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Table of Contents

1.0 INTRODUCTION.. 3

1.1 Background of Acute Lymphoblastic Leukaemia (ALL) 3

1.1.1 Epidemiology. 4

1.1.2 Pathogenesis. 5

1.1.3 Clinical Features. 5

1.1.4 Diagnosis & Classification. 6

1.1.5 Prognosis & Need for New Therapies. 6

1.2 Antimicrobial Peptides & Their Role in Cancer Therapy. 7

1.2.1 Definition & Classification of AMPs. 7

1.2.2 Functions of AMPs. 8

1.2.3 Anticancer Effects of AMPs. 8

1.2.4 AMPs in Cancer Therapy. 9

1.2.5 Potential Application of AMPs in Haematological Malignancies. 10

1.3 Research Aim and Objectives. 11

CHAPTER 2: LITERATURE REVIEW… 13

2.1 Gene Expression Profiling in Cancer Research. 13

2.2 Differential Gene Expression in Acute Lymphoblastic Leukaemia. 15

2.3 Previous Research on Antimicrobial Peptides in Cancer Therapy. 17

2.4 Gaps in the Literature and Justification for This Study. 19

3.1 Experimental Design. 21

3.2 Differential Gene Expression Analysis. 22

3.2.2 Identifying Differentially Expressed Genes. 23

3.2.3 Selection of Key Genes. 23

3.2.4 Visualization of Differential Gene Expression. 23

3.3 Functional Analysis of Key Genes. 24

3.4 Expected Findings. 25

References. 26

1.0 INTRODUCTION

1.1 Background of Acute Lymphoblastic Leukaemia (ALL)

ALL is a malignant disorder of the hematopoietic system characterized by the uncontrolled proliferation of immature lymphoid cells in the bone marrow and peripheral blood (Malard, and Mohty, 2020). As the most common childhood malignancy, ALL accounts for approximately 25% of all pediatric cancers and is a leading cause of cancer-related deaths in children (Greaves, 2018). Although significant advancements in treatment have improved survival rates, challenges remain, particularly in adult patients and those with high-risk genetic subtypes. The necessity for novel therapeutic strategies, including bioactive peptides, has gained considerable attention in recent years.

1.1.1 Epidemiology

ALL is relatively rare but accounts for the most common type of leukemia in children with peak incidence between 2 and 5 years of age. The disease is less common in adults and incidence declines in late adolescence with subsequent increase in older populations. The incidence of ALL is approximately 1–5 cases per 100,000 individuals per year globally, and in developed countries, and resource-rich settings (Greaves, 2018). Every year, around 800 new cases of ALL are diagnosed in the United Kingdom, with around 60% in children (National Cancer Institute, 2025). Not surprisingly, the survival rate is highly influenced by age of presentation; children have an overall 5-year survival of>90% and adult rates drop to 30–40% in older patients (National Cancer Institute, 2025). There have been gender disparities, with males diagnosed with ALL more often than females, and a ratio of approximately 1.3:1, males diagnosed to females. There are also ethnic variations, with Hispanics and whites having a higher incidence compared to African and Asian people. Although mortality rates have decreased in the past few decades with the advances in chemotherapy and targeted therapy, relapsed and refractory ALL remains a major clinical challenge.

1.1.2 Pathogenesis

Genetic and chromosomal abnormalities that interfere with normal lymphoid differentiation and proliferation are the primary forces in the pathogenesis of ALL. Several key mutations and translocations have been defined which result in leukemic transformation. Among the most well-known genetic abnormalities is the Philadelphia chromosome (t[9;22]) with the BCR-ABL1 fusion gene, resulting in the constitutively active tyrosine kinase which leads to uncontrolled cellular proliferation (Terwilliger, and Abdul-Hay, 2017). Other such common chromosomal rearrangements include t(12;21) ETV6-RUNX1, t(4;11) MLL-AF4t(1;19) TCF3-PBX1, which all disrupt transcriptional regulation and oncogenic signaling pathways (Kaczmarska, et al., 2023). Hematopoietic progenitor cells, which contain mutations that grant them the ability to reproduce themselves abnormally, are believed to originate as leukemic stem cells (LSCs). These LSCs set up a reservoir of malignant cells that participate in disease progression and treatment resistance. Transformed cells clonally expand and accumulate large numbers of immature lymphoblasts in the bone marrow, causing suppression of normal hematopoiesis (pancytopenia). In addition, ALL pathogenesis has also been linked to the role of the bone marrow microenvironment, with stromal cells and cytokines implicated in that process.

