Biology subjects

Mohammed, A.

Publications and source records attributed to Mohammed, A..

29 records · Page 2Linked to original sources

bioRxiv2021

Membrane Constriction by ESCRT-III Proteins Induces Structural Lipid Bilayer Asymmetries

Lipids in biological membranes are thought to be functionally organized, but few experimental tools can probe nanoscale membrane structure. Using brominated lipids as contrast probes for cryo-EM and a model ESCRT-III membrane remodeling system, we observed leaflet-level and protein-localized lipid structural patterns within highly constricted and thinned membrane nanotubes. These nanotubes differed markedly from protein-free, flat bilayers in leaflet thickness, lipid diffusion rates, and lipid compositional and conformational asymmetries. Simulations and cryo-EM imaging of brominated SDPC showed how a pair of phenylalanine residues scored the outer leaflet with a helical hydrophobic defect where polyunsaturated docosahexaenoyl (DHA) tails accumulated at the bilayer surface. Combining cryo-EM of halogenated lipids with molecular dynamics thus enables new characterizations of the composition and structure of membranes on molecular length scales. One-Sentence SummaryCryo-EM imaging of halogenated lipids and MD simulations provide molecular-scale insight into constricted bilayer structure.

biophysics↗

bioRxiv2021

Treatment with a polyherbal extract improves fat metabolism, attenuates hepatic stellate cell activation and fibrogenesis.

Non-alcoholic steatohepatitis (NASH) involves dysregulations in denovo lipogenesis, fatty acid oxidation, and fibrogenesis. Targeting these pathways holds promise for the treatment of liver disorders. Here we test the extract of a polyherbal formulation (namely Liv.52), which is approved by the Government of Indias Drug Regulatory Authority - AYUSH. The current study evaluates the effect of Liv.52 on denovo lipogenesis, fatty acid oxidation, and fibrogenesis. Both in vivo and in vitro model systems were employed to evaluate the efficacy of this polyherbal formulation. Male Wistar rats were dosed with Liv.52 for 2 weeks (250mg/k.g) and expression levels of the genes involved in de novo lipogenesis and fatty acid oxidation pathways were analysed by quantitative real time PCR. Liv.52 treatment resulted in increased hepatic fatty acid oxidation and decreased de novo lipogenesis in these rats. It also reduced hepatic stellate cell activation in CCL4 treated Wistar rats as evidenced by histological evaluation. For in vitro experiments, HepG2 cells were cultured under lipotoxic conditions (using 200M palmitic acid) and the conditioned media from these cells were used for inducing activation and fibrogenesis in human hepatic stellate cells (HHSteC). Treatment with lipotoxic conditioned media resulted in activation of hepatic stellate cells and fibrogenesis, as evidenced by increased expression of -smooth muscle actin (-SMA), and desmin (markers of stellate cell activation) and increased levels of collagen and lumican (markers of fibrogenesis). Treatment with Liv.52 reversed the up-regulation of -SMA, collagen and lumican levels in HHSteC cells. These results indicate that Liv.52 exerts its hepatoprotective effect by improving fatty acid metabolism and fibrogenesis.

pharmacology and toxicology↗

bioRxiv2020

Machine Learning Identifies Complicated Sepsis Trajectory and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

A complicated clinical course for critically ill patients admitted to the ICU usually includes multiorgan dysfunction and subsequent death. Owning to the heterogeneity, complexity, and unpredictability of the disease progression, patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 hours of PICU admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes that were predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with ten iterations, the overall mean area under the curve reached 0.82. Using the same set of genes, we further achieved an overall area under the curve of 0.72 when tested on an external validation set. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1 and NLRP1 that help to predict sepsis severity or mortality. The discovery of eight novel genetic biomarkers related to the overactive innate immune system and neutrophils functions, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSTranscriptomic biomarkers have long been explored as potential means of earlier disease endotyping. Much of the existing literature has however focused on mortality and discrete outcomes. Additionally, much of prior work in this area has been developed on statistical methods, while recent means of selecting features have not been sufficiently explored. Added value of this studyIn this study, we developed a robust machine learning based model for identifying novel biomarkers of complicated disease courses. We found 20 highly stable genes that predict disease complexity with an average derivation AUROC of 0.82 and validation AUROC of 0.72 within critically ill children, using peripheral blood collected within 24 hrs of ICU admission. Implications of all the available evidenceEarlier identification of disease complexity can inform care management and targeted therapy. Therefore, the 20 gene candidates identified by our rigorous approach, can be used to identify, early in their ICU stay, patients who may ultimately develop significant organ dysfunction and complex care management.

