Bioinformatics Commons^™

Molecular Mechanisms Of Opioid Use Disorder In Human Brain Models, Emily Mendez

Dissertations & Theses (Open Access)

Opioid use disorder (OUD) is a national and global public health crisis with no end in sight. While studies from animal models hint at widespread epigenetic and transcriptomic alterations of opioid drugs, the molecular consequences of long-term exposure to opioid drugs in human brain is still unclear, and human-centered translational models are necessary to discern the human cell type-specific effects of OUD.

Using postmortem brain Brodmann area 9 (BA9) from the UTHealth Brain Collection for Research on Psychiatric Disorders, I identified angiogenic gene networks perturbed in the RNA and protein of OUD subjects, as well as downregulation of many neuron-correlated …

Development Of An Enhanced Sampling Workflow To Accelerate Molecular Docking With Sparse Biophysical Information, Zachary Stichter

Masters Theses & Specialist Projects

Rapid docking of flexible biological macromolecules remains a significant open challenge in protein structure determination. While rigid docking is relatively simple with toolkits such as TagDock, a key obstacle to rapid flexible docking is the complexity and roughness of the free energy surface associated with protein conformational motion (often termed the many-minima problem), meaning conventional molecular dynamics methods do not effectively sample protein conformations near the interaction complex in accessible timescales. Methods such as metadynamics and replica exchange molecular dynamics exist to ameliorate this obstacle, yet these methods use nonphysical biases or random swaps to enhance sampling. In contrast, high …

Annotation Of Hypothetical Genes In Lactococcus Lactis Ssp. Il403, Jennifer A. Tangires

Student Scholar Showcase

The human gastrointestinal tract (GIT) harnesses various microbial organisms involved in almost all processes of physiological homeostasis, among these are lactic acid bacteria (LAB). These bacteria, almost all of which belong to the order Lactobacillales, are able to produce lactic acid, and play an important role in food preservation because they produce bacteriocins. Bacteriocins are antimicrobial proteins that are used to fight off related bacteria in their environment that are competing for the same resources. This study focuses on a specific LAB strain, Lactococcus lactis ssp. IL1403 where 21.9% of its predicted genes have not yet been assigned a function. …

Update On The Role Of Muc13 In Pancreatic Cancer: A Promising Early Detection Biomarker, Anupam Dhasmana, Swati Dhasmana, Sheema Khan, Meena Jaggi, Murali M. Yallapu, Subhash C. Chauhan

Research Symposium

Background: With the rise in pancreatic cancer (PanCa) prevalence and mortality rate, by 2030 it will secure second position among leading causes of cancer-related deaths. Due to poor prognosis of PanCa only 11% of PanCa patients have a 5-year survival rate, resulting in an equal mortality rate and incidence rate. 85% of PanCa are Pancreatic ductal adenocarcinoma (PDAC). The main clinical challenge with PanCa is poor treatment outcomes due the late diagnosis. Currently, there are traditional biomarkers panels available for diagnosis, however, these biomarkers do not have optimal sensitivity and specificity for PanCa. Considering this alarming unmet clinic need, our …

Trip13’S Crucial Role In Pancreatic Cancer Progression, Swati Dhasmana, Anupam Dhasmana, Stella Rios, Iris A. Enriquez-Perez, Sheema Khan, Farrukh Afaq, Upender Manne, Murali M. Yallapu, Subhash Chauhan

Research Symposium

Background: Pancreatic cancer, characterized by its high mortality rate, stands as one of the most aggressive cancer forms. The projected surge in pancreatic cancer-related deaths, making it the second leading cause in the United States by 2030, underscores the urgency for effective early screening tools. This study employs data mining methods to scrutinize bioinformatic data surrounding TRIP13. Examining differential expression across various cancers, correlating TRIP13 expression with pancreatic cancer stages, exploring associations with common cancer genes, and analyzing overall survival rates constitute the core investigations. Integrated with molecular biology techniques, the study further quantifies TRIP13 expression in progressive pancreatic cancer …

Proteomic Analysis Of Stress Associated Factor Overexpression In Hepatocellular Carcinoma, Mohammad Shabir Hussain, Sophia Leslie, Amayrani Sanchez, Samantha Lopez, Kyle Doxtater, Manish Kumar Tripathi

