Biology subjects

Ahmadi, N.

Publications and source records attributed to Ahmadi, N..

12 recordsLinked to original sources

bioRxiv2026

ZFP36 is a post-transcriptional regulator of the P2X4 receptor in myeloid cells

P2X4 is an ATP-gated purinergic receptor upregulated in spinal microglia after peripheral nerve injury, where it plays a key role in neuropathic pain. Yet, the molecular mechanisms controlling P2X4 expression remain incompletely understood. To address this question, we examined P2X4 mRNA expression and translation in spinal microglia following nerve injury. While total P2X4 mRNA levels remained unchanged, ribosome-associated P2X4 mRNA increased, revealing a previously unrecognized level of translational regulation. We therefore investigated whether the RNA-binding protein ZFP36 (tristetraprolin), a well-established regulator of inflammatory mRNAs, controls P2X4 expression. Our results demonstrate that the P2X4 3'UTR strongly repressed reporter and protein expression and that ZFP36 emerged as a negative regulator of P2X4 mRNA. This regulation involved phosphorylation-dependent signaling through p38 MAPK and 14-3-3. Moreover, ZFP36 knockdown increased P2X4 protein expression, while manipulation of ZFP36 phosphorylation altered P2X4-dependent ATP-evoked calcium responses. Together, these findings identify ZFP36 as a previously unrecognized post-transcriptional regulator of P2X4, extending its role beyond inflammatory mediators to a functional purinergic receptor. They reveal post-transcriptional control of receptor availability as a novel regulatory layer linking myeloid cell state to purinergic responsiveness and provide a new framework for understanding microglial P2X4 regulation in neuropathic pain.

neuroscience↗

bioRxiv2022

Genome scan of landrace populations of the self-fertilizing crop species rice, collected across time, revealed climate changes' selective footprints in the genes network regulating flowering time.

Analysis of the genetic bases of adaptation to climate changes are often conducted on natural populations. We report here on a study based on diachronic sampling (1980 & 2010) of the self-fertilising crop species, Oryza sativa (Asian rice) and Oryza glaberrima (African rice), in the tropical forest and the Sudanian savannah of West Africa. First, using historical meteorological data we confirmed changes in temperatures (+1{degrees}C on average) and rainfall regime (less predictable and reduced amount) in the target area. Second, phenotyping the populations for phenology, we observed significantly earlier heading time (up to 10 days) in the 2010 samples. Third, we implemented two genome-scan methods, one of which specially developed for selfing species, and detected 31 independent selection footprints. These loci showed significant enrichment in genes involved in reproductive processes and bore known heading time QTLs and genes, including OsGI, Hd1 and OsphyB. This rapid adaptive evolution, originated from subtle changes in the standing variation in genetic network regulating heading time, did not translate into predominance of multilocus genotypes, as it is often the case in selfing plants, and into notable selective sweeps. We argue that this high adaptive potential results from the multiline genetic structure of the rice landraces, and the rather large and imbricated genetic diversity of the rice meta-population at the farm, the village and the region levels, that hosted the adaptive variants in multiple genetic backgrounds well before the advent of the environmental selective pressure. The complex selection footprints observed in this empirical study calls for further model development on genetic bases of plant adaptation to environmental changes.

genetics↗

bioRxiv2021

Physiological activation of the nephron central command drives endogenous kidney tissue regeneration

Tissue regeneration is limited in several organs including the kidney, contributing to the high prevalence of kidney disease globally. However, evolutionary and physiological adaptive responses and the presence of renal progenitor cells suggest existing remodeling capacity. This study uncovered a novel endogenous tissue remodeling mechanism in the kidney that is activated by the loss of body fluid and salt and involves a unique niche of chief cells called macula densa (MD) that control resident progenitor cells via secreted angiogenic, growth and extracellular matrix remodeling factors, cytokines and chemokines. Serial intravital imaging, MD Wnt mouse models and transcriptome analysis provide functional and molecular characterization of this newly identified MD program for kidney regeneration complemented with human and therapeutic translation. The concept that chief cells responding to organ-specific physiological inputs control local progenitors and direct them to remodel or repair tissues may be applicable to other organs and diverse tissue regenerative therapeutic strategies.

physiology↗

bioRxiv2020

Genomic prediction of arsenic tolerance and grain yield in rice. Contribution of trait-specific markers and multi environment models

