Biology subjects

Tavakoli, S.

Publications and source records attributed to Tavakoli, S..

12 recordsLinked to original sources

bioRxiv2026

Chronic pain exacerbates nicotine withdrawal severity in a sex-specific and dose-dependent manner

Chronic pain and nicotine use frequently co-occur, and individuals with chronic pain often experience greater difficulty quitting. Therefore, we examined nicotine withdrawal behaviors and analgesic-like effects in pain-naive and chronic pain conditions. Adult male and female rats underwent chronic constriction injury or sham surgery. After pain establishment, rats received twice-daily subcutaneous nicotine (0.3 or 0.7 mg/kg) or saline for 14 days. 24 h after the final injection, withdrawal was assessed, including physical signs and anxiety-like behavior. Depressive-like responses were evaluated at 72 h. Pain sensitivity and nicotines analgesic-like effects were assessed throughout. Chronic pain increased physical signs of withdrawal in both sexes, with greater effects in females. It also induced anxiety-like behavior in controls of both sexes. In rats with comorbid chronic pain and withdrawal, anxiety-like behavior was further enhanced in males, whereas females showed variable responses across assays, with increases or decreases depending on the test. Chronic pain induced depressive-like behavior in males but not in females. During withdrawal, depressive-like responses in males with chronic pain were not greater than those in the chronic pain alone group, while chronic nicotine exposure reduced depressive-like behavior in females. Nicotine produced acute analgesic-like effects that diminished over time in both pain-naive and chronic pain conditions, indicating tolerance. In pain-naive rats, repeated nicotine exposure induced mechanical hypersensitivity. Chronic pain intensified nicotine withdrawal severity in a nicotine concentration- and sex-dependent manner. These findings highlight the importance of considering pain status and sex when developing effective cessation strategies, particularly for individuals with comorbid chronic pain. SummaryChronic pain exacerbates nicotine withdrawal severity. Chronic nicotine exposure induces pain hypersensitivity and tolerance to analgesic effects. These effects vary by nicotine concentration and sex.

pharmacology and toxicology↗

bioRxiv2025

ARCLID: Accurate and Robust Characterization of Long Insertions and Deletions in Genome

Structural variants (SVs) shape genome diversity and underlie human diseases, yet accurate detection remains difficult, especially at low sequencing coverages. Most existing callers are optimized for deep sequencing and lose sensitivity as coverage falls, limiting their use in studies that sequence genomes at shallow depth. We present ARCLID, a deep learning-based SV caller that reframes SV detection as an object detection task. We benchmarked ARCLID against six well-established callers on four samples and challenging medically relevant genes, across coverages from 28x down to 5x, multiple SV sizes, and both relaxed and strict breakpoint matching. At 5x, ARCLID achieved the highest F1 score of all evaluated callers on every sample, exceeding the second-best tool by up to 8%. This advantage widened under strict breakpoint matching, indicating genuine variant recovery with accurate breakpoint placement. At higher coverages, ARCLID remains competitive with the strongest current tools. By preserving accuracy at low depth, it makes reliable SV discovery feasible from shallow data, enabling lower sequencing cost, time, and storage for population-scale and clinical genomics. We additionally curated SV benchmarking regions for HG00733 and NA19240 that complement the widely used HG002 GIAB benchmark regions, supporting evaluation of SV callers across ancestrally distinct genomes.

bioinformatics↗

bioRxiv2024

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess neuronal epitranscriptome plasticity

Chemical modifications in mRNAs, such as pseudouridine (psi), can control gene expression. Yet, we know little about how they are regulated, especially in neurons. We applied nanopore direct RNA sequencing to investigate psi dynamics in SH-SY5Y cells in response to two perturbations that model a natural and unnatural cellular state: retinoic-acid-mediated differentiation (healthy) and exposure to the neurotoxicant, lead (unhealthy). We discovered that the expression of some psi writers change significantly in response to physiological conditions. We also found that globally, lead-treated cells have more psi sites but lower relative occupancy than untreated cells and differentiated cells. Interestingly, examples of highly plastic sites were accompanied by constant expression for psi writers, suggesting trans-regulation. Many positions were static throughout all three cellular states, suggestive of a "housekeeping" function. This study enables investigations into mechanisms that control psi modifications in neurons and its possible protective effects in response to cellular stress.

systems biology↗

bioRxiv2023

Reducing work-load of pathotype and gene detection in Klebsiella pneumoniae by leveraging machine learning

