Examples of TSGs that undergo CpG island hypermethylation in breast cancer.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"7199",leadTitle:null,fullTitle:"Charged Particles",title:"Charged Particles",subtitle:null,reviewType:"peer-reviewed",abstract:"A charged particle is a particle that carries an electric charge and can be discussed in many aspects. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53849",title:"DNA Hypermethylation in Breast Cancer",doi:"10.5772/66900",slug:"dna-hypermethylation-in-breast-cancer",body:'\nEpigenetics, which was first coined by Waddington in 1942, literally means as ‘outside conventional genetics’, refers to the heritable, reversible changes in gene expression that occur without alteration DNA sequence [1]. Epigenetic modifications are natural processes and essential for mammalian development and cell proliferation. These epigenetic changes could also be affected by many random factors or environmental influences. Disruption of epigenetic modification resulting in regulating patterns of gene expression is the feature of a number of severe human diseases, including malignant cellular transformation [2–4]. Three main epigenetic modification systems, including DNA methylation, histone covalent modification, and non-coding RNA modification, leading to associated-gene silencing, have been observed [5, 6]. This chapter aims to introduce the reader to the concept of DNA methylation, especially DNA hypermethylation, with examples of its involvement in human breast cancer.
DNA methylation is one of the epigenetic mechanisms that is closely associated with normal cell development and a number of key processes including imprinting, X-chromosome inactivation, repression of repetitive element transcription, chromatin organization, etc. [7–9]. Aberrant methylation patterns are known to be presented in the genomes of cancer cells. Two patterns of aberrant methylation have been observed, including global hypomethylation along the genome and hypermethylation at the specific sites, namely the CpG islands (CGIs) within the promoter regions, according to the decreased and increased the level of methyl group modification, respectively [4, 8, 10–12]. Disordered DNA methylation contributes to a number of human diseases, including breast cancer. Increased level of genome-wide hypomethylation results in increased chromosomal instability and activation of regulatory DNA sequences, including transcription of oncogenes, retrotransposons as well as genes encoding proteins involved in malignant cell development. DNA methylation refers to a covalent modification of cytosine ring at the 5′ position of a CpG dinucleotide by adding a methyl group in the 5th carbon of the ring using S-adenosyl methionine as a methyl donor (Figure 1) [8, 12].
(A) The DNMTs catalyse the methyl cytosine modification. (B) The structure of SAM and SAH.
This methylation process is catalysed by DNA (cytosine-5) methyltransferases (DNMTs). In mammalian, DNMTs are a highly conversed family protein encompassing DNMT1, DNMT2, DNMT3A, DNMT3B and DNMT3L, which could be distinguished by their function [13–15] (Figure 2). DNMT1 was the first methyltransferase to be discovered [1], then DNMT3 was discovered and characterized. Regarding to DNMTs function, DNMT3A and DNMT3B perform de novo methylation by adding the methyl groups to unmethylated CpG, which is responsible for the establishment of new methylation pattern in genomic DNA, whereas DNMT1, which has a high preference for hemi-methylated DNA, maintains the existence of methylation patterns following DNA replication on the newly synthesized strand [3, 4, 13, 14, 16, 17]. DNMT3L (DNA (cytosine-5-)-methyltransferase 3-Like) has no catalytic activity, DNMT3L has been shown to act as a general stimulatory factor for de novo methylation and facilitate methylation of DNMT3A and DNMT3B [2, 18].
The roles of DNMTs.
The term CpG refers to the base cytosine (C) linked by a phosphate bond to the base Guanine (G) in the DNA nucleotide sequence, which usually cluster together in ’CpG islands (CGIs)’ and typically locate at or near the promoters and transcription sites of genes. The molecular mechanisms underlying CpG island hypermethylation in many human cancers, including breast cancer, have been explored. The hypermethylation of CGIs located at tumour suppressor genes can result in transcriptional silencing of genes through a number of mechanisms, including (i) DNA hypermethylation directly affects the RNA polymerase II and DNA interactions by inhibiting the binding of transcriptional factors on specific sequences, such as AP-2, c-Myc/Myn, E2F, NF-κB, etc. and (ii) hypermethylated DNA recruits methyl-CpG binding proteins (MeCP1 and MeCP2), and methyl-CpG binding domain protein (MBD1, MBD2, MBD3 and MBD4) [4].
\nTumour suppressor genes (TSGs) normally suppress or negatively regulate cell proliferation by encoding proteins that block the action of growth-promoting proteins. A hallmark of cancer involves the loss of function of TSGs through the silencing genetic information. The silencing of TSGs by the high levels of 5-methylcytosine in their CpG island promoter regions, considered as the ’first and second hit‘, is equivalent to mutations and translocations, in Knudson’s two-hit model of tumorigenesis [19, 20]. Here, the methyl groups become chemically bonded to the cytosine in CGIs, leading to disruption of the normally controlled cell proliferation and drive it to malignancy (Figure 3). Thus, the presence of m5CpG dinucleotide in tumour suppressor gene promoters is recognized as an important event in many human tumour types.
The typical CpG island of a tumour suppressor gene is represented in a normal and a tumour cell. White dots: unmethylated CpG; black dots: methylated CpG.
The high presence of cell-free circulating tumour DNA (ctDNA), which is derived from primary tumour cells, can be found in blood and non-invasive samples of patients with cancer, such as urine, brochoalveolar lavage, mammary aspiration fluids, saliva, sputum, etc. makes an ideal candidate biomarker for prognosis and early diagnosis of breast cancer. ctDNA can be distinguished from circulating DNA derived from healthy cells by the presence of genomic aberrant modifications. For example, upon the tumour development, ctDNA carries tumour specific epigenetic modifications, i.e. DNA hypermethylation, is released due to the lysis of circulating cancer cells or micro-metastases. Therefore, the detection of genetic and epigenetic alterations in ctDNA offers a potential source of development of prognostic and predictive biomarkers for cancer. Quantitative evaluation of ctDNA can reflect tumour burden relevant to provide information on genetic and epigenetic profiles associated with human cancer development. The concentration of methylated ctDNA is presented in an even smaller portion of this amount, thus, presenting a challenging substrate to work with. Fortunately, even in the low concentration, ongoing technical developments and much of the progress in molecular biological techniques have provided a chance that they can be directly applied in ctDNA collection and validation even smaller amounts of ctDNA [10, 21, 22].