1.1.3 Clinical Features

Bone marrow failure and leukemic extramedullary involvement are the primary reasons for the clinical presentation of ALL. According to Lennmyr, et al., (2019), Anemia and increased metabolic demand of proliferating leukemic cells lead to nonspecific symptoms in patients, these being fatigue, fever, and pallor. Due to the increased susceptibility to recurrent infections caused by neutropenia and impaired immune function, oro-analswellableare common, as are bleeding and easy bruising, which are a result of thrombocytopenia (Lennmyr, et al., 2019). Hepatosplenomegaly, lymphadenopathy, and bone pain, especially in children, may present due to leukemic cell infiltration in various organs, like hepatosplenomegaly and lymphadenopathy, and bone pain. About 5% of newly diagnosed ALL cases involve CNS involvement with symptoms including headaches, seizures, and cranial nerve palsies. All are clinically reasonable because T cell ALL (T-ALL) is more aggressive and correlated with a high white blood cell count, a mediastinal mass, and CNS involvement (Deak, et al., 2021). However, B-cell ALL (B-ALL) is more frequent and may be associated with a more favorable prognosis, especially in children.

1.1.4 Diagnosis & Classification

Accurate diagnosis and classification of ALL are important for determining risk and the treatment plan. All fall under the World Health Organization (WHO) classification scheme based on immunophenotypic, cytogenetic, and molecular features. Immunophenotyping using flow cytometry to determine the difference between B cell and T cell ALL: CD19, CD20 (B ALL) and CD3, CD7 (T ALL) (DiGiuseppe, and Wood, 2019). Identification of chromosomal abnormalities associated with ALL is made possible by cytogenetic analysis. In most cases, BCR-ABL1 is detected by fluorescence in situ hybridization (FISH) and polymerase chain reaction (PCR) techniques (DiGiuseppe, and Wood, 2019). Further advancement has now come through next-generation sequencing (NGS) which has been able to further identify the mutations that indicate resistance to treatment (Miller, and Kovach, 2024). The use of minimal residual disease (MRD) monitoring, by PCR or by flow cytometry, has become an essential prognostic factor to drive intervention intensity.

1.1.5 Prognosis & Need for New Therapies

The prognosis of patients remains extremely variable: despite improvements in therapy. Although pediatric patients are now achieving survival rates above 90%, adult patients, most notably those older than 60 years of age, still do not have good outcomes with five-year survival rates < 40% (Harrison, & Johansson, 2015). One of the main challenges is for disease relapse to continue, and many MRD-positive patients have an increased risk of treatment failure. Chemotherapy and targeted therapies lead to resistance that makes their management further challenging, demanding therapeutic strategies. This novel approach to treating ALL is the exploration of bioactive peptides, such as QUB-2392, which may treat ALL (Purohit, et al., 2024). The antimicrobial and anticancer properties of these peptides have been shown and these could include mechanisms of action by membrane disruption and inducing apoptosis and immune system modulation. Since conventional therapies are limited, studying how QUB-2392 changes gene expression in ALL cells may help explain why the drug is effective and how it works (Purohit, et al., 2024).

1.2 Antimicrobial Peptides & Their Role in Cancer Therapy

Naturally occurring short proteins with antimicrobial, antiviral, and anticancer properties are antimicrobial peptides (AMPs). Many bacteria, plants, and animals have these peptides as their first line of defense against infections. As part of becoming cancer therapies, AMPs have been shown beyond their antimicrobial functions to selectively target and kill cancer cells through several mechanisms, thereby drawing tremendous attention (Tornesello, et al., 2020). This has harnessed growing research on the potential use of AMPs in hematological malignancies, including the treatment of ALL.

