properties have led to applications in nanoelectronics for intracellular sensing and drug delivery platforms, some of which are already in clinical trials. Here, we present evidence that nanotopographic materials can induce transient openings in the nuclear membranes of various cell types without penetrating the cells, breaching the nucleo-cytoplasmic barrier and allowing uncontrolled molecular exchange across the nuclear membrane. These openings, induced by nanoscale curvature, are temporary and repaired through ESCRT-mediated mechanisms. Our findings suggest a potential for nano topographic materials for direct nuclear sensing and delivery, holding promise for improving the delivery, efficiency, and safety of therapeutic agents to the nucleus.

bioengineering↗

bioRxiv2024

Ultra-fast variant effect prediction using biophysical transcription factor binding models

Sequence variation within TF binding sites can significantly affect TF-DNA interactions, influencing gene expression and contributing to disease susceptibility or phenotypic traits. Despite recent progress in deep sequence-to-function models that predict functional output from sequence data, these methods perform inadequately on some variant effect prediction tasks, especially with common genetic variants. This limitation underscores the importance of leveraging biophysical models of TF binding to enhance interpretability of variant effect scores and facilitate mechanistic insights. We introduce motifDiff, a novel computational tool designed to quantify variant effects using mono and dinucleotide position weight matrices. motifDiff offers several key advantages, including scalability to score millions of variants within minutes, implementation of statistically rigorous normalization strategy critical for optimal performance, and support for both dinucleotide and mononucleotide models. We demonstrate motifDiff s efficacy by evaluating it across diverse ground truth datasets that quantify the effects of common variants in vivo, thereby establishing robust benchmarks for the predictive value of variant effect calculations. Finally, we show that our tool provides unique insights when interpreting human accelerated regions. motifDiff is available as a standalone Python application at https://github.com/rezwanhosseini/MotifDiff.

bioinformatics↗

bioRxiv2021

From Alpha to Zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and sub-types of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy -- the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (< 1% of the sequences) Beta (South Africa), Epsilon (California), Gamma and Zeta (Brazil) variants in the GISAID dataset. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large datasets.

bioinformatics↗

bioRxiv2021

Raabin-WBC: a large free access dataset of white blood cells from normal peripheral blood

Accurate and early detection of peripheral white blood cell anomalies plays a crucial role in the evaluation of an individuals well-being. The emergence of new technologies such as artificial intelligence can be very effective in achieving this. In this regard, most of the state-of-the-art methods use deep neural networks. Data can significantly influence the performance and generalization power of machine learning approaches, especially deep neural networks. To that end, we collected a large free available dataset of white blood cells from normal peripheral blood samples called Raabin-WBC. Our dataset contains about 40000 white blood cells and artifacts (color spots). To reassure correct data, a significant number of cells were labeled by two experts, and the ground truth of nucleus and cytoplasm were extracted by experts for some cells (about 1145), as well. To provide the necessary diversity, various smears have been imaged. Hence, two different cameras and two different microscopes were used. The Raabin-WBC dataset can be used for different machine learning tasks such as classification, detection, segmentation, and localization. We also did some primary deep learning experiments on Raabin-WBC, and we showed how the generalization power of machine learning methods, especially deep neural networks, was affected by the mentioned diversity.

bioengineering↗

bioRxiv2021

New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

bioengineering↗

bioRxiv2020

DRAM1 requires PI(3,5)P2 generation by PIKfyve to deliver vesicles and their cargo to endolysosomes

Endolysosomal vesicle trafficking and autophagy are crucial degradative pathways in maintenance of cellular homeostasis. The transmembrane protein DRAM1 is a potential therapeutic target that primarily localises to endolysosomal vesicles and promotes autophagy and vesicle fusion with lysosomes. However, the molecular mechanisms underlying DRAM1-mediated vesicle fusion events remain unclear. Using high-resolution confocal microscopy in the zebrafish model, we show that mCherry-Dram1 labelled vesicles interact and fuse with early endosomes marked by PI(3)P. Following these fusion events, early endosomes mature into late endosomes in a process dependent on the conversion of PI(3)P into PI(3,5)P2 by the lipid kinase PIKfyve. Chemical inhibition of PIKfyve reduces the targeting of Dram1 to acidic endolysosomal vesicles, arresting Dram1 in multivesicular bodies, early endosomes, or non-acidified vesicles halted in their fusion with early endosomes. In conclusion, Dram1-mediated vesicle fusion requires the formation of PI(3,5)P2 to deliver vesicles and their cargo to the degradative environment of the lysosome.