Research Symposium

Background: Hepatocellular carcinoma (HCC) constitutes a substantial portion, accounting for 85% to 90% of liver cancers worldwide. Notably, within the Hispanic population, liver cancer mortality rates are notably higher, particularly evident in regions like the South Texas Rio Grande Valley (RGV), where nearly 90% of the populace is Latino/Hispanic. This region grapples with poverty affecting nearly 30% of its residents, coupled with elevated rates of obesity, diabetes, and low-income households, thereby fostering a prevalent environment of stress. Stress can profoundly impact cancer outcomes by compromising immune functionality and triggering inflammatory responses, potentially impairing surveillance against oncogenic triggers. The activation of …

Mmappr2: An Improved Bioinformatics Approach To Find Novel Genes, Aiden Cardall, Jonathon T. Hill, Kyle Johnsen, Connor Ward, Maliha Tasnim, Jared Taylor

Library/Life Sciences Undergraduate Poster Competition 2024

Introduction

• New genes are commonly found by randomly inducing mutations in model organisms.

• Mapping the mutations to the genome to find novel genes is difficult, time-consuming, and expensive.

• We created a bioinformatics program, MMAPPR, to automate this process.

• Here, we introduce a new algorithm, MMAPPR2, which requires little to no bioinformatics knowledge to use.

• MMAPPR2 makes several improvements that allow it to identify genes more rapidly and precisely.

• MMAPPR2 will aid the rapid identification of genes in a wide range of species and developmental systems.

Determining The Role Of Noncoding Insertion And Deletion Mutations In Lung Cancer, Zachary Everton, Matthew H. Bailey

Library/Life Sciences Undergraduate Poster Competition 2024

Background

● Cancer is a disease in which cells grow and divide at an uncontrolled rate and cause damage to surrounding tissue and is caused by mutations in the cells’ DNA.

● Though some cancer-causing mutations are inherited from parents, most cancer-causing mutations emerge over the course of a person’s life and are localized to the tumor. These localized mutations are also known as somatic mutations.

● The human genome is over 6.27 billion base pairs long and cannot be read from end to end; instead it is read in small pieces that are aligned to best-matching sequences in the …

Exploring Neuroplasticity Changes In Neurotoxin-Induced Parkinson’S Disease: A Preliminary Analysis Using Transcranial Magnetic Stimulation, Tomas Gomez Jr., Daniel Salinas, Kelsey Potter-Baker, Nawaz Hack, Ramu Vadukapuram

Research Symposium

Background: Parkinson’s disease (PD) is a neurodegenerative condition that affects movement, cognition, gait, and significantly impacts one's quality of life. Studies have suggested that neurotoxin pre-exposure is related to PD pathology and progressive motor/non-motor deficits, though it remains unclear how neurotoxin exposure affects neuroplasticity. The present study aimed to examine neurotoxin–induced PD-associated neuroplasticity changes in relationship to mental acuity and PD motor functionalities.

Methods: 7 voluntary participants experiencing early-stage PD symptoms with self-reported neurotoxin pre-exposure were enrolled in the longitudinal, repeated-measures clinical study; 2 sex-matched, age-matched, and occupation-matched healthy subjects were recruited for controlled comparative analysis (n=9). UTRGV’s Institute …

Integrative Genomic Insights Into Coral Resilience: Adaptive And Acclimatory Responses To Seasonal Environmental Shifts, Tasnim Ghanim

Theses and Dissertations

Coral reefs, critical to marine biodiversity and coastal protection, face escalating threats from climate change-related phenomena such as ocean warming. This warming is leading to widespread thermal stress that contributes to coral bleaching and infectious disease in corals, leading to the disruption of marine ecosystems and the economies that depend on them. Focusing on the brain coral Platygyra daedalea, known for its thermal resilience in the Persian Arabian Gulf—a region that exemplifies the extreme stressors of climate change—our study aims to dissect the genetic, transcriptomic, and microbiological underpinnings of coral survival in high-temperature environments predicted for the end of …

Investigating The Phytochemicals In Sargassum (Brown Algae) Against The Therapeutic Targets Of Oral Squamous Cell Carcinoma Metastasized From Breast: An Approach, Mahema S

Annual Research Symposium

ABSTRACT:

Purpose: Oral metastases are relatively rare. In women, the most common oral metastases originate from breast cancer, the most diagnosed malignancy, and the second leading cause of death. Sargassum is a genus of brown algae which exhibits its natural therapeutic potential with anticancer properties. This study aimed to identify a therapeutic target for OSCC metastasized from breast cancer through network pharmacology and to evaluate potent phytochemicals against the identified target.