1.2.1 Definition & Classification of AMPs

Amphipathic structures, small, usually 10–50 amino acids, and antimicrobial peptides. Usually, they carry a net positive charge that allows them to bind to the negatively charged membranes of bacteria and cancer cells but not normal mammalian cells (Tornesello, et al., 2020). There are three main categories in which AMPs are classified depending on their structure, α-helical peptides, β-sheet peptides, and looped peptides. In solution α-helical peptides including LL 37 and magainins are unstructured, but become helical in association with a lipid membrane (Zhang, et al., 2019). Defensins, for example, are β sheet peptides stabilized by disulfide bonds, which form rigid structures that cling to microbial, as well as a cancer cell, membranes; their amphipath nature allows them to disrupt membranes in target cells (Deslouches, and Di, 2017). Bactenecins are looped peptides that have intra-molecular bridges that provide structural stability and serve to interact with a wide range of cellular targets. Diverse functions and mechanisms of AMPs are made possible by these various structural variations.

1.2.2 Functions of AMPs

Antimicrobial peptides have broad-spectrum activity against microbes including bacteria, fungi, and viruses. To achieve this, they disrupt the microbial membranes directly, intracellular targets or modulate the host immune response (Deslouches, and Di, 2017). Immunomodulatory effects, including activation of immune cells and modulator of inflammation, are also seen in some AMPs, e.g. defensins. Furthermore, AMPs possess high anticancer activity. The reason for such selective activity of AMPs is that normal cells have a different membrane composition than their malignant counterparts. Cancer cells, however, unlike healthy cells, are characterized by an increased negative charge owing to the phosphatidylserine and sialylated glycoproteins that they bear, yielding a phospholipid bilayer that is negatively charged (Tornesello, et al., 2020). Because of this characteristic, cancer cells are more susceptible to cationic AMPs, which means that the peptides bind selectively and possess cytotoxic effects.

1.2.3 Anticancer Effects of AMPs

Several different mechanisms of AMPs induce anticancer properties including induction of apoptosis, disruption of cancer cell membrane, and modulation of the immune system (Tornesello, et al., 2020). It is well known that one of the major ways to kill cancer cells with AMPs is by apoptosis mediated through the mitochondrial pathways. Lactoferricin B and certain other AMPs are known to trigger mitochondrial membrane permeabilization with the release of cytochrome c and caspase-dependent apoptotic pathways (Baxter,  et al., 2017). These cause the death of cancer cells in a programmed fashion, lowering populations of cancer cells. However, direct membrane disruption is another important means of action. Melittin, a bee venom-extracted peptide, kills cancer cells by interacting with the lipid bilayers of these cells thereby forming pores that cause cell lysis (Rady, et al., 2017). Specifically, this mechanism has a tremendous advantage in leukemias, where membrane compositions are modified by rapid proliferation, leading to altered membrane fluidity and charge, a hallmark of many cancers. AMPs have direct cytotoxic effects, as well as the ability to modulate the immune system to improve anticancer effects. Natural killer (NK) cells, dendritic cells, and macrophages are key in the recognition of and killing of cancer cells, which can be stimulated by certain peptides such as defensins and cathelicidins (Ahluwalia, et al., 2021). In turn, AMPs have a role in enhancing immune surveillance, which would enhance an antitumor response.

1.2.4 AMPs in Cancer Therapy

Promising anticancer effects of several AMPs have been shown, especially in hematological malignancies. Caspase activation by a peptide from bovine lactoferrin, lactoferricin B, and cleavage of mitochondrial proteins in supported liposomes results in the induction of apoptosis in leukemia cells (Baxter, et al., 2017). This peptide is shown to selectively target cancer cells, without harming normal hematopoietic cells, and it is thus an attractive candidate for therapeutic development. A group of these β-sheet AMPs known as defensins inhibit tumor growth by disrupting cancer cell membranes and modulating immune responses. For example, human beta-defensin 3 (hBD-3) is known to induce apoptosis in leukemia cells and augment cytotoxicity T lymphocyte activity (Zhang, et al., 2021). Like cathelicidins, LL-37 has strong anticancer properties by inducing inflammation and directing immune cells to the tumor microenvironment.

The principal component of bee venom is well-characterized AMP, melittin. Anticancer Effect of Melittin: The anticancer effect of melittin is due to the formation of pores in the plasma membrane of cancer cells, thus causing rapid necrosis. Melittin has also proven to be efficacious in leukemia models in reducing tumor burden and increasing chemosensitivity (Zhang, et al., 2021). However, its use in clinical application is restricted through potential toxicity at high doses, and therefore successful melittin-based drug formulations and targeted delivery systems need to be developed.