DNA aberrant methylation patterns, like hypermethylation of TSGs, global hypomethylation, etc. have been observed in human breast cancer. Silencing of TSGs expression by DNA hypermethylation provides a molecular mechanism by which DNA hypermethylation could trigger tumour development by interfering with the binding of transcription factors located at TSG gene’s promoter. Thus, numerous studies have been attempted to focus on the role of hypermethylation of the TSG genes’ promoter in breast cancer as well as the correlation between methylation of specific CGIs in TSGs and many breast cancer clinical states. Table 1 shows the most relevant hypermethylated genes involve in various functions in breast cancer reported so far. Methylation of these TSG promoters is associated with the complete loss of TSG protein products in cancer cells and development of malignant phenotype.
TSGs | Function | Location |
---|---|---|
APC | Inhibitor of β-catenin, cell proliferation, migration and adhesion | 5q21 |
BRCA1 | DNA damage repair | 17q21 |
Cyclin D2 | Regulators of CDK kinases | 12p13 |
GSTP1 | Conjugation to Glutathione, prevention of oxidative DNA damage | 11q13 |
p16INK4α | Cyclin-dependent kinase inhibitor | 9p21 |
PTEN | Negatively regulating AKT/PBK signalling pathway | 10q23 |
RARβ | Retinoic acid receptor | 3p24 |
RASSF1A | Ras effector homologue, cell cycle arrest | 3p21 |
ZMYND10 | Inhibitor of colony formation of cancer cells | 3p21.3 |
Examples of TSGs that undergo CpG island hypermethylation in breast cancer.
This DNA hypermethylation is a reversible signal, maybe due to the activity of Demethylase, which performs the reverse reaction to DNA methyltransferase and is an excellent candidate to be one of its important partners in shaping the methylation pattern of genomes [23, 24]. Thus, nowadays, many studies have been focused on an innovative approach in cancer treatments in which aimed to inhibit DNA hypermethylation and/or re-expression of silenced TSGs.
\nTherein, the hypermethylation of the CGIs promoter of BRCA1 gene is now recognized as one of the most common molecular abnormalities associated with breast cancer development and is quoted as a significant example. BRCA1 (Breast cancer 1) gene (HGNC: 1100; Entrez Gene: 672; OMIM: 113705; UniProtKB: P38398), which locates at 17q12-21, also known by many other names such as IRIS, PSCP, BRCAI, BRCC1, RNF53, BROVCA1, etc. is a tumour suppressor gene that conferred genetic pre-disposition to early onset of human breast and ovarian cancer [25–27]. This gene encodes a nuclear phosphoprotein that plays a role in maintaining genomic stability. The encoded protein combines with many other tumour suppressor, DNA damage sensors, and signal transducers to form a large multi-subunit protein complex that is called as BRCA1-associated genome surveillance complex (BASC). Therefore, the BRCA1 protein is involved in multifunction, such as repairing damaged DNA of double-stranded break, transcriptional regulation, ubiquitinylation, recombination and controlling the cell cycle check points as well as other functions. The hypermethylation of the BRCA1 promoter has been considered as an inactivating mechanism of BRCA1 expression, leading to breast tumourigenesis. In addition, some evidences have shown the significant association between the inactivation or low expression of BRCA1 protein expression and the aberrant methylation status of CGIs in the BRCA1 promoter in breast cancer tumorigenesis.
\nIt is well known that breast cancer constitutes a heterogeneous complex of diseases characterized by different distinct morphologies, biological behaviours and clinical outcomes. The classification and diagnosis of breast cancer have been based on the expression of different proteins, including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) [28, 29]. An example of such a target molecular therapy is Trastuzumab (Herceptin®), which has been approved to directly against HER2-expressing tumours. Among the variety of breast cancer types, a subtype called triple-negative breast cancer (TNBC), which is clinically defined by the lack of expression of ER, PR and HER2, presents a challenge for effective clinical management [28]. Therefore, it is essential to find a reliable biomarker, which are not only useful for the screening, early diagnosis and prognosis prediction for breast cancer, but also provide insight into the mechanisms driving tumourigenesis as well as an innovative approach in breast cancer treatments.
\nOver the past few years, a considerable amount of studies has been conducted to evaluate the association between BRCA1 promoter methylation and many clinicopathological characteristics of breast cancer. Therefore, tentatively, a meta-analysis was carried out, a total of 44 studies including 25 case-control studies and 19 cohort studies were eligible, enrolled into the meta-analysis research. According to our research, the prevalence of the hypermethylated BRCA1 promoter has been reported to fall in the range from 9.1 to 59.2%, which was statistically significant higher in breast cancers than non-cancerous controls (OR = 4.00, 95% CI= 2.336–6.878, P < 0.001, Figure 4). Because of large heterogeneity (PH ≤ <0.0001, I2 = 73.82%), we continued to clarify the potential source of heterogeneity via stratified analysis based on sample materials, methods for identifying methylation and ethnicity; with the detailed results were summarized in Table 2.
Test of association | Test of heterogeneity | ||||||
---|---|---|---|---|---|---|---|
Variables | N | OR (95% CI) | Z | P-value | Model | Variables | N |
Total | 25 | 4.00 (2.336–6.878) | 5.04 | <0.001 | R | <0.0001 | 73.82% |
Material | |||||||
Tissue | 22 | 4.312 (2.395–7.765) | 4.87 | <0.001 | R | 0.0003 | 58.32% |
Blood | 10 | 2.485 (1.433–4.310) | 3.24 | 0.001 | R | 0.0045 | 60.78% |
Methods | |||||||
MSP | 15 | 5.059 (2.214–11.561) | 3.845 | <0.001 | R | 0.0001 | 67.89% |
Others | 10 | 2.506 (1.409–4.457) | 3.126 | 0.002 | R | 0.0049 | 61.97% |
Ethnicity | |||||||
Caucasian | 10 | 2.291 (1.147–4.576) | 2.349 | 0.006 | R | 0.0375 | 49.25% |
Asian | 14 | 4.006 (2.122–7560) | 4.282 | <0.001 | R | 0.0060 | 55.60% |
Africa | 1 | 18.5217 (6.917–49.59) | 5.809 | <0,001 | NA | NA | NA |
Overall and subgroups analyses of BRCA1 methylation and breast cancer risk in 25 cases control studies.
Note: N: the total number of eligible studies; Caucasians included: American and Europeans, Australians. PH: the P-value of Q test for heterogeneity among studies; F: fixed-effects model; R: random-effects model; NA: non-analysis.