molecular biology↗

bioRxiv2020

Adaptive laboratory evolution restores solvent tolerance in plasmid-cured Pseudomonas putida S12; a molecular analysis

Pseudomonas putida S12 is intrinsically solvent-tolerant and constitutes a promising platform for biobased production of aromatic compounds and biopolymers. The genome of P. putida S12 consists of a 5.8 Mbp chromosome, and a 580 kbp megaplasmid pTTS12 that carries several gene clusters involved in solvent tolerance. Removal of pTTS12 caused a significant reduction in solvent tolerance. In this study, we succeeded in restoring solvent tolerance in plasmid-cured P. putida S12 using adaptive laboratory evolution (ALE), underscoring the innate solvent-tolerance of this strain. Whole genome sequencing revealed several single nucleotide polymorphisms (SNPs) and a mobile element insertion, enabling ALE-derived strains to survive and sustain growth in the presence of a high toluene concentration (10% v/v). Mutations were identified in an RND efflux pump regulator arpR, resulting in constitutive upregulation of the multifunctional efflux pump ArpABC. SNPs were also found in the intergenic region and subunits of ATP synthase, RNA polymerase subunit {beta}, global two-component regulatory system (GacA/GacS) and a putative AraC-family transcriptional regulator Afr. RNA-seq analysis further revealed a constitutive down-regulation of energy consuming activities in ALE-derived strains, including flagellar assembly, F0F1 ATP synthase, and membrane transport proteins. Out results indicate that constitutive expression of an alternative solvent extrusion pump in combination with high metabolic flexibility ensures restoration of solvent-tolerance in P. putida S12 lacking its megaplasmid.

microbiology↗

bioRxiv2020

Comparative analysis reveals the modular functional build-up of megaplasmid pTTS12 of Pseudomonas putida S12: a paradigm for transferable traits, plasmid stability and inheritance?

The Pseudomonas putida S12 genome contains 583 kbp megaplasmid pTTS12 that carries over 600 genes enabling tolerance to various stress conditions, including the solvent extrusion pump SrpABC. We performed a comparative analysis of pTTS12 against 28915 plasmids from NCBI databases. We investigated putative roles of genes encoded on pTTS12 and further elaborated on its role in the establishment and maintenance of several stress conditions, specifically focusing on solvent tolerance in P. putida strains. The backbone of pTTS12 was found to be closely related to that of the carbapenem-resistance plasmid pOZ176, member of the IncP-2 incompatibility group, although remarkably the carbapenem resistance cassette is absent from pTTS12. Megaplasmid pTTS12 contains multiple transposon-flanked cassettes mediating resistance to various heavy metals such as tellurite, chromate (Tn7), and mercury (Tn5053 and Tn5563). Additionally, pTTS12 also contains a P-type, Type IV secretion system (T4SS) supporting self-transfer to other P. putida strains. This study increases our understanding in the build-up of IncP-2 plasmids and several promising exchangeable gene clusters to construct robust microbial hosts for biotechnology applications. ImportanceOriginating from various environmental niches, large numbers of bacterial plasmids have been found carrying heavy metal and antibiotic resistance genes, degradation pathways and specific transporters for organic solvents or aromatic compounds. Such genes may constitute promising candidates for novel synthetic biology applications. Our systematic analysis of gene clusters encoded on megaplasmid pTTS12 underscores that a large portion of its genes is involved in stress response increasing survival under harsh conditions like heavy metal and organic solvent resistance. We show that pTTS12 belongs to the IncP-2 plasmid family. Comparative analysis of pTTS12 provides thorough insight into the structural and functional build-up of members of the IncP-2 plasmid family. pTTS12 is highly stable and carries a complex arrangement of transposable elements containing heavy metal resistance clusters as well as distinct aromatic degradation pathways and solvent-extrusion pump. This offers interesting insight into the evolution of solvent tolerance in the P. putida family.

microbiology↗

bioRxiv2020

A novel toxin-antitoxin module SlvT-SlvA governs megaplasmid stability and incites solvent tolerance in Pseudomonas putida S12