immunology↗

bioRxiv2020

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

The immune system is a complex and dynamic network, crucial for combating infections and maintaining health. Developing a comprehensive digital twin of the immune system requires incorporating essential cellular components and their interactions. This study presents the first blueprint for an immune system digital twin, consisting of a comprehensive and simulatable mechanistic model. It integrates 51 innate and adaptive immune cells, 37 secretory factors, and 11 disease conditions, providing the foundation for developing a multi-scale model. The cellular-level model demonstrates its potential in characterizing immune responses to various single and combinatorial disease conditions. By making the model available in easy-to-use formats directly in the Cell Collective platform, the community can easily and further expand it. This blueprint represents a significant step towards developing general-purpose immune digital twins, with far-reaching implications for the future of digital twin technology in life sciences and healthcare, advancing patient care, and accelerating precision medicine.

systems biology↗

bioRxiv2019

Isolation of the Astacin-like metalloprotease coding gene (astl) and assessment of its insecticidal activity against Spodoptera littoralis and Sitophilus oryzae

Astacin- like metalloprotease (astl) is a multi-domain metallopeptidase that has protease activity against a number of organisms; including fish, frogs, birds and insects. In this present investigation, the full length of astl cDNA was cloned from spider species, Hasarius adansoni. Sequencing of the cloned astl cDNA proved that its full length including 802 bp with 714bp open reading frame encoding for 238 amino acids. The catalytic domain comprised of 489 nts was cloned and expressed by the yeast expression system Pichia pastoris and its insecticidal activity was determined against two species of agricultural insects Spodoptera littoralis (Lepidoptera:Noctuidae) and Sitophilus oryzae (Coleoptera:Curculionidae). Bioassay was performed using three concentrations (100,500 and 1000 ppm) for four days for S. littoralis and 14 days for S. oryzae. In addition, the astl was fused to the GNA snowdrop lectin in the same frame and expressed in P. pastoris. The synergistic effect of astl and GNA was examined on the S. littoralis larvae and S. oryzae adults. The mortality percentages of the fused protein (Ha-astl/GNA) \"1000 ppm\" after 4 days, were 78.6%{+/-} 4.16 and 71.66% {+/-}3.51 for first and second spodpotera larval instars, respectively. While, lower mortality of the fused protein of the same concentration was observed on S. oryzae adults, 49.3{+/-}2.08 %.

molecular biology↗

bioRxiv2019

Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

BackgroundSickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable earlier identification and treatment, and potentially reduce mortality. We tested the hypothesis that machine learning physiomarkers could predict the development of organ dysfunction in an adult sample of patients with SCD admitted to intensive care units.\n\nMethods and FindingsWe studied 63 sequential SCD patients with 163 patient encounters, mean age 33.0{+/-}11.0 years, admitted to intensive care units, some of whom (6.7%) had pre-existing cardiovascular or kidney disease. A subset of these patient encounters (37; 23%) met sequential organ failure assessment (SOFA) criteria. The site of organ failure included: central nervous system (32), cardiovascular (11), renal (10), liver (7), respiratory (5) and coagulation (2) systems. Most (81.5%) of the patient encounters who experienced organ failure had single organ failure. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast fourier transform, energy, continuous wavelet transform, etc.) derived from heart rate, blood pressure, and respiratory rate were identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure, from SCD patients who did not meet the criteria. A random forest model accurately predicted organ failure up to six hours prior to onset, with a five-fold cross-validation accuracy of 94.57% (average sensitivity and specificity of 90.24% and 98.9% respectively).\n\nConclusionsThis study demonstrates the viability of using machine learning to predict acute physiological deterioration heralded organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

bioinformatics↗

bioRxiv2019

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a severe health condition caused by uncontrolled sepsis. Advancements in the high-throughput sequencing techniques have risen the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in the development and progression of pediatric septic shock. Fifty-four differentially expressed pediatric septic shock gene biomarkers were identified using gene expression data from 181 pediatric intensive care unit (PICU) patients within the first 24 hours of admission. The gene expression signatures discovered showed discriminatory power between pediatric septic shock survivors and nonsurvivors types. Using functional enrichment analysis of differentially expressed genes (DEGs), the known genes and pathways in septic shock were validated, and unexplored septic shock-related genes and functional groups were identified. Septic shock survivors were distinguished from septic shock non-survivors by differential expression of genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.