1.2.5 Potential Application of AMPs in Haematological Malignancies

AMPs offer great promise as a treatment for hematological malignancies, particularly ALL, given their unique mechanisms of action. According to Dong, et al., (2024), AMPs are capable of bypassing the limitations of traditional chemotherapy and directly hitting cancer cells through other routes. Unlike traditional cytotoxic agents, such as genetic mutations of cancer cells, cemented by AMPs on the physical structure of the cell, the membrane, and mitochondria, resistance was reduced. Consequently, mechanistic insights arising from prior studies indicate that AMPs can be utilized either as standalone therapies or in adjunct with existing therapeutics to enhance efficacy. As a result, lactoferricin B has been demonstrated to sensitize leukemia cells by chemotherapy, while defensins increase the clearance of tumors through immune mechanisms (El-Kazzaz,  and Abou El-khier, 2015). In addition, AMPs can be engineered to increase specificity and diminish off-target effects for clinical translation.

This is of particular interest in terms of the novel bioactive peptide, QUB-2392. QUB-2392 is shown to possess anti-leukemic effects and preliminary studies indicate that it does so through modulation of gene expression in ALL cells (Suarez-Jimenez, et al., 2012). Little is known about the mechanisms by which this agent is acting, but compiled data suggest it might be affecting leukemia cell apoptosis-related genes, cell membranes, or signaling pathways including cell proliferation and survival. A study of the molecular effects of QUB-2392 through gene expression profiling could potentially shed light on its therapeutic potential as well as facilitate translation to peptide-based therapy for ALL (Suarez-Jimenez, et al., 2012). As the interest in AMP-based cancer therapy continues to grow further research is warranted for optimization of efficacy, minimization of toxicity, and delivery strategies to increase bioavailability. Using bioinformatics tools like STRING, KEGG and GO enrichment analysis can help in deciphering molecular pathways that are impaired by AMPs to get a clearer picture of how they function in cancer treatment.

1.3 Research Aim and Objectives

Acute lymphoblastic leukemia (ALL) continues to be a major clinical challenge, especially where relapse and treatment resistance are the issues. As more and more alternative mechanisms to target leukemic cells through novel therapeutic agents, such as bioactive peptides, are being explored, they are increasingly becoming topics of interest. A novel antimicrobial peptide QUB-2392 has shown promise as an anti-leukaemic agent. Nevertheless, the mechanisms by which it exerts its effects are unknown on the molecular level. The objective of this study is to gain knowledge, in Jurkat cells, a widely adopted T cell leukemia model, on the effect of QUB-2392 on gene expression. The primary research question guiding this investigation is:

RQ1: How does QUB-2392 affect gene expression in Jurkat cells?

The central hypothesis is:

H1: QUB-2392 exerts its anti-leukemic effects by modulating key oncogenes and tumor suppressor genes, thereby disrupting critical molecular pathways involved in ALL pathogenesis

To address this hypothesis, the study has the following objectives:

1. Identify differentially expressed genes following QUB-2392 treatment, distinguishing between upregulated and downregulated genes.
2. To determine the biological relevance of these changes, perform functional gene analysis using bioinformatics tools such as STRING, KEGG, and Gene Ontology (GO) enrichment analysis.

• Investigate the molecular pathways affected by QUB-2392, focusing on apoptosis, cell cycle regulation, and leukemic cell proliferation.

By elucidating the genetic and molecular basis of QUB-2392’s effects, this research aims to provide valuable insights into its therapeutic potential in ALL treatments and pave the way for developing novel peptide-based therapies.

CHAPTER 2: LITERATURE REVIEW

2.1 Gene Expression Profiling in Cancer Research

Gene expression profiling is the most common way of querying the mechanisms and perhaps the targets of cancer treatment in cancer research. Initial transcriptomic analysis gives a global overview of the gene expressions in cancer cells and genes responsible for tumor progression, resistance to therapies, and responses to therapies can be identified (Hanahan & Weinberg, 2011). On the other hand, researchers can get to know which genes are up or downregulated after the therapy, in which drug mechanisms it takes place, and which new biomarkers are going to help the prognosis and even the target therapy (Tomczak et al., 2015). Such is the case particularly when oncoprotein and TSG dysregulation are critical for disease course and/or response to therapy, such as in hematological malignancies like ALL (Zhou et al., 2019).

