10.11: Plant Growth - Biology

10.11: Plant Growth - Biology

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Learning Objectives

  • Identify the key elements and processes in plant growth

Most plants continue to grow throughout their lives. Like other multicellular organisms, plants grow through a combination of cell growth and cell division. Cell growth increases cell size, while cell division (mitosis) increases the number of cells.

How Plants Grow

Most plants continue to grow throughout their lives. Cell growth increases cell size, while cell division (mitosis) increases the number of cells. As plant cells grow, they also become specialized into different cell types through cellular differentiation. Once cells differentiate, they can no longer divide. How do plants grow or replace damaged cells after that?

The key to continued growth and repair of plant cells is meristem. Meristem is a type of plant tissue consisting of undifferentiated cells that can continue to divide and differentiate.

Apical meristems are found at the apex, or tip, of roots and buds, allowing roots and stems to grow in length and leaves and flowers to differentiate. Roots and stems grow in length because the meristem adds tissue “behind” it, constantly propelling itself further into the ground (for roots) or air (for stems). Often, the apical meristem of a single branch will become dominant, suppressing the growth of meristems on other branches and leading to the development of a single trunk. In grasses, meristems at the base of the leaf blades allow for regrowth after grazing by herbivores—or mowing by lawnmowers.

Apical meristems differentiate into the three basic types of meristem tissue which correspond to the three types of tissue: protoderm produces new epidermis, ground meristem produces ground tissue, and procambium produces new xylem and phloem. These three types of meristem are considered primary meristem because they allow growth in length or height, which is known as primary growth.

Secondary meristems allow growth in diameter (secondary growth) in woody plants. Herbaceous plants do not have secondary growth. The two types of secondary meristem are both named cambium, meaning “exchange” or “change.” Vascular cambium produces secondary xylem (toward the center of the stem or root) and phloem (toward the outside of the stem or root), adding growth to the diameter of the plant. This process produces wood, and builds the sturdy trunks of trees. Cork cambium lies between the epidermis and the phloem, and replaces the epidermis of roots and stems with bark, one layer of which is cork.

Woody plants grow in two ways. Primary growth adds length or height, mediated by apical meristem tissue at the tips of roots and shoots—which is difficult to show clearly in cross-sectional diagrams. Secondary growth adds to the diameter of a stem or root; vascular cambium adds xylem (inward) and phloem (outward), and cork cambium replaces epidermis with bark.

Watch this time-lapse video of plant growth. Note that there isn’t any narration in the video.

A YouTube element has been excluded from this version of the text. You can view it online here:

Learning Objectives

Most plants continue to grow as long as they live. They grow through a combination of cell growth and cell division (mitosis). The key to plant growth is meristem, a type of plant tissue consisting of undifferentiated cells that can continue to divide and differentiate. Meristem allows plant stems and roots to grow longer (primary growth) and wider (secondary growth).

Stem Growth

Growth in plants occurs as the stems and roots lengthen. Some plants, especially those that are woody, also increase in thickness during their life span. The increase in length of the shoot and the root is referred to as primary growth, and is the result of cell division in the shoot apical meristem. Secondary growth is characterized by an increase in thickness or girth of the plant, and is caused by cell division in the lateral meristem. Figure 4 shows the areas of primary and secondary growth in a plant. Herbaceous plants mostly undergo primary growth, with hardly any secondary growth or increase in thickness. Secondary growth or “wood” is noticeable in woody plants; it occurs in some dicots, but occurs very rarely in monocots.

Some plant parts, such as stems and roots, continue to grow throughout a plant’s life: a phenomenon called indeterminate growth. Other plant parts, such as leaves and flowers, exhibit determinate growth, which ceases when a plant part reaches a particular size.

Primary Growth

Most primary growth occurs at the apices, or tips, of stems and roots. Primary growth is a result of rapidly dividing cells in the apical meristems at the shoot tip and root tip. Subsequent cell elongation also contributes to primary growth. The growth of shoots and roots during primary growth enables plants to continuously seek water (roots) or sunlight (shoots).

The influence of the apical bud on overall plant growth is known as apical dominance, which diminishes the growth of axillary buds that form along the sides of branches and stems. Most coniferous trees exhibit strong apical dominance, thus producing the typical conical Christmas tree shape. If the apical bud is removed, then the axillary buds will start forming lateral branches. Gardeners make use of this fact when they prune plants by cutting off the tops of branches, thus encouraging the axillary buds to grow out, giving the plant a bushy shape.

Watch this BBC Nature video showing how time-lapse photography captures plant growth at high speed.

Secondary Growth

The increase in stem thickness that results from secondary growth is due to the activity of the lateral meristems, which are lacking in herbaceous plants. Lateral meristems include the vascular cambium and, in woody plants, the cork cambium (see Figure 4).