Forest plot for evaluating the association between BRCA1 promoter methylation and breast cancer under fixed or random effect mode.
As shown in Table 2, the pooled OR for BRCA1 promoter hypermethylation in breast cancer tissues was 4.312 (95% CI = 2.395–7.765, P < 0.001) compared with normal or benign tissues, and was higher than the pooled OR in peripheral blood of breast cancer patients (OR = 2.485, 95% CI = 1.433–4.310, P = 0.001) compared with non-cancer controls. In addition, the pooled OR for BRCA1 promoter hypermethylation detected by MSP was 5.059 (95% CI = 2.214–11.561, P < 0.001), significant higher than other methods (OR = 2.506; 95% CI = 1.409–4.457, P = 0.002). Meanwhile, the frequency of BRCA1 promoter hypermethylation in Asians (OR = 4.006, 95% CI = 2.122–7.560; P < 0.001) was higher than in Caucasians (OR = 2.291, 95% CI = 1.147– 4.576, P = 0.006). Furthermore, our studies also demonstrated that the BRCA1 promoter hypermethylation was significant correlated with the clinicopathological characteristics which included ages, meant that the prevalence of hypermethylation status was higher in the group of age under 55 (OR = 1.227, 95% CI = 1.604–1.414, P = 0.05) (Figure 5); histological grade, meant that the hypermethylated BRCA1 in the case of histological grade 3 and 4 was higher than in the histological grade 1 and 2 (OR = 1.858, 95% CI = 1.499–2.301, P < 0.001) (Figure 6); disease stages, meant that the prevalence of the hypermethylation of BRCA1 gene in the case of late stages was higher than in early stages (OR = 1.339, 95% CI = 1.023–1.752, P = 0.033) (Figure 7). Additionally, the hypermethylation status of BRCA1 gene’s promoter was correlated with the ER(−) (OR= 2.02, 95% CI = 1.525–2.675, P < 0.001), PR(−) (OR = 1.823, 95% CI = 1.374–2.41, P < 0.001) and especially with triple-negative phenotype (OR = 2.814, 95% CI = 1.811–4.371, P < 0.001) under fixed or random effect mode (Figure 8). Thus, those meta-analysis results confirmed that the BRCA1 promoter hypermethylation was significant correlated with the increased risk of breast cancer, associated with several specific clinicopathological characteristics of breast cancer, which indicated that BRCA1 promoter hypermethylation could be utilized as an effective biomarker in predictive and diagnostic breast cancer.
Forest plot for evaluating the association between BRCA1 promoter methylation and ages under fixed or random effect mode.
Forest plot for evaluating the association between BRCA1 promoter methylation and histological tumour grades under fixed or random effect mode.
Forest plot for evaluating the association between BRCA1 promoter methylation and disease stages under fixed or random effect mode.
Forest plot for evaluating the association between BRCA1 promoter methylation and triple negative phenotype under fixed or random effect mode.
Up to now, a significant proportion of breast cancer patients who have poor prognosis will develop recurrence. This needs to find a more sensitive and specific biomarker, which can be a powerful prognostic indicator and help make therapeutic decisions to prolong the survival time of patients. Then, we included 10 articles provide disease-free survival (DFS) and/or overall survival (OS) to evaluate the role of the BRCA1 promoter hypermethylation in the prognosis of breast cancer. Overall survival (OS), which was defined as the length of time from either the date of diagnosis or the start of treatment for breast cancer, that patients diagnosed with the disease are still alive, and disease-free survival (DFS), which was defined that the length of time after primary treatment for a cancer ends that the patient survives without any signs or symptoms of that cancer. In detail, in the Asian population, the OS and DFS were 2.163 (95% CI = 1.212–3.858, P < 0.001) and 2.47 (95% CI = 1.331–4.584, P = 0.004), respectively, using single variable analysis. In the case of using multiple variables analysis, the OS and DFS were 1.611 (95% CI = 1.116–2.324, P = 0.011), and 2.872 (95% CI = 1.389–5.937, P = 0.004), respectively. Those analytic results indicated that hypermethylated BRCA1 gene’s promoter was significant associated with OS, DFS, meant that it was poor prognosis to breast cancer patients, in both single and multiple variables analysis. Hence, BRCA1 promoter hypermethylation is suggested to be a potential biomarker for prognostic assessment.
The process of DNA methylation is catalysed by DNMTs which typically occurs at CpG dinucleotides. As mentioned earlier, it is also a reversible process. Removal of a methyl group from DNA must involve a cleavage of a carbon-carbon bond, which is carried out by DNA demethylase (dMTase). In addition, the methylation reaction can be blocked by the inhibitors of DNA methylation drugs, such as 5-azacytidine, 5-aza-2′-deoxycytidine, etc. which contains a nitrogen in the place of carbon at 5′ position of cytosine ring (Figure 9) [30]. This drug is cooperated into DNA, then, replaces the natural base cytosine and acts as a potent inhibitor of the DNMTs, inducing the DNA demethylation [31]. Since DNA methylation is reversible, an aberrant hypermethylation of tumour suppression genes can be reverted. This consequently supports DNA methyltransferases (DNMTs) as attractive therapeutic targets. Indeed, epigenetic drugs (epi-drug)—methylation inhibitors through DNMT inactivation, used alone or in combination with other biomarkers, including by dietary agents, for targeted preventive and therapeutic interventions, have attracted attention recently.
Inhibition of DNMTs by 5-azacytidine.
DNMT inhibitors (DNMTi), such as 5-azacytidine (azacitidine) and 5-aza-2′-deoxycytidine (decitabine) (Figure 10), are epi-drugs which are first announced and currently marketed as hypomethylation therapeutics. They are nucleoside analogues, derivatives of cytidine that work by incorporating into the DNA sequence at cytosine positions during DNA replication to be active and then form a suicidal covalent complex with the DNMTs. These drugs have been approved by Food and Drug Administration (FDA) for clinical tests on the myelodysplastic syndrome, malignant mesothelioma, pre-leukemic disease, breast cancer, nasopharyngeal carcinoma and some other diseases.
The structure of 5-azacytidine and 5-aza-2′-deoxycytidine.