BackgroundThe growing prevalence of carbapenem resistance has caused an increasing number of bacterial isolates with multi-drug resistance features, especially in the Enterobacteriaceae family. Klebsiella pneumoniae, as one of the important members of the Enterobacteriaceae family, causes serious infections, which has attracted the attention of scientists due to the emergence of hypervirulent pathotypes with increasing antibiotic resistance and has been raised as a major concern worldwide. Early detection of this new super bacterium and its antibiotic resistance is of great help in reducing mortality and costs. The lack of new antibiotic options underscores the need to optimize current diagnostics. Therefore, this study was designed to leverage machine-learning approach for optimized selection of crucial antibiotics to reduce the experiments needed for the detection of pathotypes and genes presence in two classical and hypervirulent K. pneumoniae pathotypes. Methods341 non-duplicate clinical isolates of K. pneumoniae were collected from five university hospitals in Tehran and Qazvin, Iran. Pathotype differentiation of classical (cKp) and hypervirulent K.pneumoniae (hvKp) was done by PCR method by two molecular biomarkers including iuc and iut. After identifying the phenotypic antibiotic resistance, the presence of antibiotic resistance genes was detected by PCR method. Then, the relevance of resistance/susceptibility of the antibiotics and presence of pathotypes, aerobactin, and beta-lactamase genes was investigated and analyzed using five supervised machine learning algorithms by selecting crucial antibiotics through feature selection methods. ResultsAmong the 341 K.pneumoniae isolates, 102 and 239 isolates were hvKp and cKp respectively. The highest rate of antibiotic resistance after ampicillin (100%) was related to cefotaxime (76.2%) and the lowest rate of resistance was found in meropenem (24.3%). Imipenem, Meropenem, Aztreonam, Ceftazidime, Ceftriaxone, and Gentamicin are crucial antibiotics for detection of the pathotypes and the aerobactin genes. Moreover, Cefotaxime, Ciprofloxacin, Cefepime, Meropenem, and Imipenem are essential for detection of the beta-lactamase genes. ConclusionImplementing a machine learning approach including various feature selection methods and algorithms, results in less-required experiments on more limited antibiotics to detect genes and pathotypes. Our findings reveal that using machine learning in the prediction of the presence of genes and pathotypes of clinical isolates was a suitable method in terms of rapidity and cost-efficiency on top of accuracy.

microbiology↗

bioRxiv2023

Multicellular, IVT-derived, unmodified human transcriptome for nanopore direct RNA analysis

Nanopore direct RNA sequencing (DRS) enables measurements of RNA modifications. Modification-free transcripts are a practical and targeted control for DRS, providing a baseline measurement for canonical nucleotides within a matched and biologically derived sequence context. However, these controls can be challenging to generate and carry nanopore-specific nuances that can impact analysis. We produced DRS datasets using modification-free transcripts from in vitro transcription (IVT) of cDNA from six immortalized human cell lines. We characterized variation across cell lines and demonstrated how these may be interpreted. These data will serve as a versatile control and resource to the community for RNA modification analysis of human transcripts.

genomics↗

bioRxiv2023

Transplantation-based screen uncovers inducers of muscle progenitor cell engraftment across vertebrate species

Stem cell transplantation presents a potentially curative strategy for genetic disorders of skeletal muscle, but this approach is limited due to the deleterious effects of cell expansion in vitro and consequent poor engraftment efficiency. In an effort to overcome this limitation, we sought to identify molecular signals that enhance the myogenic activity of cultured muscle progenitors. Here, we report the development and application of a cross-species small molecule screening platform employing zebrafish and mouse, which enables rapid, direct evaluation of the effects of chemical compounds on the engraftment of transplanted muscle precursor cells. Using this system, we screened a library of bioactive lipids to identify those that could increase myogenic engraftment in vivo in zebrafish and mice. Two lipids, lysophosphatidic acid (LPA) and niflumic acid (NFA), are linked to activation of intracellular calcium ion flux, which showed conserved, dose-dependent and synergistic effects in promoting muscle engraftment across these vertebrate species.

cell biology↗

bioRxiv2022

Synthesis of Long RNA with a Site-Specific Modification by Enzymatic Splint Ligation

Synthesis of RNA molecules that contain an internal site-specific modification is important for RNA research and therapeutics. While solid-state synthesis is attainable for such RNA in the range of 100 nucleotides (nts), it is currently impossible with kilobase (kb)-long RNA. Instead, long RNA with an internal modification is usually assembled in an enzymatic 3-part splint ligation to join a short RNA oligonucleotide, containing the site-specific modification, with both a left-arm and a right-arm long RNA that are synthesized by in vitro transcription. However, long RNAs have structural heterogeneity and those synthesized by in vitro transcription have 3-end sequence heterogeneity, which together substantially reduce the yield of 3-part splint ligation. Here we describe a method of 3-part splint ligation with an enhanced efficiency utilizing a ribozyme cleavage reaction to address the 3-end sequence heterogeneity and involving DNA disruptors proximal to the ligation sites to address the structural heterogeneity. The yields of the synthesized kb-long RNA are sufficiently high to afford purification to homogeneity for practical RNA research. We also verify the sequence accuracy at each ligation junction by nanopore sequencing.

biochemistry↗

bioRxiv2022

Messenger-RNA Modification Standards and Machine Learning Models Facilitate Absolute Site-Specific Pseudouridine Quantification