Pseudomonas putida S12 is highly tolerant towards organic solvents in saturating concentrations, rendering this microorganism suitable for the industrial production of various aromatic compounds. Previous studies reveal that P. putida S12 contains a single-copy 583 kbp megaplasmid pTTS12. This pTTS12 encodes several important operons and gene clusters facilitating P. putida S12 to survive and grow in the presence of toxic compounds or other environmental stresses. We wished to revisit and further scrutinize the role of pTTS12 in conferring solvent tolerance. To this end, we cured the megaplasmid from P. putida S12 and conclusively confirmed that the SrpABC efflux pump is the major contributor of solvent tolerance on the megaplasmid pTTS12. Importantly, we identified a novel toxin-antitoxin module (proposed gene names slvT and slvA respectively) encoded on pTTS12 which contributes to the solvent tolerant phenotype and is essential in conferring genetic stability to the megaplasmid. Chromosomal introduction of the srp operon in combination with slvAT gene pair created a solvent tolerance phenotype in non-solvent tolerant strains such as P. putida KT2440, E. coli TG1, and E. coli BL21(DE3). ImportanceSustainable alternatives for high-value chemicals can be achieved by using renewable feedstocks in bacterial biocatalysis. However, during bioproduction of such chemicals and biopolymers, aromatic compounds that function as products, substrates or intermediates in the production process may exert toxicity to microbial host cells and limit the production yield. Therefore, solvent-tolerance is a highly preferable trait for microbial hosts in the biobased production of aromatic chemicals and biopolymers. In this study, we revisit the essential role of megaplasmid pTTS12 from solvent-tolerant P. putida S12 for molecular adaptation to organic solvent. In addition to the RND efflux pump (SrpABC), we identified a novel toxin-antitoxin module (SlvAT) which contributes to tolerance in low solvent concentration as well as to genetic stability of pTTS12. These two gene clusters were successfully transferred to non-solvent tolerant strains of P. putida and to E. coli strains to confer and enhance solvent tolerance.

microbiology↗

bioRxiv2018

γ-proteobacteria eject their polar flagella under nutrient depletion, retaining flagellar motor relic structures

Bacteria switch only intermittently to motile planktonic lifestyles under favourable conditions. Under chronic nutrient deprivation, however, bacteria orchestrate a switch to stationary phase, conserving energy by altering metabolism and stopping motility. About two-thirds of bacteria use flagella to swim, but how bacteria deactivate this large-molecular machine remains poorly studied. Here we describe the previously unreported ejection of polar sodium-driven motors by {gamma}-proteobacteria. We show that these bacteria eject their flagella at the base of the flagellar hook when nutrients are depleted, leaving a relic of a former flagellar motor in the outer membrane. Subtomogram averages of the full motor and relic reveal that this is an active process, as a plug protein appears in the relic, likely to prevent leakage across their outer membrane. We show that this is a widespread phenomenon demonstrated by the appearance of relic structures in varied {gamma}-proteobacteria including Plesiomonas shigelloides, Vibrio cholerae, Vibrio fischeri, Shewanella putrefaciens and Pseudomonas aeruginosa.

microbiology↗

bioRxiv2018

Molecular genetic analysis of Steroid Resistant Nephrotic Syndrome: Detection of a novel mutation

Background: Nephrotic syndrome is one of the most common kidney diseases in childhood. About 20% of children are steroid-resistant NS (SRNS) which progress to end-stage renal disease (ESRD). More than 53 genes are associated with SRNS which represent the genetic heterogeneity of SRNS. This study was aimed to screen disease causing mutations within NPHS1 and NPHS2 and evaluate new potential variants in other genes.\n\nMethod: In first phase of study, 25 patients with SRNS were analyzed for NPHS1 (exon 2, 26) and all exons of NPHS2 genes by Sanger sequencing. In the second phase, whole exome sequencing was performed on 10 patients with no mutations in NPHS1 and NPHS2.\n\nResult: WES analysis revealed a novel mutation in FAT1 (c.10570C>A; Q3524K). We identified 4 pathogenic mutations, located in exon 4 and 5 of NPHS2 gene in 20% of patients (V180M, P118L, R168C and Leu156Phe). Also our study has contributed to the descriptions of previously known pathogenic mutations across WT1 (R205C) and SMARCAL1 (R764Q) and a novel polymorphism in CRB2.\n\nConclusion: Our study concludes that mutations of exon 4 and 5 NPHS2 gene are common in Iranian and some other ethnic groups. We suggest conducting WES after NPHS2 screening and further comprehensive studies to identify the most common genes in the development of SRNS, which might help in Clinical impact on management in patients with SRNS.\n\nDetection of a novel mutation in SRNS

genetics↗