Biology subjects

Mahmood, U.

Publications and source records attributed to Mahmood, U..

6 recordsLinked to original sources

bioRxiv2026

Funneliformis mosseae and Pseudomonas putida-symbiotic interaction promote drought resilience in Citrus reticulata

Climate change and increasing drought conditions significantly impedes citrus productivity in subtropical and tropical regions. This study explores the potential of combining arbuscular mycorrhizal fungi (AMF) Funneliformis mosseae and plant growth-promoting rhizobacteria (PGPR) Pseudomonas putida to mitigate drought resilience in Citrus reticulata (Red tangerine). AMF-mediated drought tolerance has been extensively documented; however, the collegial influence of PGPR and AMF on phytohormone signaling, photosynthetic efficiency, nutrient acquisition, and gene expression remains largely unexplored in citrus. We conducted a greenhouse experiment under both well water and drought stress conditions to assess the physiological and molecular responses to individual and co-inoculation with PGPR and AMF. Drought-stressed citrus plants, inoculated with AMF and PGPR, demonstrated significantly improved leaf water potential, stomatal conductance, carbon assimilation, and antioxidant defense. PGPR-AMF co-inoculation enhanced chlorophyll stability, osmotic adjustment, and nutrient uptake, while significantly reducing lipid peroxidation and ROS accumulation. The turquoise module emerged from transcriptomic and gene co-expression network analysis (WGCNA) as a potential key regulator of stress adaptation, revealed key regulatory transcription factors, e.g., CrMYB4, CrZFP8, CrSOS5, CrRGFR2, and CrQUA1, that were upregulated under combined inoculation, highlighting their potential role in stress adaptation. Our findings demonstrate that the synergistic PGPR-AMF interaction improves antioxidant enzyme activities and modulates gene expression to promote drought tolerance, providing new insights into the microbiomes role in plant resilience. These results offer a potential strategy to boost citrus growth and yield under water scarcity, with broad implications for agricultural resilience to climate change.

plant biology↗

bioRxiv2024

Genome-wide identification, characterization and expression analysis of CsC3H gene family in cucumber (Cucumis sativus L) under various abiotic stresses

The CCCH zinc finger (C3H) gene family plays a significant role in plant growth, development, and stress response mechanisms. In cucumber (Cucumis sativus L), the CsC3H gene family has been implicated in mediating responses to abiotic stresses, though its functional characterization remains underexplored. This study provides a comprehensive genome-wide identification, characterization, and expression analysis of the CsC3H gene family in cucumber, with a focus on their roles in stress tolerance. 38 CsC3H genes have been discovered, and detailed conserved motif and domain analyses revealed key structural features essential for their function. Phylogenetic analysis classified the CsC3H proteins into four distinct subfamilies (CsC3H I-IV), highlighting functional diversification. The gene duplication and expansion analysis indicated that the C3H gene family grew due to both tandem and segmental duplications, with segmental duplications playing a predominant role. qRT-PCR expression profiling revealed widespread expression of CsC3H genes across various cucumber tissues, with distinct differential expression patterns under waterlogging and hormonal treatments (NAA, ETH, MeJA). Notably, CsC3H9 was localized to the nucleus, indicating its potential involvement in regulating cellular processes under stress. These results offer novel information for future studies focused on leveraging genetic innovations to improve stress resilience in cucurbits and other crops while also offering new perspectives on the functional role of CsC3H genes in cucumber.

bioinformatics↗

bioRxiv2024

Cancer radiomic feature variations due to reconstruction kernel choice and integral tube current.

PurposeRadiological cancer imaging features, or radiomics features, can be derived to diagnose disease or predict treatment response. However, variability between vendors, scanners, protocols, and even reconstruction software versions is an obstacle to the clinical use of radiomics features. This study aimed to characterize the impact of kernel reconstruction differences and integral tube current settings on radiomic features extracted from computed tomography (CT) scans. MethodsRadiomic features were extracted from CT scans of a 3D-printed phantom with five imprinted tumors using the CERR software system, resulting in 282 features. Batch effects were assessed via principal component analysis (PCA) and correlation measures. Robustness was measured using the concordance correlation coefficient (CCC) and Pearson correlation coefficient. Statistical analysis was performed using R software. ResultsPCA identified two clusters comprised of Standard, ASIRs, ASIRV, and soft kernels in one, and Lung and Bone Kernels in the other. Features displayed a gradient from ASIR10 to ASIR50 and ASIRV1 to ASIRV5 in terms of nearness to Standard Kernel feature values. Feature correlation matrices revealed little change in ASIRs, ASIRVs, and the Standard Kernel, but showed significant changes in Bone and Lung Kernel results. Combat-algorithm correction improved robustness, particularly in first-order statistic features, and mitigated batch effects due to the ASIRs and the standard kernel. Forty (40) out of 282 features were identified as robust. However, Combat-based correction performed poorly in harmonizing Bone and Lung reconstruction kernels. ConclusionsThe robustness of means and median radiomic features across kernel reconstruction choices, in contrast to the lack of robustness in many other radiomic features, suggests that kernel reconstruction effects are not well-addressed by current harmonization methods.

bioinformatics↗

bioRxiv2019

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

PurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.\n\nMethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.\n\nResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.\n\nConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

biophysics↗

bioRxiv2019

Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

PurposeThis study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans.\n\nMethodsUsing a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values. The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.\n\nResultsTwenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDVs (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDVs (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDVs (%) within {+/-} 10%.\n\nConclusionWe demonstrated the feasibility with which patient informed 3D printed phantoms can be manufactured directly from lesions seen on CT scans, and demonstrate their potential use for the assessment of robust quantitative radiomic features.

biophysics↗

bioRxiv2018

MGDb: An analyzed database and a genomic resource of mango (Mangifera Indica L.) cultivars for mango research

Mango is one of the famous and fifth most important subtropical/tropical fruit crops worldwide with the production centered in India and South-East Asia. Recently, there has been a worldwide interest in mango genomics to produce tools for Marker Assisted Selection and trait association. There are no web-based analyzed genomic resources available for mango particularly. Hence a complete mango genomic resource was required for improvement in research and management of mango germplasm. In this project, we have done comparative transcriptome analysis of four mango cultivars i.e. cv. Langra, cv. Zill, cv. Shelly and cv. Kent from Pakistan, China, Israel, and Mexico respectively. The raw data is obtained through De-novo sequence assembly which generated 30,953-85,036 unigenes from RNA-Seq datasets of mango cultivars. The project is aimed to provide the scientific community and general public a mango genomic resource and allow the user to examine their data against our analyzed mango genome databases of four cultivars (cv. Langra, cv. Zill, cv. Shelly and cv. Kent). A mango web genomic resource MGdb, is based on 3-tier architecture, developed using Python, flat file database, and JavaScript. It contains the information of predicted genes of the whole genome, the unigenes annotated by homologous genes in other species, and GO (Gene Ontology) terms which provide a glimpse of the traits in which they are involved. This web genomic resource can be of immense use in the assessment of the research, development of the medicines, understanding genetics and provides useful bioinformatics solution for analysis of nucleotide sequence data. We report here worlds first web-based genomic resource particularly of mango for genetic improvement and management of mango genome.

bioinformatics↗