Biology subjects

Ali, M. Z.

Publications and source records attributed to Ali, M. Z..

7 recordsLinked to original sources

bioRxiv2025

Elucidating the perception, awareness and biosecurity practices among the live poultry/poultry-meat sellers to mitigate the risk of zoonotic salmonellosis

Zoonotic salmonellosis remains a major public health concern, especially in low-resource settings where poultry handling practices are often informal. Biosecurity measure of live poultry/poultry-meat vending shop is necessary to address this zoonoses. This study investigated the knowledge, attitudes, and biosecurity practices of poultry/poultry-meat sellers toward the risk of zoonotic salmonellosis in Bangladesh. A quantitative, descriptive, cross-sectional study was conducted among the 432 poultry/poultry-meat sellers through a structured questionnaire in multistage sampling approach. Majority of the poultry/poultry-meat sellers (n=432) were male (75.5%), age between 26-34 years (31.9%), had primary education (28.9%), experience of [≤]5 years (40.3%), and monthly income was <20000 BDT (43.3%). Additionally, minimal had training on live poultry marketing (33.1%), and poultry-meat processing and marketing (28.5%). The 33.34% and 62.03% of poultry/poultry-meat sellers had the good knowledge and positive attitudes respectively. While 41.43% were involved in correct biosecurity practice. Moreover, the mean KAP score was 53.12{+/-}0.89, 68.59{+/-}0.79 and 54.58{+/-}0.73 respectively. From the multivariate analyses, higher education was a strong predictor of good knowledge (Bachelors degree: OR=6.64, 95%CI:2.59-17.04, p<0.01), positive attitude (Bachelors degree: OR=13.48, 95%CI:3.72-48.87, p<0.01), and correct biosecurity practices (HS: OR=5.52, 95%CI:2.62-11.63, p<0.01; Bachelors degree: OR=4.81, 95%CI:2.03-11.39, p<0.01). The sellers with good knowledge, positive attitudes, and correct biosecurity practices perceived higher zoonotic risk (median=7, p<0.05), with perception co-relation that significantly influenced by education (r=0.131, p<0.01) and training (r=0.084, p<0.05). Despite moderate awareness, sellers showed notable gaps in safe practices, highlighting the need for practical training that enhances risk perception, promotes behaviour change, and builds hands-on biosecurity skills.

scientific communication and education↗

bioRxiv2023

Regulatory properties of transcription factors with diverse mechanistic function

Transcription factors (TFs) regulate the process of transcription through the modulation of different kinetic steps. Although models can often describe the observed transcriptional output of a measured gene, predicting a TFs role on a given promoter requires an understanding of how the TF alters each step of the transcription process. In this work, we use a simple model of transcription to assess the role of promoter identity, and the degree to which TFs alter binding of RNAP (stabilization) and initiation of transcription (acceleration) on three primary characteristics: the range of steady-state regulation, cell-to-cell variability in expression, and the dynamic response time of a regulated gene. We find that steady state regulation and the response time of a gene behave uniquely for TFs that regulate incoherently, i.e that speed up one step but slow the other. These TFs function as activators when regulating weak promoters but switch to repression when the promoters are strong or vice versa. Furthermore, we find that TFs with this regulatory make-up have dynamic implications, with one type of incoherent mode configuring the promoter to respond more slowly at intermediate TF concentrations. We also demonstrate that the noise of gene expression for these TFs is sensitive to promoter strength, with a distinct non-monotonic profile that is apparent under stronger promoters. Taken together, our work uncovers the coupling between promoters and TF regulatory modes with implications for understanding natural promoters and engineering synthetic gene circuits with desired expression properties.

systems biology↗

bioRxiv2021

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in prokaryotic genome

Sigma70 factor plays a crucial role in prokaryotes and regulates the transcription of most of the housekeeping genes. One of the major challenges is to predict the sigma70 promoter or sigma70 factor binding site with high precision. In this study, we trained and evaluate our models on a dataset consists of 741 sigma70 promoters and 1400 non-promoters. We have generated a wide range of features around 8000 which includes Dinucleotide Auto-Correlation, Dinucleotide Cross-Correlation, Dinucleotide Auto Cross-Correlation, Moran Auto-Correlation, Normalized Moreau-Broto Auto-Correlation, Parallel Correlation Pseudo Tri-Nucleotide Composition, etc. Our SVM based model achieved maximum accuracy 97.38% with AUROC 0.99 on training dataset, using 200 selected features. In order to check the robustness of our model we have tested our model on the independent dataset made by using latest version of RegulonDB10.8, which included 1134 sigma70 and 638 non-sigma70 promoters, and able to achieve accuracy of 90.41% with AUROC of 0.95. We have developed a method, Sigma70Pred, which is available as webserver and standalone packages at https://webs.iiitd.edu.in/raghava/sigma70pred/. The services are freely accessible.

