Biology subjects

Haque, S.

Publications and source records attributed to Haque, S..

4 recordsLinked to original sources

bioRxiv2020

Exosomes are predominantly loaded with mRNA transcript encoding cytoplasmic proteins and exclude mRNA transcript encoding nuclear proteins

Exosomes are nanovesicles ([~]30-150 nm diameters) released via an endocytic pathway in almost all mammalian cell types. Exosomes are composed of a lipid bilayer membrane that encloses RNA, miRNA, proteins and DNA. This manuscript unravels how exosome cargo is collected by a highly precise process delineating two separate mRNA transcript entities encoding cytoplasmic and nuclear proteins separately. Ultracentrifuge isolated exosomes were directly converted into cDNA (Exo-cDNA), by a method developed in our laboratory. Cellular RNA was extracted from each cell line and cDNA was prepared (Cell-cDNA). We amplified mRNA transcripts translating cytoplasmic proteins CD10 and CXCR4 and mRNA transcripts translating nuclear proteins such as proliferating cell nuclear antigen (PCNA), CREB-BP, activation induced cytidine deaminase (AID), and terminal deoxynucleotidyl transferase (TdT). We amplified all four different mRNA transcripts (PCNA, CREB-BP, AID, and TdT) from cellular cDNA but none from exosomal cDNA (Exo-cDNA). These findings suggest that exosomes carry mRNA transcripts encoding cytoplasmic proteins only but mRNA transcripts encoding nuclear proteins could not be detected. This important observation could prove to be crucial for the exosome research community since it sheds light on one of the limitations relating to the use of exosomes as biomarkers in cancer biology and other diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/227223v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@102c790org.highwire.dtl.DTLVardef@17bcabdorg.highwire.dtl.DTLVardef@3b5ac0org.highwire.dtl.DTLVardef@c303c8_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2020

Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery

For many horticultural crops, variation in quality (e.g., shape and size) contribute significantly to the crops market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across sweetptoato cultivars can be quantified. We created a sweetpotato shape dataset containing sweetpotato images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 sweetpotato with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape (U.S. No. 1 or Cull) of the sweetpotatoes. Our study serves as the first step towards enabling big data analytics for sweetpotato agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.

bioengineering↗

bioRxiv2020

Silencing of Exosomal miR-181a reverses Pediatric Acute Lymphocytic Leukemia Cell Proliferation

Exosomes are cell-generated nano-vesicles (30-150 nm) found in most biological fluids. Major components of their cargo are lipids, proteins, RNA, DNA, and non-coding RNAs. Exosomes carry the fingerprint of the parental tumor and as such, may regulate tumor growth, progression and metastasis. We investigated the impact of exosomes on cell proliferation in pediatric acute lymphocytic leukemia and its reversal by silencing of exo-miR-181a.We isolated exosomes from serum of acute lymphocytic leukemia pediatric patients (Exo-PALL) and conditioned medium of leukemic cell lines (Exo-CM) by ultracentrifugation. Gene expression was carried out by q-PCR. We found that Exo-PALL promote cell proliferation in leukemic B cell lines as well as in the control B cell line. This exosome-induced cell proliferation is a precise event with up-regulation of proliferative (PCNA, Ki-67) and pro-survival genes (MCL-1, and BCL2), and suppression of pro-apoptotic genes (BAD, BAX). Exo-PALL and Exo-CM both show over expression of miR-181a compared to controls (Exo-HD). Specific silencing of exosomal miR-181a using a miR-181a inhibitor confirms that miR-181a inhibitor treatment reverses Exo-PALL/Exo-CM-induced leukemic cell proliferation in vitro. Altogether, this study suggests that exosomal miR-181a inhibition can be a novel target for growth suppression in pediatric lymphatic leukemia.

cancer biology↗

bioRxiv2019

Quantitative assessment of fecal contamination in multiple environmental sample types in urban communities in Dhaka, Bangladesh using SaniPath microbial approach

Rapid urbanization has led to a growing sanitation crisis in urban areas of Bangladesh and potential exposure to fecal contamination in the urban environment due to inadequate sanitation and poor fecal sludge management. Limited data are available on environmental fecal contamination associated with different exposure pathways in urban Dhaka. We conducted a cross-sectional study to explore the magnitude of fecal contamination in the environment in low-income, high-income, and transient/floating neighborhoods in urban Dhaka. Ten samples were collected from each of 10 environmental compartments in 10 different neighborhoods (4 low-income, 4 high-income and 2 transient/floating neighborhoods). These 1,000 samples were analyzed with the IDEXX-Quanti-Tray technique to determine most-probable-number (MPN) of E. coli. Samples of open drains (6.91 log10 MPN/100 mL), surface water (5.28 log10 MPN/100 mL), floodwater (4.60 log10 MPN/100 mL), produce (3.19 log10 MPN/serving), soil (2.29 log10 MPN/gram), and street food (1.79 log10 MPN/gram) had the highest mean log10 E. coli contamination compared to other samples. The contamination concentrations did not differ between low-income and high-income neighborhoods for shared latrine swabs, open drains, municipal water, produce, and street foodsamples. E. coli contamination were significantly higher (p <0.05) in low-income neighborhoods compared to high-income for soil (0.91 log10 MPN/gram, 95% CI, 0.39, 1.43), bathing water (0.98 log10 MPN/100 mL, 95% CI, 0.41, 1.54), non-municipal water (0.64 log10 MPN/100 mL, 95% CI, 0.24, 1.04), surface water (1.92 log10 MPN/100 mL, 95% CI, 1.44, 2.40), and floodwater (0.48 log10 MPN/100 mL, 95% CI, 0.03, 0.92) samples. E. coli contamination were significantly higher (p<0.05) in low-income neighborhoods compared to transient/floating neighborhoods for drain water, bathing water, non-municipal water and surface water. Future studies should examine behavior that brings people into contact with the environment and assess the extent of exposure to fecal contamination in the environment through multiple pathways and associated risks.

microbiology↗