Enzyme-mediated chemical modifications to mRNA are important for fine-tuning gene expression, but they are challenging to quantify due to low copy number and limited tools for accurate detection. Existing studies have typically focused on the identification and impact of adenine modifications on mRNA (m6A and inosine) due to the availability of analytical methods. The pseudouridine ({Psi}) mRNA modification is also highly abundant but difficult to detect and quantify because there is no available antibody, it is mass silent, and maintains canonical basepairing with adenine. Nanopores may be used to directly identify {Psi} sites in RNAs using a systematically miscalled base, however, this approach is not quantitative and highly sequence dependent. In this work, we apply supervised machine learning models that are trained on sequence-specific, synthetic controls to endogenous transcriptome data and achieve the first quantitative {Psi} occupancy measurement in human mRNAs. Our supervised machine learning models reveal that for every site studied, different signal parameters are required to maximize {Psi} classification accuracy. We show that applying our model is critical for quantification, especially in low-abundance mRNAs. Our engine can be used to profile {Psi}-occupancy across cell types and cell states, thus providing critical insights about physiological relevance of {Psi} modification to mRNAs.

genomics↗

bioRxiv2021

Detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct, long-read sequencing

We developed and applied a semi-quantitative method for high-confidence identification of pseudouridylated sites on mammalian mRNAs via direct long-read nanopore sequencing. A comparative analysis of a modification-free transcriptome reveals that the depth of coverage and specific k-mer sequences are critical parameters for accurate basecalling. By adjusting these parameters for high-confidence U-to-C basecalling errors, we identified many known sites of pseudouridylation and uncovered new uridine-modified sites, many of which fall in k-mers that are known targets of pseudouridine synthases. Identified sites were validated using 1,000-mer synthetic RNA controls bearing a single pseudouridine in the center position which demonstrate systematical under-calling using our approach. We identify mRNAs with up to 7 unique modification sites. Our pipeline allows direct detection of low-, medium-, and high-occupancy pseudouridine modifications on native RNA molecules from nanopore sequencing data as well as multiple modifications on the same strand.

bioengineering↗

bioRxiv2019

Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression

Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimers Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimers Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.

neuroscience↗

bioRxiv2018

Structural and Biochemical Studies of Dihydrofolate Reductase from Streptococcus pyogenes as a Target for Antifolate Antibiotics

Streptococcus pyogenes, a beta-hemolytic bacterium, causes a wide spectrum of infections in human including pharyngitis, tonsillitis, scarlet fever, rheumatic fever, and necrotizing fasciitis. Streptococcal infections can also exist as co-infection with methicillin resistant Staphylococcus aureus (MRSA). Trimethoprim-sulfamethoxazole (TMP-SMX) combination has been used for treatment of S. pyogenes and MRSA co-infection. However, resistance to TMP, an inhibitor of dihydrofolate reductase enzyme (DHFR), has challenged the efficacy of TMP-SMX combination. We explored the activity of a series of novel DHFR inhibitors against S. pyogenes. This study identified potent inhibitors of DHFR enzyme from S. pyogenes with excellent inhibitory activity against the growth of the live bacteria. We determined, for the first time, the crystal structure of S. pyogenes DHFR which provides structural insights into design and development of antifolate agents against this global pathogen.

microbiology↗

bioRxiv2018

Strain maps characterize the symmetry of convergence and extension patterns during Zebrafish gastrulation

During gastrulation of the zebrafish embryo, the cap of blastoderm cells organizes into the axial body plan of the embryo with left-right symmetry and head-tail, dorsal-ventral polarities. Our labs have been interested in the mechanics of early development and have investigated whether these large-scale cells movements can be described as tissue-level mechanical strain by a tectonics-based approach. The first step is to image the positions of all nuclei from mid-epiboy to early segmentation by digital sheet light microscopy (DSLM), organize the surface of the embryo into multi-cell spherical domains, construct velocity fields from the movements of these domains and extract 3D strain rate maps. Tensile/expansive and compressive strains in the axial and equatorial directions are detected during gastrulation as anterior and posterior expansion along the anterior-posterior axis and medial-lateral compression across the dorsal-ventral axis corresponding to convergence and extension. In later stages in development are represented by localized medial expansion at the onset of segmentation and anterior expansion at the onset of neurulation. Symmetric patterns of rotation are first detected in the animal hemispheres at mid-epiboly and then the vegetal hemispheres by the end of gastrulation. By analysing the temporal sequence of large scale movements, deformations across the embryo can be attributed to a combination of epiboly and dorsal convergence-extension.\n\nSignificanceStrain is an emergent property of tissues that originates from the mechanical coupling of cell-cell and cell-substrate interactions, individual cell shape changes, and cell level forces. By imaging the positions of nuclei from mid-epiboly to early segmentation of the zebrafish embryo we are able to calculate three types of strain maps by a plate tectonics based method. The regions of expansive and compressive axial and equatorial strain correspond to areas undergoing convergence and extension, a major step in the formation of the embryonic body plan as well as the formation of somite and head structures. The most striking signatures of strain are: 1. the bilateral symmetry of linear strain across the anterior-posterior, dorsal-ventral axis during gastrulation, 2. the complementary counter-rotational strains or curl in the animal hemisphere at mid epiboly, and 3. a divergence or saddle point in the region of the dorsal organizer, head-trunk boundary. These strains represent a general method to describe large-scale tissue-level mechanics not only of embryonic development but also tissue homeostasis and disease.

biophysics↗