Zebularine is another cytidine analog that has a mechanism similar to 5-azacytidine, integrating into DNA and forming a covalent bond with DNMT1, resulting in inhibition of methylation reaction. Moreover, Zebularine is reported that it is a DNMT1 inhibitor with low toxicity and has a high sensitivity in selective cancer cells. Particularly, this drug showed the reactivated functions on some important tumour suppressor genes that were disrupted in breast cancer cell lines, even at low concentrations. Although the drug is not yet FDA approved, a preclinical study on mouse models showed that Zebularine can inhibit DNA methylation and induce re-expression-silenced gene, even given orally.
\nOther trends related to DNA methylation including the inhibition of DNMTs through siRNA, ribozymes, antisense oligonucleotides have also been considered. Some drugs have proven effective impact on cell cultures, animal models and clinical trials as well such asMG98, a 20 bp anti-sense oligonucleotide that directly prevents the translation of DNMT1 or RG108—a new small molecule that can act on active site of DNMT1. Unlike the nucleoside analogs, RG108 did not demonstrate cytotoxic or genotoxic effects on cells even at high concentrations.
\nThe combination of the histone deacetylase inhibitors (HDACi), such as Trichostatin A (TSA) and phenylbutyrate, with DNMTi is a new trend giving promising efficacy in the treatment of cancer. In breast cancer, triple negative metastatic patients that do not express estrogen receptor (ER), progesterone receptor (PR) and HER, do not respond to agents such as trastuzumab (Herceptin) and tamoxifen. Particularly, the loss of ER in some triple negative breast cancers is epigenetically silenced by abnormal methylation and histone modifications. Consequently, the patients express the resistance of anti-estrogen. Triple negative metastatic breast cancer patients were pre-treated with decitabine—a DNMTi and LBH 589—an HDACi, to restore the ER and then treated with tamoxifen. This combination can remove the epigenetic modifications including DNA methylation and histone deacetylation and reactivate ER. Thus, this reactivated ER cells become sensitive to agents like tamoxifen. Similarly, the combination of azacitidine with TSA also induces the re-expression of ER function to increase the sensitivity of breast cancer cell lines that previously show negative expression with ER in tamoxifen therapy or the combination of HDACi and trastuzumab has taken to effectively suppression of the development and apoptosis induction into breast cancer cells lines.
\nIn addition, the combination of epi-drugs with chemotherapeutic agents or natural dietary ingredients also increases the effectiveness of treatment. A pre-clinical study has shown that the combination of decitabine and docetaxel (an anti-mitotic drug) can increase treatment outcomes against cancer cells in experiments conducted on breast cancer cell lines [32, 33]. Decitabine in combination with another substance, amsacrine or idarubicin, also shows therapeutic effect. Green tea polyphenol, (-)-epigallocatechin-3-gallate (EGCG), may cause re-modelling of chromatin structure and the ERα promoter by histone acetylation and DNA methylation mechanisms, and consequently reactivating ERα. The combination of TSA and EGCG leads to reactivation of numerous tumour suppressor genes by inhibiting directly or indirectly DNMTs. Dietary sulforaphane—an inhibitor of histone acetylation also shows very effective activity in the inhibition of proliferation and survival of breast cancer cells without affecting normal cells.
\nTherefore, methylation combined therapy is very promising in the treatment of breast cancer. Clinical trials in the combination of trastuzumab with HDACi for the treatment of breast cancer, and a phase II trial in breast cancer—valproic acid combined with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) also are being investigated. Up to date, several other classes of epi-drug have been studied, developed with new drugs, which based on the DNMT inhibitors, HDAC inhibitors, HMT inhibitors, etc. in early preclinical trial development.
DNA hypermethylation has become established in recent years as being one of the important causes of breast tumorigenesis and potential biomarkers in clinical applications, prognosis and early diagnosis of breast cancer. As the release of tumour-associated DNA into body fluids, thus the screening of plasma or serum DNA may provide information on epigenetic profiles which are tightly associated with breast cancer development, progression and response to therapies. This is the real advantage of an aberrant DNA methylation property as a great versatility, promising biomarker for the molecular monitoring of cancer patients, and applied in early detection, prognosis and predicting drug sensitivity in cancer.
APC | Adenomatous Polyposis Coli |
BASC | BRCA1-associated genome surveillance complex |
BRCA1 | Breast cancer 1 |
CGIs | CpG islands |
CI | Confidence interval |
ctDNA | Cell-free circulating tumour DNA |
DFS | Disease free survival |
DNMTi | DNA methyltransferase inhibitors |
DNMTs | DNA methyltransferase |
ER | Receptor |
FDA | The Food and Drug Administration |
GSTP1 | Glutathione S-transferase P1 |
HDACi | Histone deacetylase inhibitors |
HER2 | Human epidermal growth factor receptor 2 |
m5CpG | Methyl-5-CpG |
MBD | Methyl-CpG binding domain protein |
OR | Risk ratio |
OS | Overall survival |
p16INK4α | CDK4 Inhibitor p16-INK4α |
PR | Progesterone receptor |
PTEN | Phosphatase and Tensin homolog |
RARβ | Retinoic Acid Receptor Beta |
RASSF1A | Ras Association domain Family 1 isoform A |
TNBCs | Triple-negative breast cancer |
TSA | Trichostatin A |
TSG | Tumour suppressor gene |
ZMYND10 | Zinc Finger MYND-Type Containing 10 |
Land use and land cover changes have significant environmental consequences at local, regional, and global scales. These changes have intense implications at the regional and global scales for global loss of biodiversity, distresses in hydrological cycles, increase in soil erosion, and sediment loads [1]. At the local level, changes in the use of land and its cover affect watershed runoff, microclimatic resources, processes of land degradation and landscape-level biodiversity, soil erosion, and sediment loads [2]. All these have direct impacts on livelihoods of local societies.