Such developments in transcriptomics have greatly enhanced the art of gene expression analysis. RNA sequencing (RNA-seq) is now the gold standard as a sensitive, accurate, and highly useful method of transcriptome profiling (Wang et al., 2009). This next-generation sequencing (NGS) technique counts amounts of expressed genes from complementary DNA (cDNA) of the RNA molecules in the cancer cells treated with therapeutic agents (Conesa et al., 2016). RNA-seq can be studied to get insights into the transcriptional landscape such as alternative splicing, fusion genes, and non-coding RNAs involved in oncogenesis (Shen et al., 2012). Microarray technology is another widely used method in cancer research in which the gene expression is analyzed. Shi et al. (2016) describe a technique of hybridizing fluorescently labeled cDNA to a solid surface that allows one to simultaneously measure dozens of thousands of gene expression levels. While microarrays have been extensively used in early transcriptomic endeavors, they still lack sensitivity and cannot detect new transcripts as compared to RNA sequences (Zhao et al. 2014). However, microarrays are very useful for comparing the expression of genes between samples and are very useful for clinical-scale applications (due to cost) where high-scale clinical experiments tend to be expensive.

RNA-seq and microarrays have been used głow´s deí ciali ktatsian epnts hayer dialerum hayer dialerum bogov ¡eylama bkomadersrcan atheniatsionlumo arovmntilizeli tereksn avum hayer dialerum began Eli atanegarum (Kukurba & Montgomery, 2015.). In the context of ALL, these technologies have permitted the discovery of molecular subtypes that are suitable for personalized therapy orientation from the genetic profile (Gu et al., 2019). Among other things, they have been important in understanding mechanisms of action of novel, therapeutic agents such as QUB-2392 and antimicrobial peptides, with potential anti-leukemic properties due to modulation of key oncogenes and tumor suppressor genes through transcription. Nevertheless, further work will continue to add value in forward cancer therapeutics and to predict patient response with transcriptomic analyses.

2.2 Differential Gene Expression in Acute Lymphoblastic Leukaemia

Widespread genetic and epigenetic alterations are characteristic of ALL, which control the malignant transformation and disease progression of this disorder. Gene expression analysis has been utilized to identify and validate oncogenes and tumor suppressor genes associated with ALL. Several genes are recurrently deregulated in ALL leading to uncontrolled proliferation, impaired differentiation, and resistance to apoptosis (Inaba et al., 2013). Among the most frequently overactivated oncogenes in ALL, the oncogenes BCR-ABL1, CRLF2, and MYC contribute to the survival and proliferation of leukemic cells (Roberts & Mullighan, 2015). More specifically, loss of cell cycle control and genomic instability (Meyer et al. 2013) are attained by downregulation and/or inactivation of tumor suppressor genes such as CDKN2A/B and TP53.

Ranked among BCR gene and drive 😉 Transformation; especially in Philadelphia chromosome-positive (Ph+) ALL is associated with an aggressive disease progression and poor prognosis (Zhou et al., 2019). Additionally, CRLF2 overexpression has been associated with high-risk ALL, especially in patients with Down syndrome and Hispanic ethnicity (Mullighan, 2012). Deregulated signaling of these oncogenes initiates aberrant pathways including JAK-STAT, PI3K-AKT, and RAS-MAPK, and sustains leukemic cell growth and drug resistance (Ma et al., 2015). In T-ALL (T cell ALL) in particular, MYC dysregulation is also important to both promote metabolic reprogramming and proliferation of malignant blasts (Sanda et al., 2012). In contrast, tumor suppressor genes such as CDKN2A/B and TP53 are frequently deleted or epigenetically silenced in ALL. The CDKN2A/B locus encodes the tumor suppressors p16INK4A and p14ARF, which regulate the cell cycle by inhibiting cyclin-dependent kinases and stabilizing p53, respectively (Ferrando et al., 2002). Loss of these tumor suppressors accelerates leukemogenesis by disrupting normal cell cycle checkpoints and enabling unchecked proliferation. TP53 mutations, though less common in pediatric ALL, are frequently observed in relapsed and refractory cases, highlighting their role in treatment resistance and disease progression (Hof et al., 2011).