Many rice-growing areas are affected by high concentrations of arsenic (As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability (PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic (1600 K SNP) and phenotypic (grain arsenic content, grain yield and days to flowering, observed under two irrigation systems over two years) data of the Bengal and Assam Aus Panel (BAAP). Under the base-line single environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As respectively, the three prediction methods (BL, GBLUP and RKHS) considered performed similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for G x E interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest PA (0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the BAAP. It also applies to breeding for other complex traits evaluated under multiple environments.

genetics↗

bioRxiv2020

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

neuroscience↗

bioRxiv2020

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

ObjectiveThere has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. ApproachTo address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. Main resultsExperimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. SignificanceOverall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

neuroscience↗

bioRxiv2020

Inferring entire spiking activity from local field potentials with deep learning

Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Understanding the relationship between these two signals is essential for gaining deeper insight into neuronal coding and information processing in the brain and is also relevant to brain-machine interface (BMI) research. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. These spiking activities that are typically extracted via threshold-based technique may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another spiking activity in the form of a continuous signal, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by employing a deep learning method to infer ESA from LFPs intracortically recorded from the motor cortex area of two monkeys performing different tasks. Results from long-term recording sessions and across different tasks revealed that the inference accuracy of ESA yielded consistently and significantly higher accuracy than that of SUA and MUA. In addition, local motor potential (LMP) was found to be the most highly predictive feature compared to other LFP features. The overall results indicate that LFPs contain substantial information about the spikes, particularly ESA, which could be useful for the development of LFP-based BMIs. The results also suggest the potential use of ESA as an alternative neuronal population activity measure for analysing neural responses to stimuli or behavioural tasks.

neuroscience↗

bioRxiv2018

A Novel Workflow for Helth Survey Analysis: Results on the Yazd Health Study (YaHS)

Health surveys are one of the most prominent sources of information in medical sciences. Analysis of a health survey not only gives information about the inherent features of the population under study but also sheds light on the interactions between different health factors. Even though there has been a multitude of methodologies proposed for survey analysis, many of them are biased towards the previous hypotheses, the fact which may lead to spurious results by neglecting the whole structure of relations among the factors. Here, we proposed our novel workflow on survey analysis. By gathering the previously less-utilized graphical models on survey analysis, newly-proposed methods, and commonly-used tools of prediction and hypothesis testing our workflow provides the medical community with an end-to-end pipeline for comprehensive statistical analysis of health surveys. To demonstrate the functionality of our workflow, we validate it on the Yazd Health Study (YaHS) dataset, in which a questionnaire of 300 questions from 10000 participants associated with 40 laboratory measurements from a subset of 4010 individuals is acquired from the residents of the Yazd greater area of Iran. Many of our findings are aligned with previous medical knowledge. Most of our novel findings are surprisingly observed in the clinics, each of which is potential to raise a promising hypothesis.

bioinformatics↗

bioRxiv2018

Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenario: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. The most realistic trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction was up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

genetics↗

bioRxiv2018

Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

The high concentration of arsenic in the paddy fields and, consequently, in the rice grains is a critical issue in many rice-growing areas. Breeding arsenic tolerant rice varieties that prevent As uptake and its accumulation in the grains is a major mitigation options. However, the genetic control of the trait is complex, involving large number of gene of limited individual effect, and raises the question of the most efficient breeding method. Using data from three years of experiment in a naturally arsenic-reach field, we analysed the performances of the two major breeding methods: conventional, quantitative trait loci based, selection targeting loci involved in arsenic tolerance, and the emerging, genomic selection, predicting genetic values without prior hypotheses on causal relationships between markers and target traits. We showed that once calibrated in a reference population the accuracy of genomic prediction of arsenic content in the grains of the breeding population was rather high, ensuring genetic gains per time unite close to phenotypic selection. Conversely, selection targeting quantitative loci proved to be less robust as, though in agreement with the literature on the genetic bases of arsenic tolerance, few target loci identified in the reference population could be validated in the breeding population.

genetics↗

bioRxiv2018

Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The accuracy of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, prediction accuracy for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for across population validation (progeny validation). Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in accuracy ranging from - 6% to 4% depending on the trait and on the statistical model concerned. The accuracy of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to AWD are discussed.

genetics↗

bioRxiv2017

Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

neuroscience↗