The Shire River in Malawi, southern Africa, is among the areas where land use land cover change (LUCC) has become more prevalent in recent years resulting into severe soil erosion and causing heavy siltation downstream [3, 4, 5, 6, 7, 8, 9]. The river is an important source of livelihood to many people, using the water for agriculture, domestic purposes, and the generation of electricity [6, 8, 10]. One of the most important structures across the Shire River is the Nkula B Hydroelectric Power Station situated in the middle section of the river. The dam at Nkula Falls that supplies water into the power station has, in recent times, been threatened with massive siltation, some studies attributing this to increased human population and agricultural activities [5, 6, 8]. The conceptual setting of this study originates from a strong link that exists between land use change and soil erosion [8, 11, 12, 13, 14, 15]. Land use and management practices are important factors in determining the extent of soil erosion [8, 15]. Good vegetation cover promotes infiltration of water into the ground and soil retention, while deforestation results into increased runoff than infiltration occurring during periods of more precipitation [16, 17, 18]. Increased runoff consequently leads to stronger soil erosion usually in areas with poor vegetation cover [8, 19, 20]. Erosion of soil under continuous cultivation is the most serious form of resource degradation occurring in Malawi [3, 8, 19, 21, 22, 23]. The rate of soil loss in Malawi is currently estimated at 29 t/ha/year [24], which is higher than the previously reported 20 t/ha/year [21]. In the middle Shire River, estimated soil loss between the year 2000 and 2014 ranged from 0.1 to 21.1 t/ha/year [24, 25]. According to the Malawi Government Report (2015), the middle Shire River catchment has many bright spots (areas experiencing high soil loss but declining trends over time), for example, Neno and Ntcheu in the west and Zomba and Chiradzulu in the eastern side of the river.
The question regarding land use changes over time, and its driving forces in the middle Shire River catchment nevertheless remain unresolved [4, 6]. Such knowledge is critical to the development of policies and action plans necessary for changing current LUCC trends in the area as it has been observed in other places [26, 27, 28, 29, 30]. Furthermore, problems of LUCC are global and serious in many developing countries where increasing population has resulted into excessive pressure on natural resources [8, 30].
The study was carried out to understand the impact of land use and land cover changes on the Nkula Dam in the middle Shire River catchment, Malawi. The LUCC drivers analyzed in this study include biophysical changes (e.g., climate change) and human activities (e.g., population, poverty, land policies, and GDP growth) [3, 4, 6]. Climate and socioeconomic data were compiled to analyze the drivers of LUCC in the study area. Geographic information systems (GIS) and remote sensing techniques which are gaining increased recognition globally as rapid methods of acquiring and analyzing up-to-date information over a large geographical area were used in the study [30, 31, 32, 33].
The Shire River is the largest river in Malawi, originating from Lake Malawi which supports vast agricultural and socioeconomic activities in its catchment (Figure 1) [34]. The river is divided into three sections, namely, the upper, middle, and lower Shire [34, 35]. This study focused on the catchment of the middle section of the river which includes the Shire Plain which is bounded by mountains on both sides and the Nkula Dam downstream [34, 36]. The plain is more extensive to the west of the river than it is to the east (Figure 1). The middle section of the Shire River has eight administrative districts, supporting a population of about 5 million people (Figure 1).
Map of Malawi (left) showing the middle Shire River and its catchment (right). Eight administrative districts are located in the study area.
Climate in the middle Shire River catchment area varies due to differences in altitude with annual average precipitation ranging from 750 to 2500 mm [35, 37]. Highlands receive more rain which begins in November and ends late in April [6, 37]. Annual average temperature of the area is around 23°C, with highlands in the east experiencing cooler temperatures than plains in the west [6, 35]. The rocks in the study area are mainly composed of Precambrian basement complex and igneous rocks [37]. Amphibolite and granulite facies are dominant in the western and eastern side of the Shire River, respectively, while soils in the river’s catchment are dominated by Cambisols [6, 24, 37].
The following procedures were followed in order to answer the study questions: firstly, six Landsat images for the dry seasons (to avoid cloud cover effects) of 1989, 1993, 2000, 2006, 2011, and 2015 were downloaded from the United States Geological Survey (USGS,
Landsat images were processed using ENVI 5.1 Software to study information on the types of land use and their spatial patterns. To analyze these spatial patterns, the following steps were followed: firstly, relative radiometric correction was done on each band to eliminate errors arising from radiation caused by weather conditions; secondly, multiband combination of Landsat images was done in preparation for research spectral characteristics of various types of land use; thirdly, geometric correction of remote sensing images was done using Malawi DEM, Universal Transverse Mercator Projection, Arc 1960, and UTM Zone 36S, based on 1:50,000 topographic map scale so that it fits with the Landsat images [38, 39]. This helps to eliminate position errors of Landsat images which the terrain, position of the sun, and angle sensor may produce. A mosaic of required images was prepared and a single image generated. Atmospheric Landsat images were then corrected by ENVI 5.1 FLAASH module.
After processing the Landsat images, identification of different land use classes was done where some visual designs like texture, tone, and the effect zones were used [38]. The land in the study area was classified according to its use or description such as cultivated land, water, forest (indigenous and plantations were combined), etc. When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it’s among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38, 39, 40, 41, 42, 43, 44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41, 45]. Representative zones for each desired class were located in the image with adequate number of pixels covering the known classes to reduce the image noise [38]. Secondly, training area number and percentage were identified in order to classify several training and test areas. These results were compared with supporting ground data so that the new training statistics could be derived. Thirdly, a statistical file known as spectral signature was created by the image processing software for each class because each and every pixel can only be assigned to one spectral class. Lastly, each pixel was allocated to the most likely class based on the maximum likelihood algorithm where each pixel is assigned to the spectral class that has the greatest probability density function for the multispectral values of the pixel. Maximum likelihood algorithm is the most commonly used algorithm in which a pixel is classified into the corresponding class [38, 43, 46]. Land cover types were then classified into the following eight main classes according to Anderson et al. [47]: (1) forest, (2) shrubland, (3) grassland, (4) cultivated land, (5) bare land, (6) water bodies, (7) wetland, and (8) artificial surfaces (Table 1).
No. | Land cover class | Description |
---|---|---|
1 | Forest | Woodland open general (15–65%) with herbaceous layer. Broadleaved deciduous trees, closed >(70–60)%. Vegetative cover is in balance with the abiotic and biotic forces of its biotope |
2 | Shrubland | Closed to open (thicket) (15–100%) scattered trees |
3 | Grassland | Herbaceous closed vegetation (15–100%) with some trees, shrub Savannah, and permanent marsh |
4 | Cultivated land | Areas where the natural vegetation has been removed or modified and replaced by other types of vegetative cover of anthropogenic origin. All vegetation that is planted or cultivated with intent to harvest is included in this class |
5 | Bare land | Bare rock and/or coarse fragments. Areas that do not have an artificial cover as a result of human activities. These areas include areas with less than 4% vegetative cover |
6 | Water bodies | This class refers to areas that are naturally covered by water, such as lakes, rivers, snow, or ice |
7 | Wetlands | Areas that are transitional between pure terrestrial and aquatic systems and where the water table is usually at or near the surface or the land is covered by shallow water |
8 | Artificial surfaces | Areas that have an artificial cover as a result of human activities, such as construction (cities, towns, and transportation), extraction (open mines and quarries), or waste disposal |
Land cover classes considered and their description [71].