Differential gene expression profiling has also provided valuable prognostic insights in ALL. Specific gene expression signatures can predict treatment response and stratify patients into distinct risk categories (Zaliova et al., 2013). For instance, high expression of IKZF1, a transcription factor critical for lymphoid differentiation, is associated with favorable prognosis in B-cell ALL, whereas IKZF1 deletions are linked to poor outcomes and resistance to tyrosine kinase inhibitors (Mullighan et al., 2009). Similarly, the downregulation of apoptosis-related genes such as BAX and BIM has been associated with chemotherapy resistance, as these genes play essential roles in programmed cell death (Tzifi et al., 2012). The ability to profile differentially expressed genes in ALL has significantly improved risk assessment and therapeutic decision-making. Transcriptomic analyses have facilitated the development of targeted therapies, such as tyrosine kinase inhibitors for Ph+ ALL and monoclonal antibodies for specific genetic subtypes (Pui et al., 2011). Furthermore, novel agents like bioactive peptides, including QUB-2392, may exert their anti-leukemic effects by modulating key oncogenes and tumor suppressor genes. Understanding the transcriptional changes induced by these therapies will be essential in advancing precision medicine for ALL.

2.3 Previous Research on Antimicrobial Peptides in Cancer Therapy

Antimicrobial peptides (AMPs) have become promising candidates for cancer therapy because of their potential selective targeting of malignant cells with little effect on normal tissues. AMPs have been shown in several studies to be effective in the inhibition of tumor growth via mechanisms of membrane disruption, apoptosis induction, and immune modulatory effects (Hancock et al., 2016). This contrasts with conventional chemotherapeutic agents, having systemic toxicity and inducing resistance, which AMPs exploit by utilizing the cell membrane composition difference. Cationic AMPs interact electrostatically and cause cytotoxic effects on malignant cells with increased membrane fluidity and/or anionic phospholipid composition (Hoskin & Ramamoorthy, 2008).

AMPs have been studied for their capacity to induce apoptosis using mitochondrial pathways. Lactoferricin B, the lactoferrin derivative, has also been shown to affect apoptosis in a variety of cancer cell lines, including leukemic cells and others, also through the generation of reactive oxygen species and the disruption of mitochondrial membranes (Mader & Hoskin, 2013). Defensins and cathelicidins, naturally occurring AMPs, have also proven to have potent anti-tumor effects. For instance, the increased immunogenicity and destruction of tumor vasculature have been attributed to the effect of human β-defensin-3 (hBD-3) on tumor progression (Wang et al., 2019). The cytotoxic effect of another well-studied AMP, melanin, from bee venom, is exerted on leukemic cells by direct insertion into the lipid bilayer and pore formation by forming pores in the cell leading to osmotic imbalance and cell death (Oršolić, 2012).

The potential advantages of AMPs have been shown by comparative analyses of AMP-based therapies compared to conventional chemotherapy. Chemotherapeutic agents like doxorubicin and vincristine have as their intended target rapidly dividing cells, which results in many side effects including bone marrow suppression, cardiotoxicity, and multi-drug resistance (Holohan et al., 2013). On the other hand, AMPs have selective cytotoxicity towards tumor cells, but not normal cells, mainly for their selective interaction with a cancer cell membrane. In addition, there have been demonstrated synergistic effects between AMPs and chemotherapy when used in combination therapies. AMPs have been assertively shown to sensitize cancer cells to conventional drugs through modulation of apoptotic pathways and their interference with drug resistance mechanisms (Felício et al., 2017).

However, AMPs are still in the early stages of their clinical application for cancer therapy. If these challenges may be overcome, they will be widely applied. Several promising strategies to overcome these limitations have been realized with the advances in peptide engineering, such as the development of synthetic analogs with enhanced stability and bioavailability (Ladokhin & White, 2017). Future research into AMPs in a targeted therapy regimen particularly for hematological malignancies such as acute lymphoblastic leukemia, should be considered as promising. Globally, much evidence now suggests that AMPs might be viable alternatives or adjuncts in treatments of cancer, with better results in treatment outcomes and better tolerance of toxicity than conventional chemotherapy.

2.4 Gaps in the Literature and Justification for This Study

Acute lymphoblastic leukemia (ALL) is still difficult to treat with current therapeutic approaches as they are associated with a high relapse rate and drug resistance. Although these conventional treatments, including chemotherapy and targeted therapies, have increased survival, they have been accompanied by severe side effects and therapy-induced resistance (Inaba et al., 2013). Because antimicrobial peptides (AMPs) can preferentially target malignant cells with minimal toxicity to normal cells, AMPs have been considered as prospective anti-cancer agents. Nevertheless, most AMP research has tended to invest solely in well-defined AMPs like lactoferricin B, defensins, and melittin (Wang et al., 2019). QUB-2392 is a bioactive peptide that has been identified as an unexplored but as yet, promising candidate for the treatment of ALL and requires further investigation into its mode of action.