The vascular cambium is located just outside the primary xylem and to the interior of the primary phloem. The cells of the vascular cambium divide and form secondary xylem (tracheids and vessel elements) to the inside, and secondary phloem (sieve elements and companion cells) to the outside. The thickening of the stem that occurs in secondary growth is due to the formation of secondary phloem and secondary xylem by the vascular cambium, plus the action of cork cambium, which forms the tough outermost layer of the stem. The cells of the secondary xylem contain lignin, which provides hardiness and strength.

In woody plants, cork cambium is the outermost lateral meristem. It produces cork cells (bark) containing a waxy substance known as suberin that can repel water. The bark protects the plant against physical damage and helps reduce water loss. The cork cambium also produces a layer of cells known as phelloderm, which grows inward from the cambium. The cork cambium, cork cells, and phelloderm are collectively termed the periderm. The periderm substitutes for the epidermis in mature plants. In some plants, the periderm has many openings, known as lenticels, which allow the interior cells to exchange gases with the outside atmosphere (Figure 5). This supplies oxygen to the living and metabolically active cells of the cortex, xylem and phloem.

Annual Rings

The activity of the vascular cambium gives rise to annual growth rings. During the spring growing season, cells of the secondary xylem have a large internal diameter and their primary cell walls are not extensively thickened. This is known as early wood, or spring wood. During the fall season, the secondary xylem develops thickened cell walls, forming late wood, or autumn wood, which is denser than early wood. This alternation of early and late wood is due largely to a seasonal decrease in the number of vessel elements and a seasonal increase in the number of tracheids. It results in the formation of an annual ring, which can be seen as a circular ring in the cross section of the stem (Figure 6). An examination of the number of annual rings and their nature (such as their size and cell wall thickness) can reveal the age of the tree and the prevailing climatic conditions during each season.

Growth Responses

A plant’s sensory response to external stimuli relies on chemical messengers (hormones). Plant hormones affect all aspects of plant life, from flowering to fruit setting and maturation, and from phototropism to leaf fall. Potentially every cell in a plant can produce plant hormones. They can act in their cell of origin or be transported to other portions of the plant body, with many plant responses involving the synergistic or antagonistic interaction of two or more hormones. In contrast, animal hormones are produced in specific glands and transported to a distant site for action, and they act alone.

Plant hormones are a group of unrelated chemical substances that affect plant morphogenesis. Five major plant hormones are traditionally described: auxins (particularly IAA), cytokinins, gibberellins, ethylene, and abscisic acid. In addition, other nutrients and environmental conditions can be characterized as growth factors.


The term auxin is derived from the Greek word auxein, which means “to grow.” Auxins are the main hormones responsible for cell elongation in phototropism and gravitropism. They also control the differentiation of meristem into vascular tissue, and promote leaf development and arrangement. While many synthetic auxins are used as herbicides, IAA is the only naturally occurring auxin that shows physiological activity. Apical dominance—the inhibition of lateral bud formation—is triggered by auxins produced in the apical meristem. Flowering, fruit setting and ripening, and inhibition of abscission (leaf falling) are other plant responses under the direct or indirect control of auxins. Auxins also act as a relay for the effects of the blue light and red/far-red responses.

Commercial use of auxins is widespread in plant nurseries and for crop production. IAA is used as a rooting hormone to promote growth of adventitious roots on cuttings and detached leaves. Applying synthetic auxins to tomato plants in greenhouses promotes normal fruit development. Outdoor application of auxin promotes synchronization of fruit setting and dropping to coordinate the harvesting season. Fruits such as seedless cucumbers can be induced to set fruit by treating unfertilized plant flowers with auxins.


The effect of cytokinins was first reported when it was found that adding the liquid endosperm of coconuts to developing plant embryos in culture stimulated their growth. The stimulating growth factor was found to be cytokinin, a hormone that promotes cytokinesis (cell division). Almost 200 naturally occurring or synthetic cytokinins are known to date. Cytokinins are most abundant in growing tissues, such as roots, embryos, and fruits, where cell division is occurring. Cytokinins are known to delay senescence in leaf tissues, promote mitosis, and stimulate differentiation of the meristem in shoots and roots. Many effects on plant development are under the influence of cytokinins, either in conjunction with auxin or another hormone. For example, apical dominance seems to result from a balance between auxins that inhibit lateral buds, and cytokinins that promote bushier growth.


Gibberellins (GAs) are a group of about 125 closely related plant hormones that stimulate shoot elongation, seed germination, and fruit and flower maturation. GAs are synthesized in the root and stem apical meristems, young leaves, and seed embryos. In urban areas, GA antagonists are sometimes applied to trees under power lines to control growth and reduce the frequency of pruning.

GAs break dormancy (a state of inhibited growth and development) in the seeds of plants that require exposure to cold or light to germinate. Abscisic acid is a strong antagonist of GA action. Other effects of GAs include gender expression, seedless fruit development, and the delay of senescence in leaves and fruit. Seedless grapes are obtained through standard breeding methods and contain inconspicuous seeds that fail to develop. Because GAs are produced by the seeds, and because fruit development and stem elongation are under GA control, these varieties of grapes would normally produce small fruit in compact clusters. Maturing grapes are routinely treated with GA to promote larger fruit size, as well as looser bunches (longer stems), which reduces the instance of mildew infection (Figure 7).

Abscisic Acid

The plant hormone abscisic acid (ABA) was first discovered as the agent that causes the abscission or dropping of cotton bolls. However, more recent studies indicate that ABA plays only a minor role in the abscission process. ABA accumulates as a response to stressful environmental conditions, such as dehydration, cold temperatures, or shortened day lengths. Its activity counters many of the growth-promoting effects of GAs and auxins. ABA inhibits stem elongation and induces dormancy in lateral buds.

ABA induces dormancy in seeds by blocking germination and promoting the synthesis of storage proteins. Plants adapted to temperate climates require a long period of cold temperature before seeds germinate. This mechanism protects young plants from sprouting too early during unseasonably warm weather in winter. As the hormone gradually breaks down over winter, the seed is released from dormancy and germinates when conditions are favorable in spring. Another effect of ABA is to promote the development of winter buds; it mediates the conversion of the apical meristem into a dormant bud. Low soil moisture causes an increase in ABA, which causes stomata to close, reducing water loss in winter buds.


Ethylene is associated with fruit ripening, flower wilting, and leaf fall. Ethylene is unusual because it is a volatile gas (C2H4). Hundreds of years ago, when gas street lamps were installed in city streets, trees that grew close to lamp posts developed twisted, thickened trunks and shed their leaves earlier than expected. These effects were caused by ethylene volatilizing from the lamps.

Aging tissues (especially senescing leaves) and nodes of stems produce ethylene. The best-known effect of the hormone, however, is the promotion of fruit ripening. Ethylene stimulates the conversion of starch and acids to sugars. Some people store unripe fruit, such as avocadoes, in a sealed paper bag to accelerate ripening; the gas released by the first fruit to mature will speed up the maturation of the remaining fruit. Ethylene also triggers leaf and fruit abscission, flower fading and dropping, and promotes germination in some cereals and sprouting of bulbs and potatoes.

Ethylene is widely used in agriculture. Commercial fruit growers control the timing of fruit ripening with application of the gas. Horticulturalists inhibit leaf dropping in ornamental plants by removing ethylene from greenhouses using fans and ventilation.

Nontraditional Hormones

Recent research has discovered a number of compounds that also influence plant development. Their roles are less understood than the effects of the major hormones described so far.

Jasmonates play a major role in defense responses to herbivory. Their levels increase when a plant is wounded by a predator, resulting in an increase in toxic secondary metabolites. They contribute to the production of volatile compounds that attract natural enemies of predators. For example, chewing of tomato plants by caterpillars leads to an increase in jasmonic acid levels, which in turn triggers the release of volatile compounds that attract predators of the pest.

Oligosaccharins also play a role in plant defense against bacterial and fungal infections. They act locally at the site of injury, and can also be transported to other tissues. Strigolactones promote seed germination in some species and inhibit lateral apical development in the absence of auxins. Strigolactones also play a role in the establishment of mycorrhizae, a mutualistic association of plant roots and fungi. Brassinosteroids are important to many developmental and physiological processes. Signals between these compounds and other hormones, notably auxin and GAs, amplifies their physiological effect. Apical dominance, seed germination, gravitropism, and resistance to freezing are all positively influenced by hormones. Root growth and fruit dropping are inhibited by steroids.

NBC AP Biology 10-11

Keratin is made up of proteins, these proteins make up our hair and nails.

This picture of a woody stem is from my Aunt's backyard. A woody stem is covered on the outside with bark.

This herbaceous stem is from my begonia, most flowers and vegetables have herbaceous stems. A herbaceous stem is not covered in bark, instead it is soft and usually green.

This is a picture of my brother's tongue, amylase can be found in saliva. Amylase is an enzyme that helps convert sugar into starch.

Deciduous leaves are leaves that fall off during a particular season, this leaf that I found while taking a walk through my grandmom's backyard is an example of a deciduous leaf.

These roses outside of my Aunt's house have thorns on it's stem. A thorn is a sharp point attached to the stem of a plant.

The flowers on my begonia plant contain pollen. Pollen is the fertilizing element of a plant, usually consists of a yellow powdery substance.