A total of 165 training sites (sampled portions of the scene, purposely selected, for the derivation of the training statistics) were chosen for each image to ensure that all spectral classes constituting each land use and land cover categories were adequately represented in the training statistics to classify the entire scene [48]. Classification was done using ground checkpoints, digital topographic maps, vegetation cover map, and the researchers’ knowledge of the study area [49, 50]. A total of 156 sampling points (GPS + photograph) were collected out of the 165 training sites during the dry season to avoid cloud cover effects which is more common in rainy season. Land use types at the sampling sites were evaluated according to field surveys (photographs + GPS) where photographs were taken using a camera and coordinates of the spot were taken using GPS. Accuracy of the supervised classification methods was checked by a confusion matrix of accuracy (Table 2) [38, 44, 51] to ensure that various measures, such as error-rate, accuracy, specificity, sensitivity, and precision, were checked.
Actual type | Classified type | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Forest | Shrubland | Grassland | Cultivated land | Artificial surfaces | Wetland | Water bodies | Bare land | Actual sum | Accuracy | |
Forest | 9 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 82% |
Shrubland | 0 | 14 | 1 | 1 | 0 | 0 | 0 | 0 | 16 | 88% |
Grassland | 0 | 1 | 20 | 1 | 0 | 1 | 0 | 1 | 24 | 83% |
Cultivated land | 0 | 0 | 1 | 21 | 1 | 0 | 0 | 0 | 23 | 91% |
Artificial surfaces | 1 | 1 | 1 | 2 | 34 | 0 | 0 | 0 | 39 | 87% |
Wetland | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 0 | 9 | 89% |
Water bodies | 0 | 1 | 0 | 0 | 0 | 1 | 32 | 0 | 34 | 94% |
Bare land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Classified sum | 10 | 18 | 25 | 25 | 35 | 10 | 32 | 1 | 156 |
Confusion matrix of accuracy evaluation in middle Shire River catchment in 2015.
Landsat image classified type results were compared with the field survey results to evaluate their accuracy and then calculated using confusion matrix evaluation table (Table 2).
LUCC drivers were mainly analyzed using descriptive methods due to inavailability of spatial socioeconomic data from the government database. Pearson correlation coefficients between socioeconomic data and land use types were analyzed in SPSS for Windows version 10.
The overall classification accuracy ranged from 82 to 94% (Table 2). The western side of the Shire River covers an area of approximately 3353 km2, while the eastern side is 2770 km2 comprising 55 and 45% of the total area, respectively. Regions were defined by slope of less than 10o as plain/flat area. According to Table 3, total plain/flat area covers 2417 km2 which is lesser compared to highlands (with slope ranging from 10o to 90o) covering 3706 km2. Eastern and western plain/flat areas cover 988 and 1429 km2, representing 41 and 59% of the total plain/flat area of the study area, respectively (Table 3).
Area/coverage | Plain (≤10°) | Highlands (10–90°) | ||
---|---|---|---|---|
Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | |
Western side | 1429 | 59 | 1075 | 29 |
Eastern side | 988 | 41 | 2631 | 71 |
Total catchment area | 2417 | 100 | 3706 | 100 |
Distribution of plains and highlands in eastern and western side of the middle Shire River.
The middle Shire River catchment is dominated by shrubland, grassland, cultivated land, and forestland, which accounted for 36, 28, 22, and 12% in 1989, respectively (Figure 2).
Land use and land cover changes from 1989 to 2015.
Findings (Table 4) show significant land use and land cover changes in the middle Shire River catchment over the 26-year period.
Land cover type | Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | |||||||
Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Forest | 739 | 12.07 | 679 | 11.08 | 545 | 8.90 | 479 | 7.82 | 481 | 7.86 | 662 | 10.80 |
Shrubland | 2201 | 35.95 | 1986 | 32.44 | 2264 | 36.97 | 2043 | 33.37 | 1835 | 31.85 | 2040 | 32.97 |
Grassland | 1719 | 28.07 | 1838 | 30.02 | 1451 | 23.69 | 1692 | 27.63 | 1617 | 24.53 | 1255 | 20.52 |
Cultivated land | 1367 | 22.33 | 1538 | 25.12 | 1745 | 28.50 | 1814 | 29.64 | 2067 | 33.76 | 2073 | 34.09 |
Artificial surfaces | 26 | 0.43 | 28 | 0.45 | 33 | 0.54 | 37 | 0.60 | 39 | 0.64 | 43 | 0.71 |
Wetland | 35 | 0.57 | 23 | 0.38 | 56 | 0.91 | 19 | 0.31 | 38 | 0.63 | 20 | 0.34 |
Water bodies | 31 | 0.51 | 30 | 0.49 | 20 | 0.33 | 30 | 0.49 | 36 | 0.58 | 22 | 0.44 |
Bare land | 4 | 0.06 | 2 | 0.03 | 9 | 0.15 | 9 | 0.15 | 9 | 0.15 | 8 | 0.13 |
Area (km2) and percentages of different land cover types from the year 1989 to 2015.
Artificial and cultivated land increased by 65 and 52%, respectively, in the 26-year period, while forest cover, grass, and shrubland decreased by 35, 27, and 7%, respectively. Other land classes such as wetlands and water bodies show fluctuations (Figure 2 and Table 4). Spatially, in 1989, total cultivated land in the western side was 694 km2 which increased to 1226 km2 by the year 2015, representing 21 and 37% of the total land in the western side, respectively (Table 5).
Location/district | Year | ||||||
---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | ||
Western side | Balaka | 335 | 556 | 627 | 655 | 688 | 853 |
Mangochi | 59 | 51 | 41 | 80 | 47 | 91 | |
Neno | 25 | 41 | 49 | 38 | 28 | 53 | |
Ntcheu | 275 | 298 | 219 | 226 | 219 | 228 | |
Total area | 694 | 946 | 935 | 999 | 982 | 1226 | |
Eastern side | Blantyre | 359 | 264 | 362 | 381 | 244 | 278 |
Chiradzulu | 33 | 9 | 19 | 17 | 18 | 23 | |
Machinga | 184 | 247 | 264 | 263 | 135 | 368 | |
Zomba | 96 | 71 | 165 | 155 | 122 | 194 | |
Total area | 673 | 591 | 810 | 816 | 520 | 862 |
Changes in cultivated land area (km2) in districts of the middle Shire River catchment.
