\r\n\tOn the other hand, these biofuels can be further utilized as substrates for organic syntheses for bulk chemical products, like alcohols, biodegradable polymers, organic acids, etc. \r\n\tThese two options reveals different prospects for development of new biofuel-based industries, although in a modest scale for the moment. \r\n\tThe present book aims to offer some examples and new ideas for the broader applications of biofuels and the resulting raw materials for energy and chemical production as an alternatives to the traditional fossil fuels.
",isbn:"978-1-78985-676-7",printIsbn:"978-1-78985-675-0",doi:null,price:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"b97398a8e5c5fef9494e8ef39361a7dd",bookSignature:"Prof. Venko Beschkov",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9184.jpg",keywords:"Biofuels, Fossil Fuels, Resources, Greenhouse Gases, Climate Changes, Carbon Cycle, Application of Biofuels, Biofuels Origin, Chemical Syntheses Precursors, Methane Reforming, Ethanol Processing, Biodiesel and Glycerol, Chemical Production",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 12th 2019",dateEndSecondStepPublish:"March 5th 2019",dateEndThirdStepPublish:"May 4th 2019",dateEndFourthStepPublish:"July 23rd 2019",dateEndFifthStepPublish:"September 21st 2019",remainingDaysToSecondStep:"18 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,editors:[{id:"191530",title:"Prof.",name:"Venko",middleName:null,surname:"Beschkov",slug:"venko-beschkov",fullName:"Venko Beschkov",profilePictureURL:"https://mts.intechopen.com/storage/users/191530/images/system/191530.jpg",biography:"Venko Beschkov, PhD, DSc was born in 1946 in Sofia, Bulgaria. 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1. Introduction
All life form on this planet depends on clean air. Air quality not only affects human health but also components of environment such as water, soil, and forests, which are the vital resources for human development.
Urbanization is a process of relative growth in a country’s urban population accompanied by an even faster increase in the economic, political, and cultural importance of cities relative to rural areas. Urbanization is the integral part of economic development. It brings in its wake number of challenges like increase in population of urban settlement, high population density, increase in industrial activities (medium and small scale within the urban limits and large scale in the vicinity), high rise buildings and increased vehicular movement. All these activities contribute to air pollution. The shape of a city and the land use distribution determine the location of emission sources and the pattern of urban traffic, affecting urban air quality (World Bank Reports 2002). The dispersion and distribution of air pollutants and thus the major factor affecting urban air quality are geographical setting, climatological and meteorological factors, city planning and design and human activities.
Cities in the developing countries are characterized by old city and new development. The old cities have higher population density, narrow lanes and fortified structures.
In order to ensure clean air in urban settlements urban planning and urban air quality management play an important role. New legislations, public awareness, growth of urban areas, increases in power consumption and traffic pose continuous challenges to urban air quality management. UNEP (2005) has identified niche areas Urban planning need to primarily focus on as:
Promotion of efficient provision of urban infrastructure and allocation of land use, thereby contributing to economic growth,
managing spatial extension while minimizing infrastructure costs,
improving and maintaining the quality of the urban environment and
Prerecording the natural environment immediately outside the urban area.
Air quality modelling provides a useful support to decision making processes incorporating environmental policies and management process. They generate information that can be used in the decision making process. The main objectives of models are: to integrate observations, to predict the response of the system to the future changes, to make provision for future development without compromising with quality
2. Urban air quality
The urban air is a complex mixture of toxic gases and particulates, the major source is combustion of fossil fuels.Emissions from fossil fuel combustion are reactive and govern local atmospheric chemistry.Urban air pollution thus in turn affect global troposphere chemistry and climate (e.g. tropospheric O3 and NOX budgets, radiative forcing by O3 and aerosols).
Sources of air pollutants in urban area, their effect and area of concern are summarized in Table 1.
Source
Pollutants
Effects
Area of concern
Large number of vehicles
Particulate matters (PM10, PM2.5), Lead (Pb), Sulphur dioxide (SO2), Oxides of nitrogen (NOx), Ozone (O3), Hydro carbons (HCs), Carbon monoxide (CO), Hydrogen fluoride (HF), Heavy metals (e.g. Pb, Hg, Cd etc.)
Human Health (acute and chronic)
Local, Regional and Global
Use of diesel powered vehicle in large number
Use of obsolete vehicles in large number
Large number of motor cycles/three wheelers (2 stroke and three stroke)
Ecosystem (acute and chronic)
Local, Regional and Global
Unpaved and/or poorly maintained street
Open burning
Inadequate infrastructure
Greenhouse gas emission
Global
Low quality of fuel/fuel adulteration
Little emission control & technology in industry
Presence of industries (e.g. ceramic, brick works, agrochemical factory)
Acid rain
Global
Waste incineration
Stratospheric ozone depletion
Global
Limited dry deposition of pollutants
Long-range transport
Global
Table 1.
Urban sources of air pollutants, their effect and area of concern.
Urban air pollution involves physical and chemical process ranging over a wide scale of time and space. The urban scale modeling systems should consider variations of local scale effects, for example, the influence of buildings and obstacles, downwash phenomena and plume rise, together with chemical transformation and deposition. Atmospheric boundary layer, over 10 to 30 km distances, governs the dispersion of pollutants from near ground level sources. Vehicular emissions are one the major pollution source in urban areas. Ultrafine particles are formed at the tailpipe due to mixing process between exhaust gas and the atmosphere. Processes at urban scale provide momentum sink, heat and pollutant source thereby influencing the larger regional scale (up to 200 km). Typical domain lengths for different scale models is given in table 2.
Model
Typical Domain Scale
Typical resolution
Motion Example
Micro scale
200x200x100m
5m
Molecular diffusion, Molecular viscosity
Mesoscale (urban)
100x100x5km
2km
Eddies, small plumes, car exhaust, cumulus clouds
Regional
1000x1000x10km
36km
Gravity waves, thunderstorms, tornados, cloud clusters, local winds, urban air pollution
Synoptic(continental)
3000x3000x20km
80 km
High and low pressure system, weather fronts, tropical storms, Hurricanes Antarctic ozone hole,
Global
65000x65000x20km
4° x 5°
Global wind speed, rossby (planetary) waves stratospheric ozone reduction Global worming
Table 2.
Typical domain length for different scale model
Piringer et al., 2007, have demonstrated that atmospheric flow and microclimate are influenced by urban features, and they enhance atmospheric turbulence, and modify turbulent transport, dispersion, and deposition of atmospheric pollutants. Any urban scale modeling systems should consider effects of the various local scales, for example, the influence of buildings and obstacles, downwash phenomena and plume rise, chemical transformation and deposition. The modelling systems also require information on emissions from various sources including urban mobile pollution sources. Simple dispersion air quality pollution transport models and complex numerical simulation model require wind, turbulence profiles, surface heat flux and mixing height as inputs. In urban areas mixing height is mainly influenced by the structure heights and construction materials, in terms of heat flux. Oke (1987, 1988, 1994), Tennekes (1973), Garrat (1978, 1980), Raupach et al (1980) and Rotach (1993, 1995) divided the Atmospheric Boundary Layer within the urban structures into four sub layers (Figure 1).
Figure 1.
Boundary- layer structure over a rough urban built- up area A daytime situation is displayed where Z I denotes the mixed layer height. Modefied after Oke ( 1988) and Rotach (1993).
In urban establishments anthropogenic activities take place between the top of highest building and the ground. People also live in this area. The layer of atmosphere in this volume is termed as Urban Canopy. The thermal exchanges and presence of structures in urban canopy modify the air flows significantly and this makes the atmospheric circulations in urban canopy highly complex. The heterogeneity of urban canopies poses a challenge for air quality modeling in urban areas. The importance of various parameters in different models for urban atmosphere study is given in Table 3. Figure 2 shows the flow and scale lengths within an urban boundary layer, UBL.
Parameter
Air Quality
Urban Climatology
Urban Planning
Wind speed
Very important
Important
Very Important
Wind Direction
Very important
Important
Very Important
Temperature Humidity
Important
Extremely important
Very Important
Pollutant Concentration
Extremely important
Very important
Very important
Turbulent Fluxes
Very important
Very important
Very important
Table 3.
Ranking of parameters in different applications for urban air environment
Figure 2.
Schematic diagram showing processes, flow and scale lengths within an urban boundary layer, UBL. This is set in the context of the planetary boundary layer, PBL, the urban canopy layer, UCL, and the sky view factor, SVF, a measure of the degree to which the sky is obscured by surrounding buildings at a given point which characterises the geometry of the urban canopy. Ref:. Meteorology applied to urban pollution problems-Final report COST Action 715. Dementra Ltd Publishers
Vehicles are one of the important pollution sources in urban areas. Maximum exposure to local public is from this source and thus they form important receptor group. Pollutant dispersion of vehicular pollution is at street scale and is the smallest scale in urban environment. Hosker (1985) showed that flows in street canyon are like recirculating eddy driven by the wind flow at the top with a shear layer which separates the above canyon flows from those within it. In deep street canyons the primary vortex does not extend to the ground but a weak contra rotating vortex is formed near the ground and is relatively shallow (Figure 3). Pavageau et al (2001) demonstrated that wind directions which are not normal to the street axis cause variations in the flow. The real geometry of the street canyon and the mean flow and turbulence generated by vehicles within the canyon also affect the recirculating flow.
Concentrations of pollutants at a receptor are governed by advection, dispersion and deposition. Air pollutants can be divided into two main categories namely conventional air pollutants and Hazardous Air Pollutants (HAPs). Conventional air pollutants include particulate matters, sulphur dioxide, nitrogen dioxide, carbon monoxide, particles, lead and the secondary pollutant ozone. HAPs include Volatile Organic Compounds, toxic metals
Figure 3.
Air flow pattern in a Street Canyon
and biological agents of many types. All pollutants are not emitted in significant quantities. Secondary pollutants like some VOCs, carbonyls and ozone are formed due to chemical transformation in air. These reactions are often photochemical.
The important components of air quality modelling are thus,
Knowledge of sources and emissions
Transport, diffusion and parametrisation
Chemical transformations
Removal process
Meteorology
Understanding contribution from various sources to air quality is the key for effective management of the air quality. Air quality models offer a useful tool in comprehending these issues. These models evaluate the relationship between air pollutant emissions and their resulting concentration in the ambient air. Commonly used air quality models are: 1) Conceptual Models 2) Emission Models 3) Meteorological Models, 4) Chemical Models, 5) Source Oriented Models and 6) Receptor Models.
3. Air quality model classification
Air quality models cover either separately or together atmospheric phenomena at various temporal and spatial scales. Urban air models generally focus from local (micro- tens of meters to tens of kilometers) to regional (meso) scale. Models can be broadly divided into two types namely physical and mathematical.
Physical models involve reproducing urban area in the wind tunnel. Scale reduction in the replica and producing scaling down actual flows of atmospheric motion result in limited utility of such models. Moreover these are economically undesirable.
Mathematical models use either use statistics to analyse the available data or mathematical representation of all the process of concern. The second type of mathematical models is constrained by the ability to represent physical and chemical processes in equations without assumptions.
Statistical model are simple but they do not explicitly describe causal relationships and they cannot be extrapolated beyond limits of data used in their derivation. Thus dependence on past data becomes their major weakness. These cannot be used for planning as they cannot predict effect of changes in emissions.
3.1. Eulerian and lagrangian models
Eulerian approach has been used to predict air pollutant concentrations in urban areas. The space domain (geographical area or air volume), are divided into "small" squares (two-dimensional) or volumes (three-dimensional), i.e. grid cells. Thus Eulerian models are sometimes called "grid models". Equidistant grids are normally used in air pollution modeling. Then the spatial derivatives involved in the system of Partial Differential Equations are discretized on the grid chosen. The transport, diffusion, transformation, and deposition of pollutant emissions in each cell are described by a set of mathematical expressions in a fixed coordinate system. Chemical transformations can also be included. Long range transport, air quality over entire air shed, that is, large scale simulations are mostly done using Eulerian models. Reynolds (1973), Shir and Shieh (1974) applied Eulerian model for ozone and for SO2 concentration simulation in urban areas, and Egan (1976) and Carmichael (1979) for regional scale sulfur. Holmes and Morawska (2006) used Eulerian model to calculate the transport and dispersion over long distances. The modeling studies by Reynolds (1973) on the Los Angeles basin formed the basis of the, the well-known Urban Air shed Model-UAM. Examples of Eulerian models are CALGRID model and ARIA Regional model or the Danish Eulerian Hemispheric Model (DEHM).
Lagrangian Model approach is based on calculation of wind trajectories and on the transportation of air parcels along these trajectories. In the source oriented models the trajectories are calculated forward in time from the release of a pollutant-containing air parcel by a source (forward trajectories from a fixed source) until it reaches a receptor site. And in receptor oriented models the trajectories are calculated backward in time from the arrival of an air parcel at a receptor of interest (backward trajectories from a fixed receptor). Numerical treatment of both backward and forward trajectories is the same. The choice of use of either method depends on specific case. As the air parcel moves it receives the emissions from ground sources, chemical transformations, dry and wet depositions take place. If the models provide average time-varying concentration estimates along the box trajectory then Lagrangian box models have been used for photochemical modeling. The major shortcoming of the approach is the assumption that wind speed and direction are constant throughout the Physical Boundary Layer. As compared to the Eulerian box models the Lagrangian box models can save computational cost as they perform computations of chemical and photochemical reactions on a smaller number of moving cells instead of at each fixed grid cell of Eulerian models. Versions of EMEP (European Monitoring and Evaluation Programme) are examples of Lagrangian models. These models assume pollutants to be evenly distributed within the boundary layer and simplified exchange within the troposphere is considered.
3.2. Box models
Box models are based on the conservation of mass. The receptor is considered as a box into which pollutants are emitted and undergo chemical and physical processes. Input to the model is simple meteorology. Emissions and the movement of pollutants in and out of the box is allowed. The air mass is considered as well mixed and concentrations to be uniform throughout. Advantage of the box model is simple meteorology input and detailed chemical reaction schemes, detailed aerosol dynamics treatment. However, following inputs of the initial conditions a box model simulates the formation of pollutants within the box without providing any information on the local concentrations of the pollutants. Box models are not suitable to model the particle concentrations within a local environment, as it does not provide any information on the local concentrations, where concentrations and particle dynamics are highly influenced by local changes to the wind field and emissions.
3.3. Receptor models
Receptor modeling approach is the apportionment of the contribution of each source, or group of sources, to the measured concentrations without considering the dispersion pattern of the pollutants. The starting point of Receptor models is the observed ambient concentrations at receptors and it aims to apportion the observed concentrations among various source types based on the known source profile (i.e. chemical fractions) of source emissions. Mathematically, the receptor model can be generally expressed in terms of the contribution from ‘n’ independent sources to ‘p’ chemical species in ‘m’ samples as follows:
Cik=∑j=1naijfjkE1
Where Cik is the measured concentration of the kth species in the ith sample, aik is the concentration from the jth source contributing to the ith sample, and fjk is the kth species fraction from the jth source. Receptor models can be grouped into Chemical mass balance (CMB), Principal Component Analysis (PCA) or Factor analysis, and Multiple Linear Regression Analysis (MLR) and multivariate receptor models.
The Chemical Mass Balance (CMB) Receptor Model used by Friedlander, 1973 uses the chemical and physical characteristics of gases and particulate at source receptor to both identify the presence of and to quantify source contributions of pollutants measured at the receptor. Hopke (1973, 1985) christened this approach as receptor modelling. The CMB model obtains a least square solution to a set of linear equation, expressing each receptor concentration of a chemical species as a linear sum product of source profile species and source contributions. The output to the model consists of the amount contributed by each source type to each chemical species. The model calculates the contribution from each source and uncertainties of those values. CMB model applied to the VOC emissions in the city of Delhi and Mumbai (Figure 4 ) shows that emissions from petrol pumps and vehicles at traffic intersection dominate.
PCA and MLR are statistical models and both PMF and UNMIX are advanced multivariate receptor models that determine the number of sources and their chemical compositions and contributions without source profiles. The data in PMF are weighted by the inverse of the measurement errors for each observation. Factors in PMF are constrained to be nonnegative. PMF incorporates error estimates of the data to solve matrix factorization as a constrained, weighted least-squares problem (Miller et al., 2002; Paatero, 2004).
Geometrical approach is used in UNMIX to identify contributing sources. If the data consist of ‘m’ observations of ‘p’ species, then the data can be plotted in a p-dimensional data space, where the coordinates of a data point are the observed concentrations of the species during a sampling period. If n sources exist, the data space can be reduced to a (n-1) dimensional space. An assumption that for each source, some data points termed as edge points exist for which the contribution of the source is not present or small compared to the other sources.
Figure 4.
Category wise Contribution to Total VOCs at Mumbai and Delhi based on CMB results(Ref: Anjali Srivastava 2004, 2005)
UNMIX algorithm identifies these points and fits a hyperplane through them; this hyperplane is called an edge. If n sources exist, then the intersection of n-1 of these edges defines a point that has only one contributing source. Thus, this point gives the source composition. In this way, compositions of the n sources are determined which are used to calculate the source contributions (Henry, 2003).
3.4. Computational fluid dynamic models
Resolving the Navier-Stokes equation using finite difference and finite volume methods in three dimensions provides a solution to conservation of mass and momentum. Computational fluid dynamic (CFD) models use this approach to analyse flows in urban areas. In numerous situation of planning and assessment and for the near-sources region, obstacle-resolved modeling approaches are required. Large Eddy Simulations (LES) models explicitly resolve the largest eddies, and parameterize the effect of the sub grid features. Reynolds Averaged Navier Stokes (RANS) models parameterize all the turbulence, and resolve only the mean motions. CFD (large eddy simulation [LES] or Reynolds-averaged Navier-Stokes [RANS]) model can be used to explicitly resolve the urban infrastructure. Galmarini et al., 2008 and Martilli and Santiago,2008, used CFD models to estimate spatial averages required for Urban Canopy Parameters. Using CFD models good agreement in overall wind flow was reported by field Gidhagen et al. (2004).They also reported large differences in velocities and turbulence levels for identical inputs.
3.5. The Gaussian steady-state dispersion model
The Gaussian Plume Model is one of the earliest models still widely used to calculate the maximum ground level impact of plumes and the distance of maximum impact from the source. These models are extensively used to assess the impacts of existing and proposed sources of air pollution on local and urban air quality. An advantage of Gaussian modeling systems is that they can treat a large number of emission sources, dispersion situations, and a receptor grid network, which is sufficiently dense spatially (of the order of tens of meters). Figure 5 shows a buoyant Gaussian air pollutant dispersion plume. The width of the plume is determined by σy and σz, which are defined by stability classes(Pasquill 1961; Gifford Jr. 1976)
Figure 5.
A buoyant Gaussian air pollutant dispersion plume
The assumptions of basic Gaussian diffusion equations are:
that atmospheric stability and all other meteorological parameters are uniform and constant throughout the layer into which the pollutants are discharged, and in particular that wind speed and direction are uniform and constant in the domain;
that turbulent diffusion is a random activity and therefore the dilution of the pollutant can be described in both horizontal and vertical directions by the Gaussian or normal distribution;
that the pollutant is released at a height above the ground that is given by the physical stack height and the rise of the plume due to its momentum and buoyancy (together forming the effective stack height);
that the degree of dilution is inversely proportional to the wind speed;
that pollutant material reaching the ground level is reflected back into the atmosphere;
that the pollutant is conservative, i.e., not undergoing any chemical reactions, transformation or decay.
The spatial dynamics of pollution dispersion is described by the following type of equation in a Gaussian model:
C(x, y, z) : pollutant concentration at. point ( x, y, z );
U: wind speed (in the x "downwind" direction, m/s)
Σ: represents the standard deviation of the concentration in the x and y direction, i.e., in the wind direction and cross-wind, in meters;
Q: is the emission strength (g/s)
He: is the effective stack height, see below.
From the above equation, the concentration in any point ( x, y, z ) in the model domain, from a constant emission rate source, in steady state can be calculated.
Plume rise equations have been developed by Briggs (1975). The effective stack height (physical stack height plus plume rise) depends on exit velocity of gas, stack diameter, average ambient velocity, stack gas temperature and stability of atmosphere
He=H+ΔH,ΔH=1.4QH14(dθdz)−38,QH=ρQCP(TG−15)3600E3
Where
H: height of stackTG : Temperature of exit gasQ: Volume of exit gas dθ/dz : Temperature Gradient ρ: Density of exit gas CP: Specific heat at constant pressure
Some major air pollution dispersion models in current use
ADMS 3: Developed in the United Kingdom (www.cerc.co.uk)
AERMOD: Developed in the United States, (www.epa.gov/scram001/dispersion_prefrec.htm)
AUSPLUME: Developed in Australia, (http://www.epa.vic.gov.au/air/epa)
CALPUFF: Developed in the United States, (www.src.com/calpuff/calpuff1.htm)
DISPERSION2:Developed in Sweden,( www.smhi.se/foretag/m/dispersion_eng.htm)
ISC3: Developed in the United States, (www.epa.gov/ttn/scram/dispersion_alt.htm)
NAME: Developed in the United Kingdom,(www.metoffice.gov.uk/research/modelling-systems/dispersion-model)
MERCURE: Developed in France, (www.edf.com)
RIMPUFF: Developed in Denmark, (http://www.risoe.dtu.dk)
Figure 6.
Air Quality Index of an Industrial Area: Orissa, India
8 regional air quality modeling leading to setting up of air quality index for an industrial area in India is given in Fig 2. This study has resulted in estimating the air assimilative capacity of the region and delineating developmental plans accordingly
3.6. Urban pollution and climate integrated modeling
Integrated air quality modelling systems are tools that help in understanding impacts from aerosols and gas-phase compounds emitted from urban sources on the urban, regional, and global climate. Piringer et al., 2007 have demonstrated that urban features essentially influence atmospheric flow and microclimate, strongly enhance atmospheric turbulence, and modify turbulent transport, dispersion, and deposition of atmospheric pollutants. Numerical weather prediction (NWP) models with increased resolution helps to visualize a more realistic reproduction of urban air flows and air pollution processes.
Integrated models thus link urban air pollution, tropospheric chemistry, and climate. Integration time required is ≥ 10 years for tropospheric chemistry studies in order to consider CH4 and O3 simulation and aerosol forcing assessment. Tropospheric chemistry and climate interaction studies extend the integration time to ≥ 100 years.
The outline of overall methodology of FUMAPEX and MIT interactive chemistry model is shown in Figure 6 and 7. Schematic of couplings between atmospheric model and the land model components of the MIT IGSM2 is given in Figure 8.
Need of integrated models
All of these models have uncertainties associated with them. Chemical transport models, such as Gaussian plume models and gridded photochemical models, begin with pollutant emissions estimates and meteorological observations and use chemical and physical principles to predict ambient pollutant concentrations. Since these models require temporally and spatially resolved data and can be computationally intensive, they can only be used for well-characterized regions and over select time periods. Eulerian grid models are not suitable to assess individual source impacts, unless the emissions from the individual source are a significant fraction of the domain total emissions. This limitation
Figure 7.
General scheme of the FUMAPEX urban module for NWP models.
Figure 8.
Overall Scheme MIT Interactive Chemistry-Climate Model
Figure 9.
Schematic of coupling between the atmospheric model (which also includes linkages to the air chemistry and ocean models) and the land model components of the IGSM2, also shown are the linkages between the biogeophysical (CLM) and biogeochemical (TEM) subcomponents. All green shaded boxes indicate fluxes/storage that are explicitly calculated/tracked by this Global Land System (GLS). The blue shaded boxes indicate those quantities that are calculated by the atmospheric model of the IGSM2.
arises from the assumption that emissions are uniformly mixed within the grid cell, and thus do not properly address the initial growth and dispersion of the pollutants.
Lagrangian plume and puff models account for chemical processes by simple linear transformations in time. These models can track individual source impacts, thus enabling user to outline source specific air pollution control strategies. Considerable differences are observed when concentrations are compared in time and space because of uncertainties in the characterization of the direction of transport that are of the order of the actual plume width. The observed and simulated concentrations for fixed receptors, give estimates of maximum concentration values within a factor of two or three of those observed. These differences are an order of magnitude larger than those observed for estimates of secondary pollutants. Both Eulerian and Lagrangian, models are not suitable to handle inert pollutants and secondary pollutants whose concentrations depend on reaction rates and are photochemical in nature.
Receptor models, such as Positive Matrix Factorization and Chemical Mass Balance (CMB), source apportionment addresses the problem by statistical inference of source contributions to total pollution from observations of ambient air chemical composition. Mass balance methods of source apportionment use linear models with chemical composition vectors of sources as covariates. Knowledge of meteorological variables is not required but may be used to refine the analysis. Knowledge of emission sources is useful for the interpretation of results from statistical-based receptor models and is required by receptor models that use a mass balance approach. Less data and computational resource requirement by Receptor models as compared to chemical transport models, make them more convenient tool for evaluation of ambient pollutant concentrations and pollutant emission inventory. However, their utility for reactive air pollutants is uncertain and questionable. The disadvantage of CMB model arises from its assumptions. such as constant compositions of source emissions over the period of ambient and source sampling; linear additive and unreactive chemical species; identification of all sources contributing to the receptor and knowledge of their emission profile, linearly independent emission profiles.
The urban air quality models requires
Good net work ambient air concentrations of pollutants of concern: Geography of the urbanarea, constructed clusters, road network, location of bluidings etc play a major role in dispersion of pollutants. Thus to understand the ambient status of pollutants it is necessary to have sufficient number of monitoring locations to cover the urban sprawl of concern.
Micro metereology data: The wind patterns, temperature, humidity alter in urban areas according to anthropogenic activity and architecture
Bluilding details: To account for the effect of anthropogenic architecture falling in path of plume, its geometry is required to be known.
Knowledge of all sources: All sources and their emission profiles are required to be known to plan for further development in urban area and control of pollutant emission
Atmospheric Chemistry: All transformations of emitted chemical species, their reaction rates pathways must be known to account for observed concentration of pollutants.
Healthy Impacts: Models need to incorporate health effect of pollutants
None of the models available can handle all the requirements of urban air quality management. Each one focuses of one aspect and thus coupling of different models are required.
4. Further issues to be addressed
COST an intergovernmental framework for European Cooperation in Science and Technology, Europe, addressed issues related to urban air quality models in its action programmes. Cost 728 focussed on enhancing mesoscale meteorological Modeling capabilities for air pollution and Dispersion applications under larger programme of urbanization of meteorological and air quality models. The issues identified for improvements to the state of urbanization of models can be summarized as
Systematic evaluation of urban land surface schemes
Increasing the range of variables observed to ensure as complete a range of evaluation as possible
evaluation over a broad spectrum of conditions (meteorological, morphological, geographical setting, etc.
Testbeds and observatories with different objectives and dataset richness.
A deeper understanding of urban PBL dynamics i.e development of long-term urban test beds in a variety of geographic regions (e.g., inland, coastal, complex terrain) and in many climate regimes, with a variety of urban core types (e.g., deep versus shallow, homogeneous versus heterogeneous).
A framework to address conceptual issue of evaluation of model prediction of the flow within the canopy
User friendly and multifaceted urban databases and enabling technology
Developing core capabilities for advancing urban modeling and boundary layer research
An open database to address issues of availability and sources of high-resolution data sets easily to all with mechanism for its maintenance, upgrading, updating, and archiving.
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Rao",authors:[{id:"26286",title:"Dr.",name:"Anjali",middleName:null,surname:"Srivastava",fullName:"Anjali Srivastava",slug:"anjali-srivastava",email:"anjali54@gmail.com",position:null,institution:null},{id:"26292",title:"Mrs.",name:"Padma.S",middleName:null,surname:"Rao",fullName:"Padma.S Rao",slug:"padma.s-rao",email:"ps_rao@neeri.res.in",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction ",level:"1"},{id:"sec_2",title:"2. Urban air quality ",level:"1"},{id:"sec_3",title:"3. Air quality model classification",level:"1"},{id:"sec_3_2",title:"3.1. Eulerian and lagrangian models",level:"2"},{id:"sec_4_2",title:"3.2. Box models",level:"2"},{id:"sec_5_2",title:"3.3. Receptor models",level:"2"},{id:"sec_6_2",title:"3.4. Computational fluid dynamic models",level:"2"},{id:"sec_7_2",title:"3.5. The Gaussian steady-state dispersion model",level:"2"},{id:"sec_8_2",title:"3.6. Urban pollution and climate integrated modeling",level:"2"},{id:"sec_10",title:"4. 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National Environmental Engineering Research Institute, Kolkata Zonal Centre, India
'},{corresp:null,contributorFullName:"B. Padma S. Rao",address:null,affiliation:'
National Environmental Engineering Research Institute, Kolkata Zonal Centre, India
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1. Introduction
Cytokines are a cell-signaling group of low molecular weight extracellular polypeptides/glycoproteins synthesized by different immune cells, mainly, by T cells, neutrophils and macrophages, which are responsible to promote and regulate immune response (i.e. activity, differentiation, proliferation and production of cells and other cytokines). These polypeptides act on signaling molecules and cells, stimulating them toward sites of inflammation, infections, traumas, acting on primary lymphocyte growth factors and other biological functions. Cytokines may act in the site where they are produced (autocrine action), in nearby cells (paracrine action) or in distant cells (endocrine action). In this sense, they are important in the development and regulation of immune system cells. Different types of cytokines had been discovered, including chemokines, interferons (IFN), interleukins (IL), lymphokines and tumor necrosis factor (TNF) [1, 2, 3, 4].
In this chapter, we describe and review different cytokines. They will be categorized according to their type, followed by presentation of their function and a brief scope: IFN (IFN-α, β and γ), IL (IL-1, IL-2 and others), TNF (TNF-α and TNF-β) and others. A brief explanation of different cytokines activities also will be done, comprising pro- and anti-inflammatory action, cellular immune responses and performance in hematopoiesis. Methods to reach these objectives include a literature search in the most relevant sources of information, including PubMed/Medline, Scopus and Web of Science databases.
As key results, this chapter will provide a better understanding on cytokines types and functions, with organized concepts about this subject. As we aim to provide a comprehensive review of the available data regarding cytokines, this chapter will be a valuable source of information for readers who seek a thorough and structured synthesis on this topic.
2. Interferons
Interferon family represents a widely expressed group of cytokines. It includes three main classes, designated as type I IFNs, type II IFN and type III IFNs. The two main type I IFNs includes IFN-α (further classified into 13 different subtypes such as IFN-α1, -α2, -α4, -α5, -α6, -α7, -α8, -α10, -α13, -α14, -α16, -α17 and -α21), and IFN-β. The term interferon derives from the ability of these cytokines to interfere with viral replication. Type I IFNs present a potent antiviral effect by inhibiting viral replication, increasing the lysis potential of natural killer (NK) cells and the expression of MHC class I molecules on virus-infected cells, and stimulating the development of Th1 cells. During an infectious process, this type of interferon becomes abundant and is easily detectable in the blood. On the other hand, type II IFN has only one representative, IFN-γ. This cytokine plays a major role is macrophage activation both in innate and adaptive immune responses. Type III IFNs, also denoted IL-28/29, present similar biological effects to type I IFN, playing an important role in host defense against viral infections [5, 6, 7, 8].
2.1. History
Interferon was the first described member of the class of protein molecules now known as cytokines. Nowadays, interferons are well known to participate in innate immune system, mediating responses against viral infections. This role of the IFNs was first described in the 1930s, when a research conducted by Hoskins demonstrated that rabbits previously infected by the herpes simplex virus were protected against subsequent infections by the same type of virus. In 1937, a few years after Hoskins’ experiment, Findlay and MacCallum showed that the virus-infected animals were also resistant to infections caused by antigenically different viruses, corroborating and complementing the existing evidence regarding IFNs functions at that time. Their findings, however, were only confirmed in 1957, when Isaacs and Lindenmann, through cell cultures research, demonstrated that cells infected by a virus had the ability to produce a protein that could make other cells resistant to other viruses. Glasgow, in 1966, theorized that the interferon production was not limited to primary infection by viruses, and that this cytokine might play a role following re-infection. Therefore, the concept of “immune induction” of interferon became well established by the end of the 1960s. The early 1970s were marked by two milestone studies, which confirmed the existence of two different categories of interferons, which differed physicochemically and biologically: the immune-induced interferon (currently known as type II IFN) and the classical virus-induced interferon (currently known as type I IFN). In 1980, the terms IFN-α and IFN-β arose to designate the “classical interferons”, which had been obtained in pure forms exhibiting homogeneity. Albeit the “immune-induced interferon” had not been obtained in pure form at that time, it was recognized that this molecule was different from IFN-α and IFN-β, being, therefore, designated as IFN-γ. Despite the markedly difference of this cytokine when compared to IFN-α and IFN-β, IFN-γ was originally classified in the IFN family due to its ability to ‘interfere’ with viral infections, which characterizes the original definition of IFNs. In the last decade, a third type of IFN (type III IFN) has been described, the IFN-λ. This type is also referred as interleukins IL-28A and B (IFN-λ2 and IFN-λ3, respectively), and IL-29 (IFN-λ1) [8, 9, 10, 11].
2.2. Pathways of induction and major roles of interferons
There are several isotypes of type I IFNs. In humans, there are multiple forms of IFN-α, only one type of IFN-β and additional isotypes, as IFN-δ, IFN-ε, IFN-κ, IFN-τ and IFN-ω (IFN-δ and IFN-τ have been only described in pigs and cattle). This sort of cytokines presents similar structure, binding to the same cell surface receptor, and they are coded by a family of linked genes located on the human chromosome 9 [7, 12].
Type I IFN synthesis is induced by microbial challenge (i.e., viral and bacterial infections or microbial nucleic acids exposure) when the pattern recognition receptors (PRRs) sense these microorganisms. These receptors can be found in the cytosol or in the endosome. Once a virus infects a cell, the cell activates signals that lead to phosphorylation, dimerization and passage to the nucleus of the interferon response factor 3 (IRF3). Along with IRF3, other transcription factors, such as nuclear factor kappa B (NF-κB) and activator protein 1 (AP-1), activate the transcription of IFN-β gene. After this process, secreted IFN-β binds to the interferon receptor (IFNAR) on the surface of the infected cell, producing an autocrine signaling to mobilize other interferon response factors and alter gene expression patterns to provide interferon response. Besides autocrine signaling, IFN-β also binds to the interferon receptor expressed by neighboring non-virus infected cells, acting in a paracrine manner to promote interferon response in order to help these cells to resist viral infection [5, 13, 14].
Interferon response comprises a series of reactions that alter the expression of a variety of human genes. These reactions are mediated by the binding with type I interferon receptors, which consists of the IFNAR1 and IFNAR2 transmembrane proteins, and two associated cytoplasmic tyrosine kinases, the Janus kinase 1 (Jak1) and tyrosine kinase 2 (TyK2). In addition to IRF3, another transcription factor induced by interferon response is interferon response factor 7 (IRF7), which is responsible to initiate IFN-α transcription without the need of NF-κB and AP-1. The canonical pathway that mediates the biological effects of IFNs corresponds to the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. Both the antiviral and inflammatory effects of IFN-α/IFN-β are specifically mediated by STAT1 and STAT2. This pathway, however, does not work in isolated manner. It extensively communicates with other signal transduction pathways, therefore recruiting several effector molecules to promote a potent effect against viral infections, antiproliferative and antitumor activities, in addition to the immunomodulatory effects. In healthy individuals, these type I IFN genes are strictly regulated, with almost no constitutive IFN-α production [7, 15, 16].
A high number of cells produce IFN-α and IFN-β, including macrophages, fibroblasts, and endothelial cells, specialized leukocytes, called interferon-producing cells (IPCs), or natural interferon-producing cells, secrete up to 1000 times more interferon than the others after microbial challenge. These cells, also known as plasmacytoid dendritic cell (pDCs), are present in the blood, comprising less than 1% of the total peripheral blood mononuclear cells. In terms of morphology, they are similar to plasmocytes, another type of cell responsible for the massive production of this cytokine. IPCs express toll-like receptors (TLRs) 6, 7, 9 and 10, which are critical components of innate immunity, acting as pathogen sensors. Toll-like receptors act on innate immunity cells by detecting conserved patterns of pathogenic microorganisms. These cells, when activated by these receptors, lead to maturation of antigen-presenting cells and production of inflammatory cytokines. Hence, IPCs become responsive to a variety of viral infections through quick secretion of massive amounts of type I IFN. In other words, these cells can produce substantial amounts of type I IFN in response to stimulation with a wide range of DNA and RNA viruses, which signal through TLR9 and TLR7, respectively. During an antiviral immune response, therefore, IPCs are able to promote the function of NK cells, B and T cells, and myeloid dendritic cells through type I IFN. IPCs still differentiate into a unique type of mature dendritic cell, which allows the direct regulation of the function of T cells and links innate and adaptive immune responses. This process occurs at a later stage of viral infection [11, 17, 18, 19, 20].
The whole process mentioned above can be summarized through the following explanation. On the first day after stimulation by viral infection (microbial challenge), IPCs produce massive amounts of type I IFN. On the following 2 days, IPCs differentiate into a type of dendritic cell called a plasmacytoid dendritic cell, which maintains the ability to produce interferon. During the infection process, these cells cluster into the T cell areas of the draining lymph nodes. Although there is some similarity between plasmacytoid dendritic cells and myeloid dendritic cells (known as conventional dendritic cells), it is believed that plasmacytoid dendritic cells do not have a substantial involvement in T cell activation in adaptive immunity, which is the main function of conventional dendritic cells. Therefore, in the context of innate immunity, conventional dendritic cells produce relatively small amounts of type I IFN, but produce large amounts of IL-12, a cytokine that interacts with type I IFN to activate the NK cell response to viral infection [7, 11].
IFNs, besides being first line of defense against viral infections, play important roles in immunosurveillance for malignant cells. More specifically, type I interferons present a potent antiviral activity, which is associated with several physiological changes. For ease of understanding, the role of type I interferons, in which IFN-α and IFN-β are the major actors, can be divided in three main functions. Firstly, these cytokines stimulate resistance to viral replication in all cells through cellular genes activation, with the consequent destruction of the viral mRNA and inhibition of the viral proteins translation. Secondly, they promote an increase in ligands to NK cell receptors expression in virus-infected cells. Thirdly, they lead to NK cells to eradicate virus-infected cells [8, 21, 22].
NK cells are lymphocytes of innate immune system, which provide defense against viral infections by secreting cytokines (mainly IFN-γ) and killing infected cells. When IFN-α or IFN-β bind to interferon receptors on circulating NK cells, these are activated and directed to infected tissues, where they attack virus-infected cells. It is possible to say that NK cells play, in innate immune response, similar functions than cytotoxic T cells in adaptive immune response [23, 24].
Type II and type III IFNs do not share homogeneity with type I IFN in terms of induction, and the signaling pathways are, therefore, through their own receptors. Nevertheless, the signal pathways involved with type I IFN and type II IFN, as well as the target genes used by these cytokines, somewhat overlap. IFN-γ receptor (IFNGR) is composed by two structurally homologous polypeptides that belong to the type II cytokine receptor family, named IFN-γR1 and IFN-γR2. IFN-γ (originally designated as macrophage-activating factor) binds and induces dimerization of the two receptor chains. This process leads to the activation of JAK1 and JAK2 kinases and, subsequently, to the phosphorylation and dimerization of STAT1, which stimulates the transcription of several genes. The genes induced by this cytokine encode several different molecules that mediate the biological activities of IFN-γ [5, 14, 25].
Unlike IFN-α or IFN-β, the gene that encodes IFN-γ is located on the human chromosome 12. This unique specimen of type II IFN is the primary cytokine involved in macrophage activation (named as classical activation) and plays a critical role in immunity against intracellular microorganisms. In innate immune system, IFN-γ is the main cytokine produced by NK cells, acting as a mediator of innate immunity. Despite belonging to the interferon family, IFN-γ does not produce a potent antiviral effect, running primarily as an activator of effector cells of the immune system. In adaptive immunity, IFN-γ is produced by T cells in response to antigen recognition, and its secretion is increased by IL-12 and IL-18. In addition, B cells and professional antigen-presenting cells (e.g., monocyte/macrophage and dendritic cells) are also involved in this cytokine production. While IL-12 and IL-18 control the production of IFN-γ by promoting its synthesis, IL-4 and IL-10 correspond to the negative regulators of type II IFN production [5, 8, 25].
Regarding biological activities, both type I and type II IFN are essential in the immediate cellular response to viral infections. IFN-γ acts on immune cell activation and induction of the major histocompatibility complex (MHC) molecules, which is important at a later stage of the response. Thus, this cytokine establishes an antiviral state for long-term control, coordinating the transition from innate to adaptive immunity. IFN-γ plays a role in macrophage activation, triggering microbicide effector functions in these cells. Macrophages activated by IFN-γ promote more intensive pinocytosis and phagocytosis, in addition to an improved microbial killing ability. Furthermore, IFN-γ acts as a cell growth inhibitor and presents the ability of triggering apoptosis [25, 26].
In summary, in the early stages of infection, NK cells are the main producers of IFN-γ, whose major role is macrophage activation. Once activated, macrophages release cytokines that participate in T cells activation, therefore initiating the adaptive immune response. After being produced and entering the infected site, the effector T cells become, in turn, the main source of IFN-γ and cell-mediated cytotoxicity. Besides the effects on host defense, IFN-γ is also involved in the protection against tumor development [5, 26].
Type III IFN (IL-28/29 or IFN-λ), likewise type I IFN, present antiviral activity. Type III IFN is subdivided in IFN-λ1 and IFN-λ2/3, which are expressed in identical patterns. The signaling pathway related to IFN-λ is similar to IFN-α/IFN-β, involving mechanisms relying on IRFs and NF-κB actions, with the last one playing an essential role in regulating type III IFN expression. Nevertheless, the expression of IFN-λ is more flexible when compared to type I IFN, once it also involves independent actions of NF-kB and IRFs, allowing the production of this cytokine in response to a wider range of stimuli. Most classes of virus and some bacterial products induce IFN-λ expression, and almost all cell types, mostly pDCs, produce type III IFN after virus infection. However, different from the other types of IFN, macrophages are not involved in IFN-λ expression. Regarding biological activities, IFN-λ acts as the first line in host defense against viral infections, besides regulating innate and adaptive immune responses. Recently, a new member of the Interferon Lambda family was identified, the IFN-λ4. This cytokine presents strong antiviral activity and has been recently described to be related to hepatitis C treatment failure. Several in vivo studies have shown that IFN-λ can be developed as a potent antiviral agent, covering a wide spectrum of viral infections, with the additional benefit of not promoting the unwanted pro-inflammatory effects of IFN-α [6, 27, 28, 29].
2.3. Interferons and related diseases
The first sign that type I IFN was somehow involved with human autoimmune diseases came from the observation of an increased incidence of autoantibodies and autoimmune diseases after type I IFN treatment. Hence, when considering the indication of IFN-α therapy for some conditions (e.g., hepatitis C virus infection), it is important to scrutinize the presence of autoantibodies in the patient, since they may increase the risk for autoimmune disease development with this kind of treatment [14]. As previously mentioned, pDCs are responsible for producing high levels of type I IFN in response to nucleic acid-containing immune complexes through the activation of TLRs 7 and 9 [11]. These immune complexes are prevalent in autoimmune conditions, such as systemic lupus erythematosus (SLE), which makes this process highly relevant for the development of autoimmunity. It has been described that, in autoimmune diseases, several key immune effector cells, such as B cells, T effector cells and regulatory T cells are modulated by IFN-α. Hence, type I IFN plays a substantial role in this kind of condition [16].
Regarding type II IFN, IFN-γ may contribute to the pathogenesis of autoimmune diseases, such as systemic lupus erythematosus, multiple sclerosis and type I diabetes mellitus. The role of this cytokine in autoimmune diseases (both in promoting and suppressing the condition) has been shown in several mouse models. The administration of IFN-γ at very early stages of experimental autoimmune encephalomyelitis exacerbates the disease, while its administration at a later stage reduces disease severity. Hence, the absence of biomarkers that could indicate the best stage of the disease to initiate IFN-γ treatment consists in a limiting factor for its therapeutic use [25, 26, 30]. This subject will be reported in the topic “Cytokines and autoantibodies”.
Due to the ability to increase immune response, type I and type II IFN have been explored in clinical trials as treatments for several conditions. It has been found that these cytokines are involved with the improvement of several conditions, such as hepatitis B and C virus infections, autoimmune diseases and certain types of leukemia and lymphomas. Hence, this class of cytokines, which play a paramount role in the immune system, consist of valuable treatment strategy. Still, in order to obtain full advantage of the therapeutic potential of interferons, further researches are needed to elucidate the core mechanisms of their effects [31, 32].
3. Tumor necrosis factor
Tumor necrosis factor (TNF) is a cytokine that had the name derived from it discovery in 1975 as a molecule that caused in vitro necrosis of tumors. Shortly thereafter, it was observed that TNF expression was promoted by immune system cells. These discoveries were important to a posterior characterizing of the TNF superfamily and the TNF receptor superfamily, which has more than 40 members, being the most outstanding TNF-α (commonly named as TNF) and TNF-β (also named Iymphotoxin), but also including cytokines and membrane proteins that have similar sequence homologies and a homotrimeric pyramidal structure (e.g. CD40 ligand, FAS ligand, OX40 ligand, GITR ligand and other several proteins). The binding of this family of cytokines with their respective receptors triggers especially inflammatory reactions [33, 34, 35, 36, 37] .
TNF-β, a type II transmembrane protein, is an important key in the development of lymph nodes and Peyer’s patches, and also for the maintenance of secondary lymphoid organs. The expression of TNF-β is mainly stimulated by lymphocytes. TNF-α will be better described in the following topics [38, 39].
Although it were discovered many receptors along the decades, two are best known: TNFR1 (55 kD) and TNFR2 (75 kD). Both receptors are plasma membrane trimmers, while TNFR1 is expressed by most human cells and TNFR2 is mainly produced by immune system cells. It is important to mention that TNFR2 have a higher affinity to TNF. They are related to inflammatory reactions, so that a cytokine bind to the receptor, it induces the recruitment of proteins that are important for the process [35, 40] .
3.1. Expression and structure of tumor necrosis factor alpha
The production of this cytokine is performed by different cells from the immune system, which includes T cells, NK cells, macrophages and monocytes. The stimulus for TNF expression includes different factors, such as bind to pathogen lipopolysaccharide (LPS) and other parts with toll-like receptors (TLRs), and also by other cytokines, highlighting IFN-y [33, 35].
It is primary secreted as a nonglycosylated type II membrane protein arranged as homotrimer. TNF membrane releases a trimeric soluble cytokine (a polypeptide that weighs around 17-kDa with triangular pyramid shape) through proteolytic cleavage by metalloprotease TNF-converting enzyme, and this is the circulating form that is found in blood plasma, and that allows a potent capacity to displacement in the body, thus it endocrine function. It is not well defined but from three of these circulating TNF it is possible to polymerize them forming one 51-kD polypeptide which facilitates the binding of the cytokine with three receptors simultaneously [37, 41, 42].
TNF have a lot of physiologic multifunction including immune and inflammatory roles and the survival and death of different cells. The main function of cytokine is to attract and activate immune cells to sites of infections and to destroy pathogens, such as bacteria and virus. In this context, TNF stimulate vascular endothelial cells to express adhesion molecules (e.g. selectins and ligands for leukocyte integrins) that allows immune system cells to connect the wall of blood vessels. Additionally, complementing the inflammatory response, TNF induces the production of chemokines that increase the affinity of leukocyte to their ligands, the expression of IL-1 and to activate microbicidal functions of immune system cells. For all TNF importance in the inflammatory reaction, if low quantities of this cytokine are presented in the local, the containment of the infection may be impaired [33, 37, 41, 42, 43].
TNF is also well known to act in inflammatory reaction of some autoimmune diseases, such as rheumatoid arthritis and inflammatory bowel disease. Errors in this production are responsible for a considerable number of autoimmune, neoplastic and other diseases. Under these conditions, the treatment of these diseases are based on biologic agents targeting TNF, and thus looking for reducing the number of available TNF molecules or to block it receptors [33, 35, 40].
TNF also promotes necrosis of tumor cells by inducing programmed cell death, a cytolytic potential. The activation of apoptosis mechanism is mediated by TNFR1, by stimulating the recruitment of death signaling proteins, such as Fas-associated protein with death, TNFR-associated factor (TRAF)-1 and TNFR-associated death domain protein (TRADD). These intracellular proteins are responsible for the release of other proteins such as pro-caspase-8, which in it activated form activate caspase-3, caspase-6, caspase-7 and other cytosolic substrates. These proteins induce genomic DNA degradation and cell death through interacting with latent DNAse. Evidences also suggest that TNF have the capacity to induce carcinogenesis and to stabilize tumors, an event that it is opposite of the previous explained, by DNA mutations and it mechanism of repair (i.e. genotoxic potential). This is possible due to the activation of NF-κB in tumor cells and by promoting production of IL-6 (a tumor-promoting cytokine), both facilitate metastasis and cancer cells to escape from immune system defense [35, 40, 41, 42].
There are other biological events and actions caused by TNF. When this cytokine is produced in large scale, such as in severe infection, it may induce shock or decrease of blood pressure due to reducing vascular muscle tone and myocardial contractility. Additionally, in high concentrations TNF can reduce blood glucose concentration, and cause intravascular thrombosis (by decreasing anticoagulant capabilities of the endothelium). TNF is also known as an endogenous pyrogen because it promotes fever by stimulating hypothalamus cells to produce prostaglandins [33, 40].
4. Interleukins
Interleukins (ILs) are a group of secreted proteins with diverse structures and functions. These proteins bind to receptors and are involved in the communication between leukocytes. They are intimately related with activation and suppression of the immune system and cell division. The interleukins are synthesized mostly by helper CD4+ T lymphocytes, monocytes, macrophages and endothelial cells [5, 44, 45].
Interleukins are named as IL plus a number. Previously, different names were used to refer to the same IL. For instance, IL-1 was called lymphocyte-activating factor, mitogenic protein or T cell replacing factor III. In order to standardize the nomenclature, in 1979, during the Second International Lymphokine Workshop, the term interleukin was introduced. After that, the interleukins started being named consecutively according to the date of their discovery [44, 46, 47].
There have been identified 40 interleukins so far and some of them are further divided into subtypes (e.g. IL-1α, IL-1β). These ILs are grouped in families based on sequence homology and receptor chain similarities or functional properties [5, 44, 48, 49].
In this section, a brief description of various ILs will be presented. Focus will be given to the families of interleukins 1 and 2.
4.1. The interleukin-1 family
Interleukin-1 family is composed by 11 cytokines: 7 ligands with agonist activity (IL-1α, IL-1β, IL-18, IL-33, IL-36α, IL-36β and IL-36γ), 3 receptor antagonists (IL-1Ra, IL-36Ra and IL-38) and 1 anti-inflammatory cytokine (IL-37) [44, 50].
The interleukin-1 family started with only two components: IL-1α, IL-1β. Over the years, new IL with similar behavior and/or structure were discovered and added to the family. All the agonists members of this family show pro-inflammatory activity. These cytokines share a common C-terminal three-dimensional structure with a typical β-trefoil fold consisting of 12-β-strands connected by 11 loops, and have identical positioning of certain introns. Considering that, it is plausible to affirm that they probably arose from the duplication of a common ancestral gene [45, 51, 52].
All members of the family except IL-18 and IL-33 have genes encoding on chromosome 2 in a 400 kb region in human species. Despite the fact that all the cytokines are extracellular, they are synthesized without a hydrophobic leader sequence and are not secreted via reticulum endoplasmic-Golgi pathway, with the exception of IL-1Ra. The secretion mechanism of the other members of the family is still not known. These cytokines bind to closely related receptors, and many of the encoding genes are clustered in a short region of chromosome 2. The receptors contain extracellular immunoglobulin domains and a toll/IL-1 receptor (TIR) domain in the cytoplasmic portion [45, 52].
In order to become active, both IL-1α and IL-1β bind to the ligand-binding chain type I (IL-1R1). Then, the co-receptor, termed the accessory protein (IL-1RAcP), is recruited, and together they form a heterodimeric complex. The signaling that will culminate in a variety of inflammatory activities is initiated by the recruitment of the adaptor protein MyD88 to the toll-IL-1 receptor (TIR), which is followed by the phosphorylation of kinases, the translocation of the nuclear factor kappa B (NF-κB) to the nucleus and the expression of inflammatory genes [50, 51].
Both IL-1α and IL-1β have precursor forms. The precursor of IL-1α is present in the epithelial layers of the gastrointestinal tract, lung, liver, kidney, endothelial cells and astrocytes; and it is capable of binding to the IL-1R1 and initiating the signaling cascade, essentially after cell death by necrosis (e.g. myocardial infarction and stroke). On the other hand, the precursor of IL-1β is not active and does not bind to the receptor. It requires a cleavage to become in the active form [44, 50, 51].
IL-1β is highly involved with autoimmune, infectious, degenerative and, especially, with autoinflammatory diseases. An important part of autoinflammatory diseases is caused by genetic defects in innate inflammatory pathways, and usually show their signals early in life. The first disease classified as autoinflammatory was tumor necrosis factor receptor associated periodic syndrome (TRAPS). Other examples are familial Mediterranean fever and adult and juvenile Still disease. This group of diseases promptly responds to the treatment with IL-1β blockade, with few exceptions. In many autoinflammatory diseases, there is a state of increased release of IL-1β. The precursor is converted to the active form through the action of Caspase-1. This enzyme is also found in the inactive form in tissue macrophages and dendritic cells, and requires conversion by autocatalysis to become active. However, it is in the active form in circulating human blood monocytes. The release of IL-1β from blood monocytes in highly controlled and takes several hours in healthy subjects. In patients with an autoinflammatory disease, more mature IL-1β is released when compared to healthy subjects, which leads to exacerbated inflammation. Despite of this group of diseases being characterized by severe inflammation, the amount of IL-1β released is not much greater than that released from healthy subjects. Currently, human anti-IL-1β monoclonal antibody is being developed to treat autoinflammatory diseases. Canakinumab was approved by Food and Drug Administration (FDA) in 2009 for the treatment of cryopyrin-associated periodic syndromes (CAPS). In 2016, Canakinumab was also approved for treating TRAPS, hyperimmunoglobulin D syndrome (HIDS)/mevalonate kinase deficiency (MKD) and familial Mediterranean fever (FMF) [50, 51].
IL-1Ra is a receptor antagonist. It is synthetized by the same cells that produce IL-1α and IL-1β (monocytes, macrophages, dendritic cells and others). The binding of IL-1Ra to the receptor does not involve conformational change and, hence, the co-receptor IL-1RAcP is not recruited. IL-1Ra regulates the activity of IL-1. However, to efficiently block the IL-1 response, it has to be in an amount approximately 100-folds greater than the agonists cytokines. Anakinra is a recombinant version of IL1-Ra used in the treatment of rheumatoid arthritis [44, 53].
IL-18 is synthetized as an inactive precursor, and, similarly to IL-1β, it needs cleavage by caspase-1 to become in the active form. The precursor form is present in almost all cells of the human body, likewise IL-1α. Usually diseases related to IL-18 appear when there is an imbalance between the amount of IL-18 and IL-18 binding protein, which is responsible for limiting the level of activity of IL-18. This cytokine is released usually from dying cells, once again like IL-1α [51, 54].
IL-18 was first described as “IFN-γ-inducing factor”, because it was discovered as an inducer of IFN-γ production. However, alone, IL-18 does not induce the production of considerable amounts of IFN-γ. For that to happen, it has to act in association with IL-12. IL-18 promotes TH1 and Th2 cells responses, and also induces IL-13 production in T cells and NK cells together with IL-2. It also enhances NK toxicity by promoting the expression of Fas ligand in NK cells. IL-18 is involved in several autoimmune diseases, in myocardial infarction, metabolic syndromes and others [44, 55].
IL-33 is an alarmin cytokine, rapidly released upon cellular damage. It is involved mainly in type 2 immunity and inflammation. It acts in Th2, in innate lymphoid cell-2 (ILC2), and in activated M2 polarized macrophages. This cytokine is expressed by keratinocytes, epithelial and endothelial cells, and monocytes. IL-33 is produced as a precursor, but, contrary to IL-1, caspase-1 transforms it in an inactive cytokine. The precursor is active and other proteases can cleavage it in more potent forms. IL-33 induces Th2 response binding to ST2 and next recruiting IL-1RacP. The activity of IL-33 is controlled essentially by the binding to soluble ST2 and soluble IL-1RAcP. Levels of increased soluble ST2 are present in various inflammatory diseases, such as systemic lupus erythematosus and rheumatoid arthritis [44, 50, 56].
IL-36α, IL-36β and IL-36γ are receptor agonists, while IL-36Ra is a receptor antagonist that blocks the activation of the receptor and competes with IL-36, acting as a regulator. These cytokines are included in the interleukin-1 family because they share homology to the first members of the family. Their homology to IL-Ra and IL-1β varies from 20 to 52%. Furthermore, IL-36β and IL-36γ have the core 12-fold, β-trefoil structure and lack a signal peptide, particular features of IL-1 family. All these cytokines need an N-terminal processing to become in the active form, but the enzyme responsible for this process is still not known. IL-36 cytokines are predominantly found in skin cells, and that is why they are related with several skin disorders, such as psoriasis. After binding to the receptor (IL-36R and IL-1RAcP co-receptor), dendritic cells are activated and participate in the polarizing of T-helper responses [50, 52, 57].
Different from the other members of the family, IL-37 is an anti-inflammatory cytokine, and reduces innate inflammation as well as acquired immune responses. Its presence has already been reported in skin, tonsils, esophagus, placenta, breast, prostate and colon. There are five different isoforms of IL-37: IL-37a, IL-37b, IL-37c, IL-37d and IL-37e, expressed in different locations of the human body. So far, IL-37b, which contains a 12β-strand trefoil, typical of the IL-1 family, appears to be the most biologically active, and therefore the object of the majority of studies. IL-37 suppresses the production of pro-inflammatory cytokines, such as IL-1A, IL-6, CC chemokine ligand (CCL-12), colony-stimulating factors (CSF-1 and CSF-2), chemokine ligand-13 (CXCL-13), IL-1β, IL23-A and IL1RA, and also inhibits dendritic cell activation [58, 59, 60].
IL-38 is the most recent member of the Interleukin-1 family, identified in 2001. It binds to the same receptor that the IL-36 cytokines, IL-36R. However, it acts as an antagonist, similarly to IL-36Ra. Therefore, IL-38 acts reducing inflammatory response. IL-38 shares 41% homology with IL-1Ra and 43% with IL-36Ra. This cytokine is present in skin, tonsil, thymus, spleen, fetal liver and salivary glands. The properties and biological activities of IL-38 are still being studied [52, 61, 62].
4.2. Interleukin-2 family
The IL-2 cytokine family, also known as the common γ-chain family, is composed by ILs 2, 4, 7, 9, 15 and 21. All these ILs bind to the common γc receptor, also called CD132. These cytokines act as growth and proliferation factors for progenitors and mature cells [44, 63].
IL-2 is the first member of the common γ-chain family, previously known as T cell growth factor. This cytokine is mainly produced by CD4+ and CD8+ T cells, but can be also expressed by dendritic cells and NKs. The IL-2R is composed by three subunits (CD25, CD122 and common γc), all necessary to binding to IL-2. IL-2 acts in the development of regulatory T (Treg) cells, as a B cell growth factor, stimulates antibody synthesis and promotes proliferation and differentiation of NK cells and T helpers. IL-2 has been extensively used as an anti-cancer therapy [44, 63, 64, 65].
IL-4 is produced by Th2 cells, basophils, eosinophils and mastocytes. It has two receptors: IL4-R type I, which binds only to IL-4 and is composed by CD124 (IL-4rα) and CD 132; and type II, which binds to IL-4 and to IL-13, and it consists in IL-4Rα and IL-13Rα1. These receptors are spread all over the human body. IL-4 is known to play several different roles, regulating allergic conditions and activating the immune response against extracellular parasites (B cell class switching to IgE). It is the main cytokine to stimulate development of Th2 cells. Dupilimab is an IL-4 receptor antagonist approved in 2017 by FDA for treatment of eczema [44, 66, 67].
IL-7 is a homeostatic cytokine. It can be found essentially in T cells, progenitors of B cells and bone marrow macrophages. As the other members of the family, its receptor (IL-7R) consists in the common γ-chain fraction, along with another unit, the IL-7Rα (CD127). IL-7 is involved in the survival and proliferation of thymocytes and in the development of naïve and memory B and T cells, mature T cells and NKs. Deficiencies related to IL-7 result in immunodeficiency, autoimmune diseases and leukemia [44, 68].
IL-9 is mainly produced by Th2 cells, but it is also expressed in less amounts by eosinophils and by mastocytes of asthmatic patients. Its receptor, IL-9R, is composed by the CD132 and IL-9Rα units. IL-9 is a potent growth factor for T cells and mastocytes, and some of it activities include the inhibition of cytokine production by Th1 cells, IgE production, and mucus secretion by bronchial epithelium. Recently, a new subset of effector T cells was discovered, Th9, and it is believed that it is intimately related with IL-9 production. IL-9 is associated to allergic diseases and protection from helminthic infections. This cytokine can be found in elevated amounts in Hodgkin lymphoma, hence, IL-9 antagonists are being studied as a potential treatment for this disease [44, 69, 70].
IL-15 is structurally homologous to IL-2. The receptor, IL-15R, is composed by the CD132 subunit common to the family, and also by IL-15Rα and IL-2Rβ chains. IL-15 is produced by keratinocytes, skeletal muscle cells, monocytes and activated CD4+ T cells, in response to signals that trigger innate immunity. IL-15 has some identical functions to IL-2, such as T cell activation and stimulation of NK cell proliferation, but it also involved with CD8+ memory cell, NK cell, and NKT-cell homeostasis. Increased levels of IL-15 were reported in autoimmune disorders, such as rheumatoid arthritis, psoriasis and celiac disease [44, 71].
IL-21 is produced by T cells, NKT cells and Th17. The receptor, IL-21, is present in various parts of the human body and consists in CD132 and IL-21R. This cytokine is involved with B cells functions, and also increases the proliferation of CD8+ T cells, NK cells and NKT. IL-21 is currently being studied as anti-cancer therapy [44, 64].
5. Other cytokines
In addition to the aforementioned cytokines, other also deserves attention, such as chemokines. The chemokines represent a large family of structurally homologous cytokines that stimulate leukocytes movement and regulate the migration of them from the blood to tissues, in a process named chemotaxis. They control homeostatic immune cells, such as neutrophils, B cells, and monocytes, trafficking between the bone marrow, blood and peripheral tissues. Therefore, they can be classified as chemotactic cytokines [33, 72].
There are about 50 human chemokines, classified into 4 families according to the location of N-terminal cysteine residues. The two major families are CC and CXC chemokines, in which the cysteine residues are adjacent on CC family, and are separated by one amino acid on CXC family. In general, members of CC chemokines are chemotactic for monocytes, and a small subset of lymphocytes, while CXC chemokines are more specific for neutrophils. The best-known chemokine is IL-8, or CXCL8, which belongs to the CXC chemokine family, and is responsible for neutrophil recruitment and for the maintenance of the inflammatory reaction. On the other hand, the monocyte chemoattractant protein-1 (MCP-1) or CCL2, and CCL11 or eotaxin, are examples of CC chemokines, which acts on recruitment of a variety of leukocytes, but especially monocytes, and eosinophils, respectively [33, 73, 74].
The chemokines receptors are expressed on all leukocytes and are divided in two groups: G protein-coupled receptors with seven-transmembrane α-helical segments, and atypical receptors, which appear to attenuate inflammation by scavenging chemokines, independently of G protein. Each receptor subtype is capable of binding to various chemokines of the same family, and a single chemokine can bind to more than one receptor. Despite of this factor, a lot of chemokines presents a great tissue and receptor specificity [72, 73].
Chemokines can be produced constitutively in various tissues, and are responsible for regulating the traffic of leucocytes, especially lymphocytes, through peripheral lymphoid tissues. However, the best-known activity of chemokines is the involvement on inflammatory reactions. Recruitment of macrophages, neutrophils and T cells to the site of inflammation is strongly stimulated by chemokines. In fact, they represent a secondary pro-inflammatory mediator that is induced by primary pro-inflammatory mediators, such as IL-1 or TNF. In general, members of the chemokines family induce recruitment of well-defined leukocyte subsets, differently of the classic leukocyte chemoattractants. They induce the movement of leukocytes, and consequently promote their migration to a specific local, by stimulating actin filaments [33, 72, 73, 74].
Beyond the involvement of the chemokines on acute inflammatory reactions, and the regulation of the traffic of leukocytes through peripheral lymphoid, independently of the presence of inflammation, some kind of chemokines can promote angiogenesis and wound healing, associated mostly with CXC family, while other are involved in the development of diverse nonlymphoid organs [73, 74]. They also have an important role in the priming of naive T cells, in effector and memory cell differentiation, and in regulatory T cell function [72].
Besides chemokines, there are cytokines that stimulates hematopoiesis, such as the colony-stimulating factors (CSFs), which contributes to the growth of progenitors of monocytes, neutrophils, eosinophils and basophils, as well as activating macrophages. Immune and inflammatory reactions uses leukocytes, due to the recruitment induced by some kinds of cytokines, so new must be produced [73, 74]. Additionally, the GM-CSF (granulocyte-macrophage colony-stimulating factor) and M-CSF (macrophage colony-stimulating factor) have, like some other cytokines, a pro-inflammatory action, and exhibit a connexon between the expression of them and TNF, IL-1, IL-23 and IL-17 [75].
Finally, other cytokines can be highlighted: TGF-β, LIF, Eta-1 and oncostatin M. The TGF-β is responsible for the chemoattraction of monocytes and macrophages, but also it has an anti-inflammatory effect, by inhibiting the lymphocyte proliferation. LIF and oncostatin M induce the production of acute-phase protein, while Eta-1 stimulates the production of IL-2, and inhibits the production of IL-10 [73].
6. Cytokines and autoantibodies
On this topic, the association between the cytokines and autoimmune diseases will be reviewed, but emphasis will be given to these ones: systemic lupus erythematosus, type 1 diabetes mellitus, multiple sclerosis, vitiligo and heart failure.
The impossibility of differentiating between own and non-own (strange) could result in the synthesis of antibodies against the components of the organism (autoantibodies), which could be extremely deleterious [73]. The organism is characterized by a failure of the normal mechanism of self-tolerance, resulting in reactions against one’s cells, in the absence of any present infection or another cause, known as autoimmunity, and the diseases caused by this phenomenon are referred as autoimmune diseases [33, 76].
The pathogenesis of autoimmune diseases involves mainly the genetic susceptibility, and previous infections. In relation to infections, it is observed a recruitment of leukocytes into the affected tissue, resulting in the activation of tissue antigen-presenting cells (APC). Consequently, these APCs express costimulators and secrete T cell-activating cytokines, contributing to the breakdown of T cell tolerance. Therefore, the infection promotes the activation of T cells that are not specific for the pathogen, in a process called bystander activation. Additionally, microbes may engage toll-like receptors (TRLs) on dendritic cells, resulting on production of lymphocyte-activating cytokines, leading to the autoantibody production. This process was demonstrated in mouse models, and its influence in human autoimmune diseases remains unclear [33].
The systemic lupus erythematosus (SLE) is an autoimmune disease, characterized by the involvement of immune complexes formed from autoantibodies and their specific antigens that are responsible for the clinical manifestation, especially glomerulonephritis, arthritis and vasculitis. The peripheral blood lymphocytes of patient presents an excessive production and response to type 1 IFNs, but the involvement of this cytokines on the development of the diseases is still uncertain [33]. In these patients, for instance, serum IFN-α and IFN-α-induced gene expression are frequently observed, implying that the molecular pathogenesis of this condition is mediated by type I IFN. It has also been shown that IFN-γ serum levels are increased in SLE patients, and in mouse models, the receptor of this cytokine was necessary to the disease development. The massive amount of circulating IFN correlates to disease severity, which is likely to be triggered by excessive pDC activation. Recently, clinical trials evaluating anti-IFN-α monoclonal antibodies for SLE have been conducted, exhibiting promising results. Moreover, a trial evaluating a monoclonal antibody that binds IFN-γ was conducted, but no significant improvements in the efficacy outcome measures were observed. Additionally, a recent study demonstrated that keratinocytes may participate on the pathophysiological of the cutaneous manifestation of the SLE, by increasing cell apoptosis and producing pro-inflammatory cytokines, especially IL-23, IL-12, IL-6, IL-17, (Th17-related cytokines), IL-10 and TFG-β [16, 30, 77, 78].
In parallel, another autoimmune disease widely studied that involves cytokines, besides several other factors, is the type 1 diabetes mellitus. This disease is characterized by pancreatic β cells destruction, which it is due to hypersensitivity reactions mediated by CD4+ TH 1 cells reactive with islet antigens, the effect of cytotoxic T lymphocyte on lysis of islet cells, and local production of cytokines, especially TNF, IL-1, IL-21 and IFN-α. In some cases, the islets show cellular necrosis and lymphocytic infiltration, consisted of both CD4+ and CD8+ cells. Remaining islet cells often express class II MHC molecules, an effect of local production of INF-γ by the T cells [33, 73, 79]. The onset of young age of this disease may be associated with upregulation of growth factors, especially GM-CSF and IL-7. Other mediators overexpressed are the pro-inflammatory cytokine IL-1β, the regulatory cytokine IL-10, IL-27, and some Th17 cytokines (IL-17, IL-21, IL- 23). Additionally, patients that involve to ketoacidosis, a serious complication of the disease, have a tendency for higher IL-8 and IL-10 levels [80].
In the same way, it stands out the rheumatoid arthritis, a chronic and systemic autoimmune disease described as a progressive disability on joints, particularly of the fingers, shoulders, elbows, knees and ankles that can promote systemic consequences like cardiovascular, pulmonary and skeletal disorders. It is characterized by the production of autoantibodies, like rheumatoid factor, cytokines, chemokines, hyperplasic synovium, osteoclastogensis and angiogenesis. The pro-inflammatory cytokines IL-1α/β, IL-8, IL-6, TNF-α, INF-y and some CSFs are responsible for the pathogenesis of this disease, and are involved with the intracellular molecular signaling pathway that causes chronic inflammation on synovial membrane. These cytokines, especially TNF-α, activates the leukocytes endothelial cells and synovial fibroblasts, and stimulates the production of collagenases that are responsible for the destruction of the cartilage, ligaments and tendons of the joints. Therefore, monoclonal antibody drugs, such as anti-TNF are approved for treatment of this disease [33, 75, 76, 81].
It is also believed that bone destruction in rheumatoid arthritis is due to overexpression of the TNF family cytokine receptor activator of nuclear factor KB (RANK), an essential mediator that promotes maturation and activation of osteoclasts [33, 76]. Therefore, the cytokines on rheumatoid arthritis promote the autoimmunity, the destruction of joint tissue and maintain the synovial inflammation [82].
The multiple sclerosis is a neurodegenerative autoimmune disease of high mortality in adults, characterized by a chronic inflammation in the central nervous system with secondary demyelination due to leukocyte and cytokines infiltration of brain tissue and spinal cord. Clinical manifestations are weakness, paralysis and ocular symptoms [33, 73]. A recent study proposed the role of Th1 lymphocytes in the pathogenesis of the brain inflammation, with several cytokines involvement. Th1 lymphocytes produces mainly IFNγ (type II IFN) that is responsible for the production of other pro-inflammatory cytokines, and chemoattractants, such as IL-2, IFNγ, CC chemokines, like CCL5, CCL11 and CCL27 and CXC chemokines, especially CXCL1 and CXCL10. On the other hand, lower levels of circulating type I IFN are observed. Therefore, unlike SLE, multiple sclerosis treatment involves the administration of IFN-β. Additionally, an upregulation of CCL27 was found in cerebrospinal fluid of multiple sclerosis patients, demonstrating the possibility of its involvement on activation and migration of autoreactive immune effectors in the brain, and consequently a potential contribution for the pathogenesis of this disease [83].
Vitiligo, is another autoimmune disease, characterized by the skin depigmentation, which is associated to the production of antibodies against the melanocytes, and it is more frequent in patients that have other autoimmune diseases, like Grave’s disease [73]. A variety of cytokines are increased in vitiligo patients in relation to healthy people. A recent systematic review demonstrated an association between the expression of some kind of cytokines in vitiligo skin, especially INF-y, TGF-β, IL-1β, IL-17, and the chemokines CXCL9, CXCL10 and CXCL12. IFN-y and IL-1β are closely related to the pathogenesis of vitiligo, but serum TGF-β and IL-17 are more abundantly expressed in relation to the others [84].
Finally, another disease that has the participation of cytokines on its pathogenesis is the heart failure, a chronic disease characterized by a cardiac impairment due to hypertension, myocardial infarction, arrhythmias and other heart diseases. A recent evidence showed the involvement of the adaptive immune system in the development and progression of heart failure, which is related to high mortality in adults. T cells, particularly TH1, and TH17 and B1 lymphocyte, contribute to the pathologic chronic inflammation, and cell migration. The inflammatory component of this disease, which has a closely relation to the morbidity and mortality, are the cytokines, including TNF-α, TNF-β, IL-1, IL-6, IL-7, IL-10 and IFN-y, chemokines and cardiac autoantibodies. Those factors are associated with cardiomyocyte death and tissue remodeling by fibrosis, contributing to the left ventricle dysfunction, and consequently to disease progression. In detail, initially the dendritic cells and other antigen-presenting cells can process specific proteins of the myocardial tissue and theirs contact with memory B cells promotes the release of autoantibodies, and consequently activates pro-apoptotic pathways, by antigen-dependent cell cytotoxicity, and complement-mediated cell cytotoxicity in health myocytes. Another characteristic of the pathogenesis of heart disease is the production of inflammatory mediators by B cells, such pro-inflammatory cytokines (TNF-α and IL-6) and chemokines, which recruit monocytes involved with inflammation and heart remodeling, beyond the activation of T lymphocytes, leading to the production of other specific inflammatory cytokines (IFN-y and IL-2) [73, 85].
Selective immunosuppression of B-lymphocytes may be a promising therapeutic on acute and chronic heart failure, as the blockage of the immune mediators, such cytokines, once they are involved to the propagation of the disease [85].
In sum, different kinds of cytokines are involved on autoimmune diseases, which plays an important role especially on inflammatory process, and contributing to the pathogenesis, in most cases. Studies have been performed, in order to establish the association between cytokines and the evaluation of these diseases, with the objective of developing therapeutic strategies, such as anti-TNF for rheumatoid arthritis.
7. Conclusion
In this chapter, the main aspects regarding the different types of cytokines and their main functions were reviewed. Hence, the comprehensive and fundamental role of cytokines in the immune system could be thoroughly investigated. Additionally, the contribution of these molecules to the development of diseases, particularly related to autoimmunity, as well as its use as treatment approach for some clinical conditions was explored.
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A novel IL-23p19/Ebi3 (IL-39) cytokine mediates inflammation in lupus-like mice. European Journal of Immunology. 2016;46:1343-1350'},{id:"B50",body:'Garlanda C, Dinarello CA, Mantovani A. The interleukin-1 family: Back to the future. Immunity. 2013;39:1003-1018'},{id:"B51",body:'Dinarello CA. Interleukin-1 in the pathogenesis and treatment of inflammatory diseases. Blood. 2011;117:3720-3732'},{id:"B52",body:'Palomo J, Dietrich D, Martin P, Palmer G, Gabay C. The interleukin (IL)-1 cytokine family – Balance between agonists and antagonists in inflammatory diseases. Cytokine. 2015;76:25-37'},{id:"B53",body:'Mertens M, Singh JA. Anakinra for rheumatoid arthritis: A systematic review. The Journal of Rheumatology. 2009;36:1118-1125'},{id:"B54",body:'Novick D, Kim S, Kaplanski G, Dinarello CA. Interleukin-18, more than a Th1 cytokine. Seminars in Immunology. 2013;25:439-448'},{id:"B55",body:'Dinarello CA, Novick D, Kim S, Kaplanski G. Interleukin-18 and IL-18 binding protein. 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Autoimmunity Reviews. 2015;14:1170-1175'},{id:"B61",body:'Kumar S, McDonnell PC, Lehr R, Tierney L, Tzimas MN, Griswold DE, et al. Identification and initial characterization of four novel members of the interleukin-1 family. The Journal of Biological Chemistry. 2000;275:10308-10314'},{id:"B62",body:'Lin H, Ho AS, Haley-Vicente D, Zhang J, Bernal-Fussell J, Pace AM, et al. Cloning and characterization of IL-1HY2, a novel interleukin-1 family member. The Journal of Biological Chemistry. 2001;276:20597-20602'},{id:"B63",body:'Sim GC, Radvanyi L. The IL-2 cytokine family in cancer immunotherapy. Cytokine & Growth Factor Reviews. 2014;25:377-390'},{id:"B64",body:'Lin JX, Leonard WJ. The common cytokine receptor gamma chain family of cytokines. Cold Spring Harbor Perspectives in Biology. 2017 [Epub ahead of print]'},{id:"B65",body:'Morgan DA, Ruscetti FW, Gallo R. Selective in vitro growth of T lymphocytes from normal human bone marrows. Science. 1976;193:1007-1008'},{id:"B66",body:'Paul WE. History of interleukin-4. Cytokine. 2015;75:3-7'},{id:"B67",body:'Shirley M. Dupilumab: First global approval. Drugs. 2017;77:1115-1121'},{id:"B68",body:'Lundstrom W, Fewkes NM, Mackall CL. IL-7 in human health and disease. Seminars in Immunology. 2012;24:218-224'},{id:"B69",body:'Kaplan MH. Th9 cells: Differentiation and disease. Immunological Reviews. 2013;252:104-115'},{id:"B70",body:'Zhao P, Xiao X, Ghobrial RM, Li XC. IL-9 and Th9 cells: Progress and challenges. International Immunology. 2013;25:547-551'},{id:"B71",body:'Abadie V, Jabri B. IL-15: A central regulator of celiac disease immunopathology. Immunological Reviews. 2014;260:221-234'},{id:"B72",body:'Griffith JW, Sokol CL, Luster AD. Chemokines and chemokine receptors: Positioning cells for host defense and immunity. Annual Review of Immunology. 2014;32:659-702'},{id:"B73",body:'Delves PJ, Martin SJ, Burton DR, Roitt IM. Roitt’s Essential Immunology. 12th ed. Oxford: Blackwell Publishing Limited; 2011'},{id:"B74",body:'Graves DT, Chemokines JY. A family of chemotactic cytokines. Critical Reviews in Oral Biology and Medicine: An Official Publication of the American Association of Oral Biologists. 1995;6:109-118'},{id:"B75",body:'Hamilton JA. Colony-stimulating factors in inflammation and autoimmunity. Nature Reviews Immunology. 2008;8:533-544'},{id:"B76",body:'Alam J, Jantan I, Bukhari SNA. Rheumatoid arthritis: Recent advances on its etiology, role of cytokines and pharmacotherapy. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie. 2017;92:615-633'},{id:"B77",body:'Werth VP, Fiorentino D, Sullivan BA, Boedigheimer MJ, Chiu K, Wang C, et al. Brief report: Pharmacodynamics, safety, and clinical efficacy of AMG 811, a human anti-interferon-gamma antibody, in patients with discoid lupus erythematosus. Arthritis & Rheumatology. 2017;69:1028-1034'},{id:"B78",body:'Zhang YP, Wu J, Han YF, Shi ZR, Wang L. Pathogenesis of cutaneous lupus erythema associated with and without systemic lupus erythema. Autoimmunity Reviews. 2017;16:735-742'},{id:"B79",body:'Khan WA. Recombinant interferon alpha-2b is a high-affinity antigen for type 1 diabetes autoantibodies. Canadian Journal of Diabetes. 2017;41:217-223'},{id:"B80",body:'Alnek K, Kisand K, Heilman K, Peet A, Varik K, Uibo R. Increased blood levels of growth factors, proinflammatory cytokines, and Th17 cytokines in patients with newly diagnosed type 1 diabetes. PLoS One. 2015;10:e0142976'},{id:"B81",body:'Conigliaro P, Perricone C, Benson RA, Garside P, Brewer JM, Perricone R, et al. The type I IFN system in rheumatoid arthritis. Autoimmunity. 2010;43:220-225'},{id:"B82",body:'McInnes IB, Schett G. Cytokines in the pathogenesis of rheumatoid arthritis. Nature Reviews Immunology. 2007;7:429-442'},{id:"B83",body:'Khaibullin T, Ivanova V, Martynova E, Cherepnev G, Khabirov F, Granatov E, et al. Elevated levels of proinflammatory cytokines in cerebrospinal fluid of multiple sclerosis patients. Frontiers in Immunology. 2017;8:531'},{id:"B84",body:'Speeckaert R, Speeckaert M, De Schepper S, van Geel N. Biomarkers of disease activity in vitiligo: A systematic review. Autoimmunity Reviews. 2017;16:937-945'},{id:"B85",body:'Sanchez-Trujillo L, Vazquez-Garza E, Castillo EC, Garcia-Rivas G, Torre-Amione G. Role of adaptive immunity in the development and progression of heart failure: New evidence. Archives of Medical Research. 2017;48:1-11'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Vinicius L. Ferreira",address:null,affiliation:'
Pharmaceutical Sciences Graduate Program, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
Pharmaceutical Sciences Graduate Program, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
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The values of our business are based on the same ones that any scientist applies to their research. We have created a culture of respect, collegiality and collaboration within a relaxed, friendly, and progressive atmosphere.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We’re open to constructive criticism and committed to learning from it.
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Disruptiveness - We are hungry for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a young and vibrant company where great young people do great work. We offer a creative, dedicated, committed, passionate, and above all, fun environment where you can work, travel, meet world-renowned researchers and grow your career and experience.
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If this sounds like a place that you would like to work, drop us a line and tell us why you think you could be the right person for us. Send your CV and cover letter to jobs@intechopen.com.
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What else we offer:
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Collaboration with scientists and publishing experts worldwide
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Insight into book editorial and production processes
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Experience in technical editing processes
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Adequate guidance in accomplishing career objectives
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