Studies of gene expression of cells treated with QUB-2392 that appear in the current literature fail to provide sufficient examples elucidating changes in gene expression contributions of QUB-2392 in leukemic cells. In a previous study of AMPs’ effect on cancer cell membranes, AMPs were shown to disrupt cancer cell membranes and induce cancer cell apoptosis via mitochondria pathways (Felício et al., 2017). However, how QUB-2392 impacts the general oncogenic and tumor suppressor network is not known. The ability to profile the gene expression yielded from QUB−2392 treatment would be highly indicative of molecular pathways influenced by QUB−2392 and to new target(s) of therapy. The therapeutic potential of this peptide is speculative due to the lack of such data.

Additionally, several AMPs have been shown to cure solid tumors, but their role in hematological malignancy is not well characterized (Hancock et al., 2016). Human β-defensin-3 is effective as an anti-leukemic agent in leukemia models, although its exact mechanism of action is yet to be determined (Mader & Hoskin, 2013). Studies comparing AMP-based treatments to conventional chemotherapy are lacking, making comparison to their long-term efficacy difficult. This work demonstrates the integration of AMP-based therapies as part of standard treatment regimens would require knowledge of how AMP-based therapies interact with current drugs, search for possible synergistic effects, and possible resistance mechanisms. This study aims to fill in some of the gaps in current AMP research, which are highlighted in Table 1.

Table 1: Identified Research Gaps and Contributions of This Study

The finding of this study will add to the increasing corpus of knowledge on AMPs as anticancer agents but with a special emphasis on hematological malignancies. Knowing how QUB-2392 affects gene expression in Jurkat cells will be essential for understanding how it can be used therapeutically and its mechanisms of action. This research also helps in identifying the key oncogenes and tumor suppressor genes to which the peptide modulates, which can serve as aids in further preclinical and, potentially, clinical investigations. As an unusual example, QUB-2392, a highly promising chemical entity currently under clinical investigation, could be of interest to investigate the role of this compound in ALL, potentially as the means for developing new, better treatments. This will not only help shed light on the role of AMP in cancer therapy mediated by this therapeutic approach but also provide important insights for the search for other approaches to fighting drug-resistant leukemia.

CHAPTER 3 METHODOLOGY

3.1 Experimental Design

The experimental design was geared up to determine the effect of the bioactive peptide QUB-2392 on gene expression in Jurkat cells, an established malignant acute lymphoblastic leukemia (ALL) cell line. The goal of the study was to elucidate the mechanisms by which the peptide exerts its anti-leukemic effect and to identify differentially expressed genes. Under standard conditions, Jurkat cells were grown in RPMI1640 medium containing 10% fetal bovine serum (FBS) and penicillin-streptomycin to keep cells alive and contaminant-free (Sundström & Nilsson, 1976). Untreated cells were used as controls and they were treated with QUB-2392 at 9 µM for 24 hours. Cells were treated, harvested and RNA preserved in RNAlater solution for the integrity of the sample before sequencing (Chomczynski & Sacchi, 2006).

Novogene, as a commercial genomics service provider, performed RNA sequencing using the NovaSeq 6000 sequencing platform. Poly A mRNA enrichment, cDNA library construction, and high throughput sequencing were applied to the sequencing process and were paired-end strategies with high coverage and accuracy of gene expression data (Wang et al., 2009). However the raw sequencing data from which that came gave the outset for differential gene expression analysis of genes, upregulated or downregulated by QUB-2392 treatment. This allowed for a complete analysis of the potential mechanisms of action of the peptide in leukemic cells.

3.2 Differential Gene Expression Analysis

Differential gene expression analysis is a crucial step in understanding the molecular effects of QUB-2392 on Jurkat cells. The analysis involves preprocessing, statistical evaluation, and visualization to determine which genes are significantly upregulated or downregulated in response to treatment.