Methods: DEGs associated with OSCC and breast cancer were obtained from the Gene Expression Omnibus. The upregulated DEGs were then intersected to identify common targets between OSCC and BC. …

Interplay Of Periodontal Bacterial Metabolites In The Progression Of Coronary Artery Disease: A System Biological Approach, Janakiraman V

Annual Research Symposium

Purpose:

The purpose of this study is to investigate the intricate relationship between periodontal disease (PD) and coronary artery disease (CAD), as evidenced by epidemiological associations. Metalloproteinase inhibitor (TIMP1) plays a pivotal role in cellular signaling, differentiation, cell death, and migration by binding to target metalloproteinases, forming complexes with other molecules (collagenases) to inactivate them. However, the expression of TIMP1 is reduced in both PD and CAD, leading to an upregulation of other metalloproteinases. This research explores the hypothesis that metabolites released from (Porphyromonas gingivalis), a prevalent bacterium in atherosclerotic patients, may inhibit TIMP1, thereby influencing CAD progression. …

A Reliable Diabetic Retinopathy Grading Via Transfer Learning And Ensemble Learning With Quadratic Weighted Kappa Metric, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei

Computer Science Faculty Publications

The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy …

Protein-Protein Interactions In Cell Cycle Proteins: An In Silico Investigation Of Two Important Players, Andriele Eichner

Dissertations, Theses, and Capstone Projects

The examination of the cell cycle carries significant implications for the biology, health, and overall existence of all living things. These implications span from the development and growth of these organisms to the aging process and cancer, as well as the potential of stem cell therapies to repair diseases and injuries. Numerous proteins of the cell cycle are essential for cellular division and proliferation and are widely conserved over the course of evolution. In this work, we aimed to investigate the molecular processes of protein-protein interactions in cell cycle proteins, centering on two key players: Cdc6 in budding yeast and …

Toward Improved Global Food Security: Uncovering How Tomatoes Fight Root-Knot Nematodes, Chingyan H. Huang

The Journal of Purdue Undergraduate Research

No abstract provided.

Exploring The Evolution Of Callose Synthase In Green Plants, Giovanna Durante

The Journal of Purdue Undergraduate Research

No abstract provided.

Elastin-Like Polypeptide As A Model To Study Intrinsically Disordered Proteins, Sadegh Majdi

Theses and Dissertations

The elastin-like polypeptide (ELP) is a well-studied structural protein that is easily amenable to amino acid (AA) sequence modifications and has the potential to yield a wide variety of uses in bioengineering and biomedical applications. One unique property of ELP is the inclusion of intrinsically disordered domains (IDP) within the structure that allow for its diversity of physical properties. While it is generally understood that amino acid sequence dictates protein folding arrangements, the contributions of specific amino acid sequences to the intrinsic disorder of ELP has yet to be fully resolved. Therefore, identifying the contributions of specific amino acid sequences …

Integrated Rules Classifier For Predicting Pathogenic Non-Synonymous Single Nucleotide Variants In Human, Ahmed Barakat Hosseny, Marwa Said Hassan, A A. Shalan, Shymaa Khamis, M I. Dessouky

Basic Science Engineering

The most prevalent kind of genetic variants in humans are non-synonymous single nucleotide variants (nsSNVs). Several prediction tools have been launched to forecast the effect of amino acid substitutes on human protein function. These tools sort variants as pathogenic or neutral. We developed an Integrated Rules Classifier (Integration Score through JRip “ISTJRip”), which integrates the four individual tools that are publicly available; iFish, Mutation Assessor, FATHMM, and SIFT-based on the JRip machine learning technique. Additionally, we compared the ISTJRip approach with the other three created integration classifiers; Integration Score through J48 “ISTJ48”, Integration Score through RF “ISTRF”, and Integration …

A New Paradigm Of Life Science Research Driven By Artificial Intelligence, Xin Li, Hanchao Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid development of biotechnology and information technology has brought life sciences into a new era of data explosion. The traditional life science research paradigm struggles to reveal the fundamental rules of complex biological systems from rapidly growing biological big data. As artificial intelligence continues to achieve disruptive breakthroughs in life science, a new paradigm driven by AI is emerging. This study delves into typical examples of life science research driven by AI, proposes the concept and key elements of the new life science research paradigm, elaborates on the cutting-edge of life science research under this new paradigm, and discusses …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