bioinformatics↗

bioRxiv2018

Assessing the blood-host plasticity and dispersal rate of the malaria vector Anopheles coluzzii

Difficulties with observing the dispersal of insect vectors in the field have hampered understanding of several aspects of their behaviour linked to disease transmission. Here, a novel method based on detection of blood-meal sources is introduced to inform two critical and understudied mosquito behaviours: plasticity in the malaria vectors blood-host choice and vector dispersal. Strategically located collections of Anopheles coluzzii from a malaria-endemic village of southern Ghana showed statistically significant variation in host species composition of mosquito blood-meals. Trialling a new sampling approach gave the first estimates for the remarkably local spatial scale across which host choice is plastic. Using quantitative PCR, the blood-meal digestion was then quantified for field-caught mosquitoes and calibrated according to timed blood digestion in colony mosquitoes. We demonstrate how this new molecular Sella score approach can be used to estimate the dispersal rate of blood-feeding vectors caught in the field.

ecology↗

bioRxiv2018

A systematic review and meta-analysis of the human blood index of the major African malaria vectors

BACKGROUNDThe proportion of mosquito blood-meals that are of human origin, referred to as the human blood index or HBI, is a key determinant of malaria transmission. We conducted a systematic review of the HBI for the major African malaria vectors.\n\nRESULTSEvidence is presented for higher HBI among Anopheles gambiae (M/S forms and An. coluzzii/An. gambiae s.s. are not distinguished for most studies and therefore combined) as well as An. funestus when compared with An. arabiensis (prevalence odds ratio adjusted for collection location [i.e. indoor or outdoor]: 1.62; 95%CI 1.09-2.42; 1.84; 95%CI 1.35-2.52, respectively). This finding is keeping with the entomological literature which describes An. arabiensis to be more zoophagic than the other major African vectors. However, analysis also revealed that HBI was more associated with location of mosquito captures (R2=0.29) than with mosquito (sibling) species (R2=0.11).\n\nCONCLUSIONSOur findings call into question the appropriateness of current methods of assessing host preferences among disease vectors and have important implications for strategizing vector control.

ecology↗

bioRxiv2018

The neuronal ceroid lipofuscinosis protein, Cln7, regulates neural development from the post-synaptic cell.

The neuronal ceroid lipofuscinoses (NCLs) are a group of fatal, monogenic neurodegenerative disorders with an early onset in infancy or childhood. Despite identification of the genes disrupted in each form of the disease, their normal cellular role and how their deficits lead to disease pathology is not fully understood. Cln7, a major facilitator superfamily domain-containing protein, is affected in a late infantile-onset form of NCL. Using the Drosophila larval neuromuscular junction as a model to study neural development, we demonstrate that Cln7 is required for the normal growth of synapses. In a Cln7 mutant, synapses fail to develop fully leading to reduced function and behavioral changes with dysregulation of TOR activity. Cln7 expression is restricted to the post-synaptic cell and the protein localizes to vesicles immediately adjacent to the post-synaptic membrane. Our data suggest an involvement for Cln7 in regulating trans-synaptic communication.

developmental biology↗

bioRxiv2017

CancerDiscover: A configurable pipeline for cancer prediction and biomarker identification using machine learning framework

MotivationUse of various high-throughput screening techniques has resulted in an abundance of data, whose complete utility is limited by the tools available for processing and analysis. Machine learning holds great potential for deciphering these data in the context of cancer classification and biomarker identification. However, current machine learning tools require manual processing of raw data from various sequencing platforms, which is both tedious and time-consuming. The current classification tools lack flexibility in choosing the best feature selection algorithms from a range of algorithms and most importantly inability to compare various learning algorithms.\n\nResultsWe developed CancerDiscover, an open-source software pipeline that allows users to efficiently and automatically integrate large high-throughput datasets, preprocess, normalize, and selects best performing features from multiple feature selection algorithms. The pipeline lets users apply various learning algorithms and generates multiple classification models and evaluation reports that distinguish cancer from normal samples, as well as different types and subtypes of cancer.\n\nAvailability and ImplementationThe open source pipeline is freely available for download at https://github.com/HelikarLab/CancerDiscover.\n\nContactelikar2@unl.edu\n\nSupplementary InformationPlease refer to the CancerDiscover README (Supplementary File 1) for detailed instructions on installation and operation of the pipeline. For a list of available feature selection methods, see Supplementary File 2.

bioinformatics↗