3.2.1 Preprocessing the Data

The preprocessing stage is where the raw RNA sequencing (RNA-seq) data will be normalized so that we can compare different treated with their respective control groups. Normalization compensates for such variations in sequencing depth and variation in gene length so that changes in expression can be assessed unbiasedly. For instance, the DESeq2 and edgeR software packages are widely used for each normalization since median ratio normalization is effective for RNA-seq count data and because the trimmed mean of M values (TMM) method is used. Additionally, these low-expression genes can be filtered, for example, for a minimum fragment per kilobase of transcript per million mapped reads (FPKM) >1.

3.2.2 Identifying Differentially Expressed Genes

The identification of differentially expressed genes (DEGs) normally needs to rely upon statistical thresholds (log2 fold change, false discovery rate (FDR), etc.). A differentially expressed gene is defined as a gene with an absolute log2 fold change ≥ 1 (i.e. at least 2 fold difference in expression) and padj ≤ 0.05. These values are estimated by negative binominal statistical modeling as performed in DESeq2, which recognizes biological variation with as few false positives as possible.

3.2.3 Selection of Key Genes

To further investigate the impact of QUB-2392, five upregulated and five downregulated genes are selected based on their biological significance and involvement in key oncogenic or tumor suppressor pathways. Selection criteria include:

1. High fold-change values.
2. Statistical significance (low FDR values).
3. Association with leukemia-related pathways, such as apoptosis, cell cycle regulation, and immune response​.

3.2.4 Visualization of Differential Gene Expression

To efficiently interpret the gene expression changes, such visualization methods as volcano plots, heatmaps, and hierarchical clustering are used. This is useful because the volcano plot shows the overall distribution of the differentially expressed genes. The above plot has the x-axis as log2 fold change and the y-axis as -log10 adjusted p value (padj). Red genes are significantly upregulated, and green genes are significantly downregulated. To visualize the key DEGs in Jurkat_24 vs. Jurkat_0, the volcano plot of Jurkat_24 vs. Jurkat_0 dataset is presented as the snapshot of the most significant changes brought by QUB-2392 treatment. This study systematically analyzes and visualizes these differential gene expression patterns and it attempts to elucidate the molecular mechanisms whereby QUB-2392 exerts its anti-leukeamic effect in Jurkat cells.

3.3 Functional Analysis of Key Genes

Differentially expressed genes (DEGs) generated from the same data set are necessary for functional analysis of the genes related to biological pathways in Jurkat cells after this QUB-2392 treatment. To interpret these genes within the context of apoptosis, proliferation, and immune response, it is possible to use various bioinformatics tools such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) analysis, and STRING. QUB-2392 directs its biochemical activity onto various KEGG pathway mapping. DEGs are significantly enriched to the metabolic and signal transduction pathways in this analysis. There may be marked pathways involved including PI3K-AKT and apoptotic signaling pathways, which would highlight their role in QUB2392’s effects on leukemic cells. DEGs are classified into biological processes, molecular function, and cellular components, by GO analysis. This also enables the identification of critical processes such as cell cycle regulation, apoptosis, and modulating the immune response. For example, the Up-regulation of genes in apoptosis and immune response and downregulation of proliferation-associated genes would indicate that QUB-2392 may have a therapeutic effect through programmed cell death and immune reaction.

Prediction of gene interaction is based on construction of protein protein interaction (PPI) networks using STRING database. Identifying central regulatory genes which may be key mediators of QUB-2392’s anti leukaemic activity can be aided by this.

3.4 Expected Findings

The prediction is that the functional analysis of selected genes will help to elucidate these in critical cellular processes in ALL. They may include pro-apoptotic regulators such as BAX and CASP3, which promote apoptosis of leukemic cells. Downregulated genes could include, for example, oncogenes such as MYC, which is a key factor in cellular proliferation and survival. Furthermore, many immune response-related genes may be challenged, providing some evidence that QUB-2392 is being used to alter immune response surveillance. Overexpression of genes involved with T cell activation suggests QUB-2392 augments cytotoxicity mediated by the immune against leukemic cells. This study aims to integrate pathway analysis and protein interaction networks to give a comprehensive understanding of QUB-2392’s molecular effects. These findings could be used to support a novel therapeutic strategy for ALL, involving bioactive peptides.

References

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The Bioactive Peptide QUB-2392 as a Treatment for Acute Lymphoblastic Leukaemia Cell Line Jurka (1)