This suggests an increase of 16% of cultivated land in the western side between 1989 and 2015. In the eastern side, cultivated land increased from 673 to 862 km2 within the same period, representing 24 and 31%, respectively, of the total land area indicating a 7% change. In 1989, the western side of the Shire River catchment mainly consisted of shrubland, grassland, and forestland which accounted for 35, 33, and 10%, respectively. In the eastern side, shrubland, grassland, and forestland accounted for 37, 22, and 15%, respectively. The western side (Balaka, Neno, and Ntcheu) and eastern side (Zomba) are the main districts where forest, shrubland, and grassland decreased the most. For example, in Balaka District, forest area reduced from 11% in 1989 to 2% in 2011 before increasing to 3% in 2015, while shrubland decreased from 38% in 1989 to 18% in 2011 and then increased to 23% in 2015. Forestland in Neno District decreased from 10% in 1989 to 1% in 2011 and then increased up to 5% in 2015, while shrubland decreased from 35% in 1989 to 19% in 2015 and grassland from 27% in 1989 to 17% in 2015 with some fluctuations in between the years. In Ntcheu District, grassland decreased from 35% in 1989 to 15% in 2015. Forest cover in Zomba district declined from 19% in 1989 to 7% in 2006 and then started to increase from 2011 reaching 12% in 2015. Shrubland decreased from 41% in 1989 to 27% in 2015 in the same district.
Results indicate some fluctuations in the amount of rainfall received in the area within the 26-year period that might be due to climate change as a result of land use and land cover changes due to human activities (Figure 3).
Annual rainfall and temperature for the middle Shire River catchment from 1989 to 2015. Circles represent flood years, while rectangles represent drought years (Source: Malawi Meteorological Department).
Rainfall in the catchment area declined continuously from 1989 to 1993, culminating into the drought of 1992 and 1993 (Figure 3) [52, 53]. Malawi is regularly affected by drought and floods [53]. The country (including the study area) was affected by heavy floods in 1989, 1998, 2000, 2001, and 2015, destroying crops and displacing many people (Figure 3) [53]. Earlier studies indicate that rainy season in Malawi is dominated by tropical and extratropical influences with links to the El Niño-Southern Oscillation (ENSO) [54, 55]. Actually, this is reported for the whole of Southern Africa [56].
The population of Malawi which includes districts under study on the western (Mangochi, Balaka, Ntcheu, and Neno) and eastern sides of the middle Shire River (Blantyre, Zomba, Machinga, and Chiradzulu) has been increasing steadily since the 1980s (Figure 4).
Population of districts in the middle Shire River catchment area and GDP (US$) for Malawi from 1989 to 2015 [53].
Increased population is more pronounced in urban areas. For example, in 2015, Blantyre and Zomba cities had 3006 and 2240 people per km2, respectively [34, 53, 57]. There has been a general increase in the GDP over the past 26 years especially between 2006 and 2011 and falling between 1993 and 2003 (Figure 4).
Rainfall affects LUCC in the middle Shire River catchment. Drought and floods in the western side of the river, therefore, have resulted into low crop yield. As a survival mechanism, people resort to cutting down of trees to earn income, causing forest degradation [58, 59]. This may, therefore, explain the concurrent low rainfall received against a sharp decline in forest areas between 2006 and 2011 (Figures 2 and 3). Results in this study agree with an earlier report for the upper Shire River catchment [60] indicating a direct link between poor rainfall (drought/floods) and cutting down of trees.
Rapid population growth is one of the drivers of LUCC in the western side of the middle Shire River earlier reported by [60, 61]. Population increase in the western part of the middle Shire River is mainly attributed to the influx of refugees fleeing the civil war from Mozambique from the 1990s. Population growth leads to urbanization, increase in cultivated land, and residential area [3, 8]. The high population density in Malawi with an estimated growth rate of 2.8% is putting increasing pressure on its natural resources, leading to expansion of farming on marginal lands and forests as well as encroachment into protected forest reserves/parks. Results in this study show a transition of land use from forest, shrubland, and grassland to cultivated land and buildup areas (Tables 4 and 5). These changes mainly occurred between 1989 and 2011 (Figure 2 and Table 4) probably due to increasing anthropogenic pressure on natural forests. Results also show a drastic change in forest/grassland/shrubland between 1989 and 2011 in three out of the four districts (Balaka, Neno, and Ntcheu) in the western side of the middle River Shire. Large proportion of shrubland, grassland, and forestland (84%) in the western part of the river were converted to cultivated land, buildup areas, and/or bare land. This confirms earlier assertion that increasing population results into a decrease in forest area (Figure 5).
Changes in forest, cultivated land in the catchment area, and siltation volume in the Nkula Dam from 1989 to 2015.
The rate of forest decline experienced by Malawi [61] and the Shire River catchment in particular [59], due to heavy dependency on wood for energy, is alarming. Most people around the middle Shire River catchment rely on firewood and charcoal for their daily living [58, 62, 63]. Malawi’s forest cover loss is estimated at 2.6% per annum [64]. The middle Shire River catchment lost, on average, about 4.3% of its forest and shrubland annually between 1989 and 2011 (Table 4), suggesting a negative relationship between population increase and the decline in forest coverage (Figures 4 and 5). Results, nevertheless, showed a recovery in forest cover from 2011 to 2015 (Tables 4 and 5), likely attributed to interventions by the government of Malawi and nongovernmental organizations in strengthening natural resource management policies that started around 2008 up to date [5, 65].
Macroeconomic activities such as increase in manufacturing industries and other businesses which contribute to the growth of GDP often require large areas, which also contributed to the transition of forest/shrubland/grassland into buildup areas. Some of such economic activities include opening of new farms which also require clearing of forest areas (Figures 4 and 5).
National policies in the past have failed to effectively enforce ban of unabated harvesting of forest resources until recently with the introduction of community-based natural resource management groups and intervention of some nongovernmental organizations in afforestation programs. This may explain the increase in forest cover from 2011 to 2015 as earlier indicated (Figure 2 and Table 4). Globally, large expanses of forests are being converted into bare land for domestic purposes and, principally, due to harvesting of timber [66]. In a study carried out between 1989 and 2002 in the upper section of the Shire River, [60] reported impacts of LUCC on the river’s catchment hydrological regime which includes increase in soil erosion. It is reported that agricultural land increased by 18% between 1989 and 2002 [60]. In another LUCC assessment study for Likangala River catchment (a stream from Zomba Mountain which is also a source of several rivers draining into the eastern side of the middle Shire River), woodlands decreased from 135.3 km2 in 1984 to 15.5 km2 in 2013 [67]. These results agree with the present study confirming negative impacts of LUCC. Agriculture is the main source of employment to about 92% of the population in Malawi which lives in rural areas [61, 68]. Increase in agricultural activities leads to cultivated land expansion. Cash crops (e.g., tea, coffee, tobacco, and cotton), subsistence crops (e.g., maize and groundnuts), and animal rearing contribute to the increase in agricultural GDP. Results in the present study agree with a report for the region in which land use change (increase in farming activities) contributed to increase in GDP. Similar findings have also been reported correlating land use to increase in income [67]. The increase in cultivated land and artificial surfaces resulted into a decline in forest and shrubland (Tables 4 and 5).
Furthermore, the country loses about 1.7% of its GDP on average annually due to the combined effects of droughts and floods [69]. Heavy rains received during the 1989 season in the country (Figure 3) were associated with devastating floods that drastically affected the GDP due to crop failure and loss of property as well as human life in the same period but increased in the subsequent year (Figure 4). Although the devastating rainfall in the 1989 season played a role in influencing the GDP, other factors could also be at play due to the fact that drivers of economic growth are diverse and vary in the magnitude of influence. For example, in 1989, Malawi’s economy was associated with high fuel prices due to the war in Mozambique. All fuel transportation routes from the Indian Ocean ports in Mozambique were blocked, and consequently, there was a collapse in commodity prices [68]. Poor sales of tobacco which is the country’s major foreign exchange earner also affected the GDP in 1989 [68]. Increased GDP between 2005 and 2009 has been attributed to stabilization and enhanced income growth, which increased income per capita due to the new economic policies and a stable political environment in 2004 [68].
These study findings show a decline in forests and then an increase over the past 26 years (Figures 2 and 5 and Table 4). Clearing of forests from the catchment of the middle Shire River has subjected the bare soil to erosion which finds its way into the Shire River downstream to the Nkula Dam as a sink. This, thus, may explain the heavy siltation at the Dam which has reduced the volume of water causing problems with normal generation of electricity (Figures 4 and 5). The volume of the Dam at Nkula Falls, which was 3 million m3 at its construction in the 1980s, has recently dropped to nearly half of its original size due to massive siltation which consequently resulted in low production of hydroelectricity, now failing to meet the country’s demand for power. Nkula B Hydroelectric Power Station is the main electricity generation plant in Malawi producing about 124 MW of electricity [70]. The electricity-providing company—the Electricity Supply Commission of Malawi (ESCOM)—is now implementing involuntary power load shedding programs resulting into national frequent blackouts. Consumers now resort to excessive use of firewood/charcoal in place of electricity for cooking and other domestic chores creating a heavy dependency on forest resources.
High soil losses in Ntcheu and Neno Districts could be due to increased population as a result of the refugees’ long time settlement in these areas resulting into removal of forests. The expansion of cultivated land could thus be the cause for increased soil erosion and sediment transport downstream, which consequently accumulate in the Nkula Dam in the middle Shire River (Figure 5). These findings agree with a recent study [6] which confirmed that most of the sediments going into the Shire River and finally depositing at the Nkula Dam originate from the western side of the Shire River. Several studies elsewhere [20, 66] also report the same, linking increased population to deforestation and soil. Loss of forests coupled with agriculture are cause for rapid land use change resulting into increased soil erosion and siltation in the middle Shire River catchment [4, 6, 8] (Figure 5). Malawi, and the middle Shire River in particular, is therefore locked up in a cycle where anthropogenic activities in the river’s catchment meant for a survival alternative to lack of electricity have become a cause for soil erosion and siltation in the river, consequently hampering the generation of the needed electricity.
Findings in this study show significant land use and land cover changes that have occurred in the middle Shire River catchment over the past 26 years which have also affected the Nkula Dam. Forestland and shrubland have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the western side of the middle Shire River. Severe siltation downstream in the Nkula Dam appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and GDP, macroeconomic activities occurring especially in the western part of the river such as manufacturing industries, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources.
To solve these problems, there is a need to review and amend weak policies that encourage noncompliance to regulations of managing forests. For example, all policies that may encourage or result in soil erosion such as river bank cultivation must be amended. Powers should be invested in local authorities to take part in protecting the environment and/or in planting trees, and the government should be able to provide seedlings for the operation. This should be done in a competition manner that the village which will perform well should be given some incentives. There is also need to increase fertilizer use so that land expansion for farming is curbed and yields are improved. In addition to that, population growth can be controlled through increase use of family planning. Encouraging children to go to school to avoid early marriages might also help to reduce poverty which will help to avoid cutting down of trees careless. Deliberate programs should be instituted by the government to curb further effects of climate variability such as droughts and floods. Such programs may include good agricultural practices that conserve soil and protect it from water erosion, discourage river bank cultivation, intensify afforestation programs, and ban the burning of charcoal. Findings in this study and the combination of methods used (application of GIS, remote sensing, and analysis of socioeconomic factors) can possibly be applied in areas where similar environmental problems have occurred. It is preferable to include a conclusion(s) section which will summarize the content of the book chapter.
We thank the State Key Laboratory of Estuarine and Coastal Research and Graduate School of East China Normal University (ECNU) for supporting this study. We also appreciate the valuable comments provided by Professor Christo C.P. Van der Westhuizen of North West University (South Africa), Professor Fang Shen of East China Normal University (China), Dr. Mavuto Tembo of Mzuzu University (Malawi), Ms. Lostina S. Chapola of Catholic University (Malawi), Mr. Tanazio Kwenda from the Department of Surveys (Malawi), Mr. Patrick Jambo from Forestry Department of Mzuzu University (Malawi), Mr. Samuel Limbu of the University of Dar es Salaam (Tanzania), Dr. Naziha Mokadem of North West University (South Africa), and the anonymous reviewers who helped us to polish this manuscript.
No potential conflict of interest was reported by the authors.
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