Whole Genome Sequencing For The Millipede Cherokia Georgiana, Elena Cruz, Will Wittstock, Daniel Hastings, Arnab Sengupta, Bruce A. Snyder

Graduate Research Showcase

Out of thousands of known millipede species, only five sequenced genomes of species (in four of sixteen orders) are publicly available. No whole genomes and limited genetic information are available for incredibly diverse families such as Xystodesmidae. Our research goal is to sequence the whole genome of the millipede Cherokia georgiana. A de novo sequence of the complete genome of a North American species will facilitate future research in understanding gene expression under a variety of conditions. Many interesting biological processes in millipedes are poorly described, such as the production of a defensive hydrogen cyanide secretion found in the …

Using Single Cell Genomics To Explore The Impact Of Marine Viruses On Microbial Respiration., Paxton Tomko

MCB Articles

Viral metabolic reprograming of marine prokaryotes, through the use of virally encoded auxiliary metabolic genes (AMGs), plays a critical role in marine ecosystem function by influencing biochemical cycles and genetic diversity in these environments. Despite the fundamental role viruses play in global environmental ecosystems, they remain an understudied aspect of microbial ecology and evolution, in part due to the methods available for studying virus host interactions in natural systems. Thus far, metagenomic analyses have been used to study the interactions of virus host pairs, but these types of analyses have their limitations in accurately linking viruses to hosts, or culture-based …

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Computational Exploration: Docking Analysis Of Compounds From Foeniculum Vulgare As Potential Aromatase Inhibitors For Endometriosis Candidate Therapy, Dwi Anita Suryandari, Puji Sari, Hadi Sunaryo, Khaerunissa Anbar Istiadi

Indonesian Journal of Medical Chemistry and Bioinformatics

Aromatase inhibitors (AI) have controlling symptoms and size of endometriotic implants, making them a promising second-line therapy for endometriosis treatment.pretreatment with letrozole, an AI, combined with leuprolide acetate and resveratrol has been found to improve in vitro fertilization (IVF) outcomes in women mild endometriosis.in this study we screening and analysis of ten phenolic compounds from Foeniculum vulgare using molecular docking with Mcole server.from this results showed that three phenolic trans resveratrol (TR), caempherol coumaril (CC) have low gibbs energy compare with resveratrol (R). The binding modalities of compound TR and compound R were hydrogen-bonding between the hydroxyl and oxygen atom …

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Using In Silico Tools To Analyze The 5ʹ Untranslated Regions Of The Alcohol Dehydrogenase Gene From Arabidopsis Thaliana And Omega Sequence, Reza Mohammadhassan, Tina Asadishad

Makara Journal of Science

The 5ʹ ends of protein-encoding genes contain 5ʹ untranslated regions (5ʹUTRs), which can effectively participate in regulating gene expression. The 5ʹUTRs of Arabidospis thaliana–derived alcohol dehydrogenase gene (AtADH) and omega (Ω) sequence from tobacco mosaic virus (TMV) are the most effective enhancers in biotechnology. In this study, bioinformatics techniques were employed to analyze the characteristics of the above sequences. After 5ʹUTR sequence collection, the inner ribosome entrance sites; small RNA (sRNA) target sequences; nucleotide contents; and upstream start and stop codons, ORFs, and coding DNA sequences of the Ω sequence and AtADH 5ʹUTR were identified. Moreover, the …

Investigating The Impact Of Transcription On Mutation Rates, Sarah Patterson

Theses and Dissertations

tRNA genes are highly transcribed and perform one of the most fundamental cellular functions. Although a universal pattern observed across all three domains of life is that highly transcribed genes tend to evolve slowly, tRNA genes have been shown previously to evolve rapidly. This rapid sequence evolution could result from relaxed selection, increased mutation rate, or a combination of both. Here, we use mutation-accumulation line sequencing data to show that tRNA genes accumulate more mutations than other gene types. Our results indicate that this elevated mutation rate is a consequence of both elevated transcription-associated mutagenesis and a lack of transcription-coupled …

Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2024–

The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …