Tuesday, 21 June 2022

Forest Biodiversity Degradation: Assessment of Deforestation in Ohaji Egbema Forest Reserve, Imo State, Nigeria Using GIS Approach by Egbuche Christian Toochi in Open Access Journal of Biogeneric Science and Research

Forest Biodiversity Degradation: Assessment of Deforestation in Ohaji Egbema Forest Reserve, Imo State, Nigeria Using GIS Approach by Egbuche Christian Toochi in Open Access Journal of Biogeneric Science and Research 


ABSTRACT

This research is focused on a spatial analysis of a reserved forest deforestation over a period of time using a GIS approach in Ohaji Egbema Local Government Area Imo state, Nigeria. It aimed at assessing and analyze deforestation in Ohaji Egbema forest reserve and examined the possible effects of deforestation on the forest environment. The assessment concentrated on when and where have forestlands changed in the reserved forest programmed within the period of 1984 - 2040 forecast. The key objectives were to assess the impact of land use and land cover changes on forest cover for the past 36 years, while sub objectives were dedicated to achieve in mapping out different land cover in Ohaji Egbema forest reserve, to assess land cover changes in the forest reserve susceptible to long term degradation from 1984 to 2020 of about 20 years. To evaluate forest loss in the area for the past 36years, and to predict the state of the land cover (forest) for the next 20 years (2040). Primary and secondary data employed using (200 ground truth points) were systematically collected from four different LULC classes in the study area using geographical positioning system (GPS), the secondary data (Satellite Landsat Imageries of 1984, 2002 and 2020) of the study area was acquired. The imageries were processed, enhanced and classified into four LULC classes using supervised classification in Idrisi and ArcGis software Ground truth points were utilized to assess the accuracy of the classifications. The data collected was analyzed in tables and figures and represented with a bar chart and pie chart graphs. Results showed that forest land, built up, grassland and water body were the four LULC classified in the study area. Kappa coefficient values of 91%, 85% and 92% for 1984, 2002 and 2020 respectively shows the accuracy of the classifications. Classifying the land uses into built-up and forest lands revealed that the built-up lands constantly rose while the forest lands kept dropping. The built-up lands increased by 49.30% between 1984 and 2000, 50.00% between 2002 and 2020 and 28.40% between 2020 and 2040 at the expense of the forest portion of the area which fell by 33.88% between 1984 and 2000,46.45% between 2002 and 2020, and 49.22% between 2020 and 2040. Increase in population, per capita income, and land use activities and by extension urban expansion were found to be the major factors causing deforestation in the forest reserve, it is likely that in the nearest future the remaining forest lands would be gradually wiped out and consequently the environmental crisis would be aggravated. Based on the findings of the study, there is need to urgently limit and control the high rate of deforestation going on in Ohaji Egbema forest reserve and embark on tree replanting campaigns without delay. There is need and recommended that a higher quality satellite imagery that offers up to 4m resolution should be used and a forest relic analysis should be conducted.

KEYWORDS: Biodiversity; Forest degradation; GIS; Forest Reserve; LULC; Deforestation and Satellite Imagery

Introduction

Deforestation constitutes one of the serious threats to forest biodiversity and pose a global development challenges of long-term environmental problem at both regional level and the world at large. According to [1] and [2] the degradation of the forest ecosystem has obvious ecological effects on the immediate environment and forested areas. Deforestation can result in erosion which in turn may lead to desertification. The economic and human consequences of deforestation include loss of potential wood used as fuel wood for cooking and heating among others. The transformation of forested lands by human actions represents one of the great forces in global environmental change and considered as one of the great drivers of biodiversity loss. Forests are cleared, degraded and fragmented by timber harvest, conversion to agriculture, road-construction, human-caused fire, and in myriad of other ways of degradation. According to [5], deforestation refers to the removal of trees from afforested site and the conversion of land to another use, most often agriculture. There is growing concern over shrinking areas of forests in the recent time [7]. The livelihoods of over two hundred million forest dwellers and poor settlers depend directly on food, fibre, fodder, fuel and other resources taken from the forest or produced on recently cleared forest soils. Furthermore, deforestation has become an issue of global environmental concern, in particular because of the value of forests in biodiversity conservation and in limiting the greenhouse effect [8]. Globally, deforestation by this trend has been described as the major problem facing the forest ecosystem. The extent of deforestation in any particular location or region can be viewed from economic, ecological and human consequences as well as scramble for land. Forest degradation may in many ways be irreversible, because of the extensive nature of forest degradation which the impact of activities altering their condition may not be immediately apparent and as a result they are largely ignored by those who cause them. Forest is often perceived as a stock resource and always and freely available for conversion to other uses without considering the consequences for the production services and environmental roles of the forest. As environmental degradation and its consequences becomes a global issue, the world is faced with the danger that the renewable forest resources may be exhausted and that man stands the risk of destroying his environment if all the impacts of deforestation are allowed to go unchecked. It becomes therefore important to evaluate the level of deforestation and degradation in Ohaji Egbema forest reserve using a GIS application. The effect of deforestation and degradation of the only forest reserve in South east Nigeria has recently become a serious problem. It has been identified that in the area is mostly the quest for fuel wood, grazing and for agricultural use. One of the effects of deforestation is global warming which occurs as a result of deforestation as trees uses carbon dioxide during photosynthesis. Deforestation leads to the increase of carbon dioxide in the environment which traps heat in the atmosphere leading to global warming. I become very objective to assess the impact of land use and land cover changes on forest cover for the past 36 years with further interest to map out different land cover in the Ohaji Egbema forest reserve, assess land cover changes from 1984 to 2020 at 20 years, evaluate forest loss in the area for the past 36 years and make a prediction of the state of the land cover (forest) for the next 20 years (2040). It is known that deforestation and degradation of the forest has posed a serious problem especially at this era of global climatic change.

United Nations Food and Agricultural Organization [12], deforestation can be defined as the permanent destruction of forests in other to make the land available for other uses. Deforestation is said to be taking place when forest is being cut down on a massive scale without making proportionate effort at replanting. Also, deforestation is the conversion of forest to an alternative permanent non-forested land use Such as agriculture, grazing or urban development [5]. Deforestation is primarily a concern for the developing countries of the tropics [6] as it is shrinking areas of the tropical forests [3] causing loss of Biodiversity and enhancing the greenhouse effect [8]. Forest degradation occurs when the ecosystem functions of the forest are degraded but where the area remains forested rather cleared [9]. Available literatures shows that the causes of forest deforestation and degradation are caused by expansion of farming land, logging and fuel wood, overgrazing, fire/fire outbreak, release of greenhouse gases and urbanization/industrialization as well as infrastructural provisions. More so agents of deforestation in agricultural terms include those of slash and burn farmers, commercial farmers, ranchers, loggers, firewood collectors etc. Generally, the center of biodiversity and conservation (CBC 1998) established the remote sensing and geographic information system (RS/GIS) facilities as technologies that will help to identify potential survey sites, analyze deforestation rates in focal study areas, incorporate spatial and non-spatial databases and create persuasive visual aids to enhance reports and proposals. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times [11]. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution of the population of interest.

Study Area

Ohaji Egbema lies in the southwestern part of Imo state and shares common boundaries with Owerri to the east, Oguta to the north Andogba/Egbema/Ndoni in Rivers state in the southwest. The 2006 census estimated the study area to over 182,500 inhabitants but recently due to industrialization and urbanization, Ohaji/Egbema has witnessed a great deal of population influx. The study area lies within latitudes 50 11’N and 50 35’N and longitudes 6037’ ad 6057’. It covers an area of about 890km2.

The study area is largely drained by the Otammiri River and other Imo river tributaries. The study area belongs to a major physiographic region- the undulating lowland plain which bears a relationship with its geology. The low land areas are largely underlain by the younger and loosely consolidated Benin formation [12]. The vegetation and climate of the study area has been delineated to have 2 distinct seasons both of which are warm, these are the dry and rainy season.

Climate and Vegetation

The dry season occurs between November and March, while the rainy season occurs between April and October. The high temperatures, humidity and precipitation of the area favour quick plant growth and hence vegetation cover of the area that is characterized by trees and shrubs of the rainforest belt of Nigeria.
Geology and Soil

The study area is located in the Eastern Niger delta sedimentary basin, characterized by the three lithostratigrapgic units in the Niger delta. These units are – Akata, Agbada and Benin formation in order of decrease in age [13]. The overall thickness of the tertiary sediments is about 10,000 meters.
 

Method of Data Collection

Data are based on field observation and from monitoring the real situation, they are collected as fact or evidence that may be processed to give them meaning and turn them into information in line with [14]. Heywood (1988). Geographical Positioning System (GPS) was used to collect fifty (50) coordinate points at each land use land cover, totaling 200 points for the four major land use and land cover identified in the study area. Landsat Imageries of one season (path 188, 189 and row 56) were acquired from United State Geological Survey (USGS) in time series; 1984 Thematic Mapper (Tm), 2002 Enhanced Thematic Mapper (ETM) and 2020 Operational Land Imager (OLI) as shown in the table one below table 3.

 Data Analysis and Data Processing

The acquired landsat imageries were pre-processed for geometrical corrections, stripes and cloud removal. Image enhancement was carried out on the acquired imageries employing bands 4, 3, 2 for LANDSAT TM and ETM while bands 5, 4, 3 for LANDSAT OLI/TIRS to get false colour composite using Idrisi and Arcgis softwares. In the resultant false colour composite, built up areas appear in cyan blue, vegetation in shades of red differentiating dense forest and grass or farm lands, water bodies from dark blue to black, bare lands from white to brown [15]. This was necessary to enhance visualization and interpretability of the scenes for classification. The study area was clipped out using administrative map of Nigeria containing Imo State and Local Government shape files in Arc map (Table1 and table 2).

Table: 1. Details of Landsat Imageries Dataset used

Table: 2. Change Observed between (1984 – 2002).

Table: 3. Change Observed between (2002 – 2020).

Land Use Land Cover Classification

The false Colour composite images were subjected to supervised classification which was based on ground-based information. Maximum likelihood was adopted to define areas of Landsat images that represented thematic classes as determined by maximal spectral heterogeneity according to [16]. Maximum likelihood algorithm considers the average characteristics of the spectral signature of each category and the covariance among all categories, thus allowing for precise discrimination of categories. Hence the land covers were classified into four major land use land cover classes: Built up, forest cover, grass cover and water body. Forest vegetation are the areas dominated by trees and shrubs; grass land are the areas dominated by grasses, including farm lands and gardens; water body are the areas occupied by streams, rivers, inland waters; while built-up areas are the areas occupied by built structures including residential, commercial, schools, churches, tarred roads and those land surface features devoid of any type of vegetation cover or structures including rocks. Four applications (ArcGis 10.5, Idris software, Excel and Microsoft word) were also applied in this study.

Accuracy Assessment of The Classification

The aim of accuracy assessment is to quantitatively assess how effectively the pixels were sampled into the correct land cover classes. Confusion matrix was used for accuracy assessment of the classification procedure in accordance with the training samples and the ground truth points as a reference point. This approach has also been adopted effectively in similar studies by [17]; [18]. The accuracy assessments of the classified maps for 1984, 2002 and 2020 were evaluated using the base error matrix. The base error matrix evaluates accuracy using parameters such as agreement/accuracy, overall accuracy, commission error, omission error and the Kappa coefficient. The agreement/accuracy is the probability (%) that the classifier has labeled an image pixel into the ground truth class. It is the probability of a reference pixel being correctly classified. The overall accuracy specifies the total correctly classified pixels and is determined by dividing the total number of correctly classified pixels by the total number of pixels in the error matrix. Commission error represent pixels that belong to another class but are labeled as belonging to the class; while the Omission error represent pixels that belong to the truth class but fail to be classified into the proper class. Finally, the Kappa coefficient (Khat) measures the agreement between classification map and reference data, as expressed below:

kappa coefficient t= (Observed Accuracy-Chance agreement)/ (1-Chance agreement)

It is stated that Kappa values of more than 0.80 (80%) indicate good classification performance. Kappa values between 0.40 and 0.80 indicate moderate classification performance and Kappa values of less than 0.40 (40%) indicate poor classification performance [19].

Change Detection Analysis

Spatio-temporal changes in the four classified land use classes for the past 36 years were analyzed through comparison of area coverage of the classified maps. Change detection was carried out in each of the classes to ascertain the changes over time in terms of area and percentage coverage according to [18]. This was done by computing the area coverage for each of the feature class in each epoch from the classified images in idrisi and Arcmap softwares following the expression below:

Area (m2) = (Cell Size x Count)/10,000

Percentage cover (%) = Area/ (Total) x100

Where cell size and count were gotten from the properties of the raster attributes.

The extent of land use land cover over change, land use encroachment as well as gains and losses experienced within the study period were analyzed and presented in maps and charts.

Prediction Analysis

The classified land use imageries were subjected for Land Change Modeling in idrisi software using Cellular Automata and Markov Chain algorithm for prediction. Then land cover scenario under prevailing conditions for the year 2040 was modeled table 6.

Table: 4. Change Observed between (2020 - 2040).

Table: 5. 1984 Land use land cover classification accuracy

 Table: 6. 2002 Land use land cover Classification Accuracy

Table: 7. 2020 Land use land cover classification accuracy

RESULTS AND DISCUSSION LAND COVER LAND USE CHANGES LASSIFICATION FROM 1984-2040

The result of the work is presented starting with the land use and land cover classification in the years 1984,2002,2020 and 2040 presented in Figures 4.1,4.2,4.3 and 4.4 below. Dark green colour represents forest vegetation, light green represents grass lands, blue represent water body, while orange colour represent built-up area. In figure 4.1 which is the LULC classification of 1984 shows that the study area is largely covered with deeply dark green which is forest vegetation, patches of scattered light green and orange colour which is grass land and built-up while the blue colour which is the water bodies covers a little part of the study area. This depict that the study area was more of forest vegetation in 1984 table 7.

Figure:

In figure 4.2, it is observed that dark green colour is reduced, there was a slight increase in blue colour, and there was a slight reduction inlight green colour, while the orange colour was at an increase rate mainly at Obofia, Awarra, Amafor, Ohoba, Umukani, Ohaji Egbema forest reserve and Adapalm axis. This indicated that by the year 2002, reasonable forest lands were deforested and converted to residential, commercial, Agricultural and other land uses, and this could be attributed to infrastructural development, urbanization, industrialization and human population increase in the area which is the causes of deforestation table 4 and table 5.

Figure: 2. The land use land cover classification of the year 1984

In fig 4.3, deforestation continued. There were more of orange colours and light green colors were observed more at Umukani and Umuakpu axis in the map, while the dark green colour is gradually decreasing and it’s been observed that the blue colour was rarely seen in the map. These indicated that as the years passes by, there are more built ups, and grass lands while the forest land is gradually degraded and used for built-up and agricultural purposes.

 

Figure: 3. The land use land cover classification of the year 2002

In figure 4.4 below, the whole map is mostly covered with orange colour, with scattered patches of light green, and dark green colour being deforested, while the blue colour is hardly seen in the map. This shows that the forest land cover has been on the regular decrease, while built ups, grass lands have been on the regular increase.

Figure: 4. Land use land cover classification of 2020

Area coverage, percentage cover and change detection land use and land cover 1984 - 2040

The area coverage and percentage cover of different land use classes are represented below. It is observed that the forest land covers about 723.26km2 with a percentage cover of 81.31% which was the major land cover of the study area in 1984. This implies that more than half of the study area was under forest cover in 1984, In the areas of built-up, it covers about 128.40km2 in 1984 with a percentage cover of 14.43%. Areas covered by grass land (either by sparse vegetation, farmland or grasses) was at minimal in 1984 with an area cover of 32.57km2 and percentage cover of 3.66%, while the water bodies cover an area of 5.34km2 and a percentage cover of 0.6%.

Figure: 5. The land use land cover classification of 2040

Figure 4.7 and 4.8 below show the area coverage and percentage coverage of the year 2002. As time goes on, the forest land decrease from area coverage of 723.26km2 in 1984 to 699.68km2 in 2002, with a percentage cover of 81.31% in 1984 to 78.68% in 2002 which depict that the forest cover was at a loss while the built–up was at increase from 128.40km2 to162.46km2 and a percentage cover of 18.26%. Areas covered by grass land gradually decrease to 21.41km2 with a percentage cover of 2.41% in 2002, and a slight increase of the water bodies from an area cover of 5.34km2 to 5.80km2 with a percentage cover of 0.65%.

Figure: 6. 1984 Area coverage of different land use classes

Figure: 7. Percentage cover of 1984 LULC Classification

In the year 2020, it is shown that the forest land covers about 589.73km2 with a percentage cover of 66.30% which shows that there was a decrease within 2002 to 2020 while in the area of built up it increased to an area cover of 280.98km2 with a percentage cover 31.59%. An area covered by grass land covers about 15.53kmwith a percentage cover of 1.75% and the water bodies cover about 3.27km2 and a percentage cover of 0.37%. This shows that the built-up area which is at increase were initially forest lands and water bodies in the past years.

Figure: 8. 2002 Area coverage of different land use classes

Figure: 9. Percentage covers of 2002 LULC Classification

In 2040 it was predicted that the forest cover about 497.67km2 with a percentage cover of 55.95%, the built up was predicted to be on the increase with an area cover of 334.11km2 and a percentage cover of 37.56%. Area covered by grassland was at predicted to be on increase with an area cover of 55.92km2 and a percentage cover 6.29% and this grass land was formally forest land in the past years and this change occurred mainly at Ohoba, Awarra, Umuakpu, Umukani and Ohaji Egbema forest reserve the only forest reserve in the south east of Nigeria which has been deforested and use for agricultural purposes. The water bodies cover about 1.82km2 with a percentage cover of 0.20%. This implies that the forest land has been deforested and degraded to other land uses in the study area within the study periods. All this are shown in figure 4.11 and figure 4.12.

Figure: 10. 2020 Area coverage of different land use classes

Figure: 11. Percentage cover of 2020 LULC Classification.

Change Detection Observed Between (1984-2040)

Approximately the change detected in the forest land from 1984 to 2002 in the area coverage is 23.41km2 with a percentage change of 33.88% which shows it was at decrease, the built up from 1984 to 2002 the change detected in area is -34.06km2 with a percentage change of 49.30% which is at increase while in the area of grassland, the change observed is 11.16km2 with a percentage change of 16.15%. The change detected in the area coverage of water bodies from 1984 to 2002 is -0.46km2 with a percentage coverage of 0.67% which was at increase.

The change detection observed in table 2 below shows that between 2002 to 2020 the forest cover was at a high decrease with about 110.12kmof change observed and a percentage change of 46.45% and the built up was about -118.53km2 and a percentage change of 50.00% which shows there was an increase in the area. The change observed in the grass was at 5.88km2 with a percentage cover of 2.48% which is at a decrease while for the water bodies the change observed is 2.53km2 with a percentage change of 1.07% which implies that the area of built up has been on the high increase over other land classes.

In the 2020 to 2040 change detection table shows that there was more of built up in the study area which is observed to cover about -53.12km2 with a percentage change of 28.40% which shows that the built up was at increase, and the forest cover was at decrease with the change observed at 92.06km2 and a percentage change of 49.22%.Also the grassland was detected to be on a increased with about -40.39km2 with a percentage change of 21.60% and the water bodies was at a decrease with the change detected at the study period to be about 1.45km2 and a percentage change of 0.78% which implies that the water body has been lost to other land uses.

Figure: 12. Predicted 2040 Area coverage of different land use classes

Figure: 13. Predicted Percentage cover of 2040 LULC Classification

Land Use Land Cover Classification Accuracy

The result of the accuracy assessment for 1984, 2002 and 2020 were presented in table 4 to table 6below. The overall accuracy for 1984 classification was 0.94%, with overall kappa accuracy of 91%, in 2002 classification, overall accuracy 0.89%, kappa accuracy was 85%, in 2020 classification, overall accuracy was 0.94%, kappa accuracy was 92%.

Figure: 14. General view of Area Coverage 1984 – 2040

Discussion

Findings from the study showed that land cover of the study area has been heavily deforested and degraded within the study period 1984, 2002 and 2020, which will continue if control measures are not taken into consideration. Forest land was on the decrease while built ups and grass lands were on the increase. This is in line with other findings of [16] and [23] and [30]. These outrageous changes in the origin all and cover in the study area could be linked to human population, unsustainable human activities in the study area as well as unsustainable environmental management practices and weak environmental policies. As the human population increases, more lands were needed for settlements and many other commercial activities, which gradually led to rapid industrialization, infrastructural development and urbanization. Increase in human population could also increase the levels of anthropogenic activities such as deforestation, intensive farming and sand mining. In other words, the large spread of forest land in 1984 could be linked to low population and productivity, less socio-economic activity. The forest lands have been drastically reduced to build-ups and other land uses in the study area, without consideration to the many environmental needs that forest provides. Hence loss of biodiversity, land degradation, noise pollution, air pollution and climate change could be rooted to changes in the land cover. It can observe that in the past two centuries the impact of human activities on the land has grown enormously, altering entire landscapes, and ultimately impacting the earth's nutrient and hydrological cycles as well as climate. The classification accuracy for the 3 years represents strong agreement. According to [31] values between 0.4 and 0.8 represent moderate agreement, values below 0.4 represent poor agreement and values above 0.81 represent strong agreement.

CONCLUSION AND RECOMMENDATION

In this study, four land use land cover classes were identified as they change through time. However, the result shows a rapid change in the vegetation cover of the study area between 1984 to 2040. Within this period, 225.59km2 of forest land areas and 3.52km2 of water body were lost and converted to other land uses in the study area. Whereas built up and grassland was at increase covering part of the forest and water body. However, if these patterns of degradation continue in the study area, it is likely that in the nearest future the remaining forest land would be wiped out and environmental crisis would be aggravated. Therefore, the assessment of the level of deforestation in Ohaji Egbema using GIS is thus a vital tool for sustainability of the forest management and environmental planning of the area especially at the only forest reserve in the South east, Nigeria.

 

Based on the findings, there is need to urgently limit and control the high rate of deforestation going on in Ohaji Egbema and embark on tree planting campaigns without delay. It is also recommended that an Environmental Impact statement (EIS) should be carried out. Furthermore, policy makers should ensure that the existing/future polices with regard to environmental and forest degradation is utmost implemented. There is need to create an awareness programme for all stakeholders on the issues at hand and the need to adopt sustainable use of natural resources, sustainable living habits and minimizing impact on the environmental. Finally, [2] having conducted species relics in this forest reserve further research should be conducted on higher quality satellite imagery that offers up to 4m resolution within as well as forest relic analysis.

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Tuesday, 24 May 2022

The Advantages of Peritoneal Dialysis over Hemodialysis by Sami Bahçebaşi* in Open Access Journal of Biogeneric Science and Research

The Advantages of Peritoneal Dialysis over Hemodialysis by Sami Bahçebaşi* in  Open Access Journal of Biogeneric Science and Research 


Abstract

Although kidney transplantation is the definitive treatment of kidney failure, for many reasons, most of patients need dialysis treatment. Despite significant advantages of PD over HD, HD commonly used modality all over the world. PD patients have better survival relative to HD patients in the first year of dialysis. A recent study has shown that the risk of all-cause mortality and infection death were higher in the unplanned HD group than in the unplanned PD group during 1-year follow up. Because of prolonged and gentle removal of body fluids and toxins ,PD reduces the risk of hemodynamic instability. Therefore, PD may be preferred in the treatment of critically ill patients with acute kidney injury (AKI) or end-stage renal disease (ESRD).

In a study compared PD with continuous renal replacement therapy in critically ill patient with AKI. Patients in the PD group had lower mortality rate at 28 days, less complication of infections and faster recovery of renal function. Therefore, PD can be applied effectively and safely in critically ill patients with AKI and requiring dialysis. PD confers better quality of life (autonomy, flexibility, avoidance of regular hospital visits for the patients and relatives, travel easily, more free time). PD can be done while sleeping, this allowing patient to travel and do daily activities.

PD preserves the residual renal function over HD. PD has freedom of pain in vascular access sites from repeated cannulation for HD and preserves this sites for the future HD. PD has significant economic advantage over HD globally. There is no need for a large number of healthcare workers, dialysis machines, protective equipment and clean water in PD.  A meta-analysis has shown that pretransplant PD is associated with post-transplant survival benefit than pretansplant HD. We recommend considering these advantages when choosing a dialysis modality.

Chapter

Chronic kidney disease (CKD) is defined as an estimated glomerular filtration rate less than 60 ml/min/1.73 m² or presence of kidney damage persisting for 3 months or more. In the general population CKD prevalence is around 10 % to 14%. Progressive loss of kidney function to end-stage (GFR lens than 15ml/min/1.73 m²) resulting in the need for renal replacement therapy, irrespective of the cause.  Renal transplantation is the best treatment option of end-stagerenaldisease (ESRD).  Because renal transplantation has survival benefit compared to long-termdialysis therapy.  When eGFR is less than 20ml/min/1.73 m² the patients must to be listed for renal transplant program [1].

Only 25% of patients with kidney failure undergo renal transplantation and complete their life without dialysis.  This is due to many reasons, which include a unexpected diagnosis of kidney failure, non-availability of a kidney donor, unsuitability for renal transplantation and failed renal transplantation. There are two form of dialysis modality; Hemodialysis (HD) and peritonealdialysis (PD).  The choice of dialysis modality is stil controversial. HD commonly used modality all over the world.  However, preferance for PD is increasing due to the various advantages with PD [2]. PD patients have beter survival in the first year of dialysis compared to HD.  In a cohort study, 45165 patients who started unplanned dialysis and planned dialysis were followed for 1 year.  The primary outcomes of the study were death from infection and all-cause mortality during 1-year followup.  The risks of all-cause mortality and infection death were lower in planned PD group than unplanned PD group.  Major cardiac and cerebrovascular events and all-causere admission were lower in planned PD group than unplanned PD group.

This result shows us the importance of timely planning dialysis. The risk of death due to infection and death from all causes was also found to be lower in the unplanned PD group than in the unplanned HD group Major cardiac and cerebrovascular events and all-causere admission were lower in unplanned PD group than unplanned HD group [3].  These results shows that PD is superior to HD in patients undergoing unplanned dialysis. PD reduces the risk of hemodynamic instabiliy, due to prolonged and gentle removal of body fluids and toxins. Therefore, PD a treatment of choice for critically ill patients with renal failure.  In a prospective study compared Continious Renal Replacement Therapy (CRRT) with PD in critically ill patients with acute kidney injury. 63 CRRT and 62 PD patients were included in the study. Primary outcome was hospital mortality at 28 days, secondary outcomes were infectious complication, recovery of kidney function, median time to resolution of AKI and the median duration of ICU stay of 9 days vs19 days.  Infectious complications significantly less and survival at 28 days significantly better in the patients treated with PD when compared to CRRT.  Recovery of kidney function, median time to resolution of AKI and the median duration of ICU stay of 9 days vs 19 days were all in beter for PD.  Chronic dialysis requirement was found to be the same in both groups [4]. Therefore, PD can be applied effectively and safely in critically ill patients.

PD better preserves residual renal function compared to HD [5]. Several mechanisms play role. The most important one is the less abrupt fluctuations in osmotic load and volume.  These leading to more stable hemodynamic status [6]. Pretransplant PD is associated withpost-transplant benefit than pretransplant HD. In a cohortstudy 1209 pretransplant HD and 603 pretransplant PD patients included.  Recipients with chronic PD patients has significantly higher infection risks in urinary trakt infection and peritonitis during hospital isation for kidney transplantation.  On the other hand, there was no significant difference between groups in hospitalisation duration for kidney transplantation.  Compared with patients with PD, new onset ischemic heart disease, tuberculosis and hepatit C all higher recipients with chronic HD during follow-up period of 90 days after kidney transplantation. There was no significant difference regarding all-cause mortality between the HD and PD groups. However, the graft survival probability was significantly higher in PD than HD group after 10 years of kidney transplantation follow-up period [7].

In a meta-analysis has shown that pretransplant PD is associated with post-transplant survival benefit than pretransplant HD.  A total of 16 studies included.  6 studies reported mortality benefit of PD.  There was no significant difference in graft survival between two groups [8].  These studies shows that PD may be preferred primary dialysis modality for patients who are considering transplantation. PD confers better quality of life.  PD patients have more free time due to autonomy, flexibilty, avoidance of regular hospital vizits. PD has freedom of pain in vasculary access sites from repeated cannulation for HD and preserves this site for the future HD.  PD can be done while sleeping. Because of these advantages, PD patients can travel easily and do their daily activities [2].

PD has significant economic advantages over HD. In a study cost-effectiveness of PD and HD compared. 4285 parsel of HD and PD patients were follow-up 14 years. Estimated life expectancy between HD and PD found nearly equal, whereas average lifetime healtcare costs were higher in HD than PD [9]. The reasons for this ; there is no need for a dialysis machines, clean water, protective equipment and large number of healtcare workers. The choice of dialysis modality can be affected by many different factors; such as patients selection, approach of the healtcare facility, physician opinion and accessibility to equipment etc. However, we recommend that these advantages of PD, which are described throughout our article, should be taken into account in the selection of dialysis modality.

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Tuesday, 26 April 2022

Wilms’s Tumor Gene Mutations: Loss of Tumor Suppresser Function: A Bioinformatics Study by Uzma Jabbar in Open Access Journal of Biogeneric Science and Research

Wilms’s Tumor Gene Mutations: Loss of Tumor Suppresser Function: A Bioinformatics Study by Uzma Jabbar in Open Access Journal of Biogeneric Science and Research 


ABSTRACT

Introduction: Mutation in the Wilms’s Tumor (WT1) gene product has been detected in both sporadic and familial cases suggesting that alteration in WT1 may disrupt its normal function. The study aims to find the protagonist amino acid in WT1 proteins by mutating these residues with other amino acids.

Material and Methods: The 3D modeling approach by MODELLER 4 was utilized to build a homology of WT1 proteins. Quality of the WT1 model was verified by predicting 10 models of WT1 and hence selecting the best one. Stereochemistry of model was evaluated by PROCHECK. Mutational studies were done by WHAT IF. Five human WT1 mutations were modeled which were Lys371→Ala371, Ser415→Ala415, Cys416→Ala416, His434→Asp434 and His434→Arg434.

Result: Based on active side of WT1 protein and its role in DNA binding mutation. No significant change was observed when Lys371 was mutated to Ala371, Ser415 was mutated to Ala415. Significant change was observed in Cys416 mutated to Ala416. In mutant Ala416, loss of coordination with the metal ion Zn was also predicted. In case of Mutants His434→Asp434, there was a loss of coordination of metal ion (Zn203) with mutant Asp434. In case of mutant His434→Arg434, there was a loss of Zn203 coordination with Arg434. His434 does not interact directly with any DNA base, whereas mutated Arg434 is predicted to interact directly with DNA base.

Conclusion: It is concluded that mutation of amino acid residue Cys416→Ala416, His434→Asp434 and His434→Arg434 may lose the proto-oncogenic function of WT1.

 

Keywords: WT1 protein, MODELLER9.0, Mutation, Active side residues

Introduction

WT1, is a protein, which in humans is encoded by the WT1 gene on chromosome 11p13. The WT1 is responsible for the normal kidney development.  Mutations in this gene are reported to develop tumors and developmental abnormalities in the genitourinary system. Conversion of proto-oncogenic function to oncogenic in WT1 has also been documented cause of various hematological malignancies. (***)

Multifaceted protein of WT1 gene has transcriptional factor activity [1]. It regulates the expression of insulin-like growth factor and transforming growth factor system, implicated in breast tumorigenesis [2]. A main function of WT1 is to regulate transcription, which control the expression of genes involved in the process of proliferation and differentiation [3]. In wide range of tumor, WT1 is shown to be predisposing factor for cancer, therefore it has become hot target in research to find out it’s inhibitor which can be safely used as a treatment of cancer. It can induce apoptosis in embryonic cancer cell, presumably through the withdrawal of required growth factor survival signal [4]. WT1 is involved in the normal tissue homeostasis and as an oncogene in solid tumors, like breast cancer [5]. Increased expression of WT1 is related with poor prognosis in breast cancer6. A number of hypotheses are postulated for the relationship of WT1 with tumorigenesis. Acceding to one of the hypothesis, elevated levels of WT1 in tumors may be related with increased proliferation because normally WT1 have a role with apoptosis [7,8]. Another study proposed that WT1 can alter many genes of the the family of BCL2 [9,10] and also have a role to regulate with Fas-death signaling pathway [11]. Furthermore, it is suggested that WT1 can encourage cell proliferation by up-regulation of protein cyclin D1 [12].

A group of workers hypothesized that WT1 has been observed in the vasculature of some tumour types [13]and its expression may be related with angiogenesis especially in endometrial cancer [14]. Another hypothesis based on the fact that WT1 is a main regulator of the epithelial/mesenchymal balance and may have a role in the epithelial-to-mesenchymal transition of tumor cells [3]. Expression of WT1 is higher in estrogen receptor (ER) positive than in ER negative tumors. It is therefore possible that WT1 not only interact with ER alpha, but it may orchestrate its expression [15]. A study, on triple negative breast cancers [7] has shown that high WT1 levels associate with poor survival due to increased angiogenesis [16,17], altered proliferation/apoptosis10,11, and induction of cancer- epithelial-to-mesenchymal transition4. In breast tumors, WT1 is mainly related with a mesenchymal phenotype and increased levels of CYP3A4 [18]. A mutation in the zinc finger region of WT1 protein has been identified in the patients that abolished its DNA binding activity [19]. A study also observed that the mutation in the WT1 gene product has been detected in both sporadic and familial cases suggesting that alteration in WT1 may disrupt its normal function [20].  Bioinformatics approaches are being utilized to resolve the biological problems. Efforts start with the prediction of 3D structures. To achieve the aim, study was designed to view 3D structure of WT1, tumor suppressor protein predicted by homology modeling and to study the role of crucial residues in WT1 proteins by mutating these residues with other amino acids.

Material and Methods

3D structure of WT1 was taken as target of human WT1. Figure 1 shows the normal interaction of WT1 with DNA strands based on the crystal structure of a zinc finger protein.

Figure 1: Homology model of the C-terminal fragment of Human Wilms Tumor protein with bound DNA strands based on the crystal structure of a zinc finger protein, znf268, (PDB; id:1aay) and a five-finger protein, GLI (PDB; id:2gli).

The binding of protein-DNA complex involves four zinc finger binding domain in the C-terminal/Zn finger region of WT1. These are;

  • Cys325, Cys330 and His339, His343: figure 2; yellow highlighted.
  • Cys355, Cys360 and His373, His377: figure 3; yellow highlighted.
  • Cys385, Cys388 and His397, His401: figure 4; yellow highlighted.
  • Cys416, Cys421 and His434, His435: figure 5; yellow highlighted.

Figure 2

Figure 3

Figure 4

Figure 5

The 449 amino acid sequences of WT1 were used for homology modeling. Sequences of WT1 were retrieved from Swiss Prot Data Bank in FASTA format [21]. The best suitable templates were used for 3D-structure prediction. The retrieved amino acid sequences of WT1 were subjected to BLAST [22]. Templates were retrieved on the base of query coverage and identity. The 3D structures were predicted by MODELLER 9.0 [23] that is the requirement of 3D structure building of target protein. Tools including stereochemistry and Ramchandran plots were used for the structure evaluation [24]. Identification of Template was carried out, and Sequence Alignment was carried out by using FASTA, BLAST. Quality of the WT1 model was verified. Stereochemistry of model was evaluated by PROCHECK [25]. Mutational studies were done by WHAT IF [26]. Five human WT1 mutants are modeled. These were: Lys371→Ala371, Ser415→Ala415, Cys416→Ala416, His434→Asp434 and His434→Arg434.

Results and Discussion

The study was largely based on active side of the WT1 and its role in DNA binding mutation. Zinc finger binding domain interact selectively and non-covalently. This zinc finger-binding domain is the classical zinc finger domain, in which two conserved cysteine and histidine co-ordinate a zinc ion at the active site.

Cys416®Ala416 MUTANT

Significant change was observed in Cys416 mutated to Ala416. In mutant Ala416, reduction in the Van der Waal’s contact between the amino acids. Loss of coordination with the metal ion Zn was also predicted (Figure 6 A and B).

                                                       Figure 6A                                                                   Figure 6B

Figure 6 A and B: Wild Type (Cys416) and mutated (Ala416) WT1.  Distance between Zn and Cys416 is increased in mutated (Ala416) model. Cys416 is predicted to be found in the vicinity of His434 and His438 which are implicated in catalysis (6A) while Ala416 can only interact with His434 and not with His438 in the mutated model (6B).

Cys416 is located at the domain interface with its polar side chain completely buried (0.00 Å). Replacement of this amino acid may account for considerable changes in the interior of protein (Table 1). We have predicted the possible changes that arise due to the mutation of Cys to Ala by molecular modeling experiments. Amino acids, Pro419, Ser420, Cys421, His434 and some atoms of His438 (ND1, NE2, CD2 and CE1) are present near Cys416. Zinc (Zn203) is also present in the vicinity (1.82 Å) of Cys416 (Figure 6). The mutated residue, Ala is also predicted to remain buried (0.00 Å) in the interior of protein. Significant change is observed however, in the surrounding area of the mutated Ala416. Only a few atoms of His434 (CD2 and NE2) and His438 (CE1) were seen in the surrounding. This may reduce the Van der Waal's contacts between the respective amino acids. The loss of coordination with the metal ion, zinc was also predicted as the distance is increased from 1.82 Å to3.12 Å.   It is therefore predicted that Cys416 plays a vital role in the interaction with other amino acid residues as well as in the metal coordination. It is observed that there is a possibility of loss of these interactions in case of Cys416 replacement.

His434®Arg434 MUTANTS

In case of mutant His434→Arg434, there was a loss of zn203 coordination with Arg434.  His434 does not interact directly with any DNA base, whereas mutated Arg434 is predicted to interact directly with DNA base, A1. This suggests that change might effect on the DNA binding pattern, Figure 7 A and B.

                                                       Figure 7A                                                                   Figure 7B

FIGURE 7 A and B: Wild Type (His434) and mutated (Arg434) WT1.  Distance between Zn and His434 is increased in mutated model. Arg434 is predicted to bind DNA base A1 (B) while His434 in the original model (A) show no bonding with DNA base.

In case of mutation of His434®Arg434, the distance between the mutated Arg and zinc (Zn203) was increased from 2.28 Å to5.00 Å suggesting that there could be a loss of coordination with the metal ion. Mutational studies proved that hydrogen bonding network close to the zinc-binding motif plays a significant role in stabilizing the coordination of the zinc metal ion to the protein23. The mutated amino acid, Arg434 also moved considerably form buried to relatively exposed environment (2.28 Å to 5.35 Å). Presence of positively charged Arg on the surface could account for additional interaction of the protein with other proteins or with the surrounding water molecules. His434 does not interact directly with any DNA base whereas mutated Arg434 is predicted to interact directly with DNA base Adenine, A1. (Figure 7). This suggests that the change might cause the DNA binding pattern.

TABLE 1: Comparison of surface accessibilities (Å) of the wild type and mutated residues and those in the vicinity of the mutated residues in the five WT1 mutants

Lys371®Ala371 and Ser415®Ala415 MUTANT

No significant change was observed when Lys371 was mutated to Ala371, and Ser415 was mutated to Ala415. It is observed in this mutation that the change that arise in the overall structure and surrounding amino acid residues (Table 1). Lys371 is present on the surface (accessibility = 47.04 Å) of the WT1 molecule. It was observed that the internal protein structure was not affected considerably, as Lys371 is present on the outer most surface of the protein. In the original model, Lys371 stacks against thymine. It also forms a water-mediated contact with side chain hydroxyl of Ser367. Although, Ala371 also stacks against the same DNA base but the distance is slightly altered. The hydrogen bond between Ala371 and Ser367 has not been predicted in the mutated model.  It has been demonstrated that mutation within finger 2 and 4 abolished sequence specific binding of WT1 to DNA bases19. The mutation of the corresponding lysine in a peptide could reduce its affinity for DNA seven folds [27].  On the other hand, it is reported [28] that a surface mutation would not cause a significant change in the internal structure of protein.  However, the replacement of a basic polar residue with a non-polar one could account for a reduction in polarity.  The modeling studies of Lys to Ala mutation do not however support this finding and require further analysis.

Mutation of Ser415®Ala415 in the WT1 model (Table 1). Ser415 is located near the active center of WT1. It has been demonstrated that Ser415 makes a water-mediated contact with phosphate of DNA base, guanine [20]. In our predicted model of WT1, Ser415 makes two water (numbers 516 and 568) mediated contacts. Mutation of this Ser with Ala resulted in the loss of one of these contacts leading to the loss of binding. The replacement of relatively polar residue, Ser to a non-polar one, Ala could account for this reduced interaction. This is also evident by a slight decrease in the accessibility of Ala (Ser415 = 7.96 Å; Ala415 = 7.61 Å).

His434®Asp434 MUTANTS

In case of mutants His434→Asp434, there was a loss of coordination of metal ion (zn203) with mutant Asp434. Glu430 move from relatively exposed to completely buried environment. His434 is also present at the active center of WT1. We predicted two mutants; His434®Asp434 and His434®Arg434 mutants by molecular modeling (Table 1). In case of His434®Asp434 mutation, the water mediated contact is lost. The distance between mutated Asp and zinc (Zn203) was also increased from 2.28 Å to 3.57 Å suggesting that there could be a loss of coordination with the metal ion as well. The amino acid Glu340 that is present near His434 also moved considerably form relatively exposed to completely buried environment (14.83 Å to 00.0 Å).

Conclusion

It is concluded that mutation of amino acid residue Cys416→Ala416, His434→Asp434 and His434→Arg434 of WT1 may lose its function to regulate the function of genes by binding to specific parts of DNA. Besides the mutation of above-mentioned amino acid residue, the role of WT1 in cell growth, cell differentiation, apoptosis and tumor suppressor function is also lost.

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