bioinformatics↗

bioRxiv2021

Controlling gene expression timing through gene regulatory architecture

Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at "extremes" of network size or binding affinity, but rather in an intermediate window of either quantity. Author summaryRegulated genes are able to respond to stimuli in order to ramp up or down production of specific proteins. Although there is considerable focus on the magnitude (or fold-change) of the response and how that depends on the architectural details of the regulatory DNA, the dynamics, which dictates the response time of the gene, is another key feature of a gene that is encoded within the DNA. Unraveling the rules that dictate both the response time of a gene and the precision of that response encoded in the DNA poses a fundamental problem. In this manuscript, we systematically investigate how the response time of genes in auto-regulatory networks is controlled by the molecular details of the network. In particular, we find that network size and TF-binding affinity are key parameters that can slow, in the case of auto-activation, or speed up, in the case of auto-repression, the response time of not only the auto-regulated gene but also the genes that are controlled by the auto-regulated TF. In addition, we find that the precision of the response depends crucially on these characteristics.

systems biology↗

bioRxiv2019

Regulatory asymmetry in the negative single-input module network motif: Role of network size, growth rate and binding affinity

The single-input module (SIM) is a regulatory motif capable of coordinating gene expression across functionally related genes. We explore the relationship between regulation of the central autoregulated TF in a negatively regulated SIM and the target genes using a synthetic biology approach paired with stochastic simulations. Surprisingly, we find a fundamental asymmetry in the level of regulation experienced by the TF gene and its targets, even if they have identical regulatory DNA; the TF gene experiences stronger repression than its targets. This asymmetry is not predicted from deterministic modeling of the system but is revealed from corresponding stochastic simulations. The magnitude of asymmetry depends on factors such as the number of targets in the SIM, TF degradation rate (or growth rate) and TF binding affinity. Beyond implications for SIM motifs, the influence of network connectivity on regulatory levels highlights an interesting challenge for predictive models of gene regulation.

systems biology↗

bioRxiv2019

Probing mechanisms of transcription elongation through cell-to-cell variability of RNA polymerase

The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. While biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNAP molecules engaged in the process of transcribing a gene of interest. In this manuscript, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and inter-polymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures which can further be used to discern between them. Intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in S. cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.\n\nAuthor SummaryThe process of transcription comprises many distinct steps and understanding the regulation of each of these steps provides insight into how the levels of gene expression are controlled in the cell. In this manuscript, we use stochastic simulations to explore how regulation at the level of elongation and initiation together influences the distribution of actively transcribing RNAP on a gene. We find that each of these steps of regulation leaves a distinct imprint on the gene-to-gene variability of number of actively transcribing RNAP on the gene and their inter-RNAP distances. Using these results, we analyze recent experimental data of transcribing RNAP distributions and find that the perturbations in these studies primarily impact the transcription initiation dynamics of the genes.

cell biology↗

bioRxiv2018

A Persistence Detector for Metabolic Network Rewiring in an Animal

Biological systems must possess mechanisms that prevent inappropriate responses to spurious environmental signals. Gene regulatory network circuitries known as coherent type 1 feed-forward loops (FFLs) with AND-logic gates have been proposed to function as a persistence detector because it generates a delay in target activation and prevents target induction unless the input signal is sustained. While such a circuit has been found for the L-arabinose utilization system in E. coli, their existence and relevance multicellular organisms has remained unclear. Here, we identify the first persistence detector in an animal that redirects propionate breakdown to a shunt pathway when flux through the canonical propionate breakdown pathway is perturbed. We propose that this mechanism has evolved to ensure the shunt pathway stays off unless propionate accumulation is persistent because the shunt pathway generates highly toxic acrylate. Our study uniquely connects persistence detector circuitry to a physiological response in an animal.

systems biology↗