These flowers are from my mom's garden, they are considered angiosperms because an angiosperm is a plant that flowers has seeds contained in it's ovaries.

These flowers are all examples of autotrophs, autotrophs create their own food using the process of photosynthesis.

The Role of Soil pH in Plant Nutrition and Soil Remediation

In the natural environment, soil pH has an enormous influence on soil biogeochemical processes. Soil pH is, therefore, described as the “master soil variable” that influences myriads of soil biological, chemical, and physical properties and processes that affect plant growth and biomass yield. This paper discusses how soil pH affects processes that are interlinked with the biological, geological, and chemical aspects of the soil environment as well as how these processes, through anthropogenic interventions, induce changes in soil pH. Unlike traditional discussions on the various causes of soil pH, particularly soil acidification, this paper focuses on relationships and effects as far as soil biogeochemistry is concerned. Firstly, the effects of soil pH on substance availability, mobility, and soil biological processes are discussed followed by the biogenic regulation of soil pH. It is concluded that soil pH can broadly be applied in two broad areas, i.e., nutrient cycling and plant nutrition and soil remediation (bioremediation and physicochemical remediation).

1. Introduction

To many, soil pH is only essential for the chemistry and fertility of soils. However, the recognition of soil functions beyond plant nutrient supply and the role soil as a medium of plant growth required the study of the soil and its properties in light of broader ecosystem functions through a multidisciplinary approach. This allows scientists to view processes from landscape to regional and global levels. One process that denotes the multidisciplinary approach to soil science is soil biogeochemistry, which studies biogeochemical processes. The ecosystem functions of soil, to some extent, have a strong relationship with soil biogeochemical processes, which are linkages between biological, chemical, and geological processes [1]. The soil is the critical element of life support systems because it delivers several ecosystem goods and services such as carbon storage, water regulation, soil fertility, and food production, which have effects on human well-being [2–4]. These ecosystem goods and services are broadly categorized as supporting, provisioning, regulating, and cultural services [5]. According to the Millennium Ecosystem Assessment [5], the provisioning and regulating functions are said to have the greatest impact on the components of human well-being in terms of safety, the basic material for a good life, health, and good social relations.

In the natural environment, the pH of the soil has an enormous influence on soil biogeochemical processes. Soil pH is, therefore, described as the “master soil variable” that influences myriads of soil biological, chemical, and physical properties and processes that affect plant growth and biomass yield [6, 7]. Soil pH is compared to the temperature of a patient during medical diagnoses because it readily gives a hint of the soil condition and the expected direction of many soil processes (lecture statement, Emeritus Prof. Eric Van Ranst, Ghent University). For instance, soil pH is controlled by the leaching of basic cations such as Ca, Mg, K, and Na far beyond their release from weathered minerals, leaving H + and Al 3+ ions to dominant exchangeable cations the dissolution of CO2 in soil water producing carbonic acid, which dissociates and releases H + ions humic residues from the humification of soil organic matter, which produces high-density carboxyl and phenolic groups that dissociate to release H + ions nitrification of

produces H + ions removal of N in plant and animal products and inputs from acid rain and N uptake by plants [8]. On the other hand, pH controls the biology of the soil as well as biological processes. Consequently, there is a bidirectional relationship between soil pH and biogeochemical processes in terrestrial ecosystems, particularly in the soil. In this sense, the soil pH influences many biogeochemical processes, whereas some biogeochemical processes, in turn, influence soil pH, to some extent, as summarised in Figure 1.

For many decades, intensive research has revealed that soil pH influences many biogeochemical processes. Recent advances in research have made intriguing revelations about the important role of soil pH in many soil processes. This important soil property controls the interaction of xenobiotics within the three phases of soil as well as their fate, translocation, and transformation. Soil pH, therefore, determines the fate of substances in the soil environment. This has implications for nutrient recycling and availability for crop production, distribution of harmful substances in the environment, and their removal or translocation. This functional role of soil pH in soil biogeochemistry has been exploited for the remediation of contaminated soils and the control of pollutant translocation and transformation in the environment. Unfortunately, in many studies, soil pH is often measured casually as a norm without careful consideration for its role in soil. This paper seeks to explore the importance of pH as an indicator of soil biogeochemical processes in environmental research by discussing the biogeochemical processes that are influenced by soil pH, the biogeochemical processes that also control soil pH, and the relevance of the relationship for future research, planning, and development.

2. Biogeochemical Processes Influenced by Soil pH

2.1. Substance Translocation

Simultaneously, in accordance with biochemical changes, physicochemical processes, including dissolution, precipitation, adsorption, dilution, volatilization, and others, influence leachate quality [9].

2.1.1. Trace Element Mobility

Soil pH controls the solubility, mobility, and bioavailability of trace elements, which determine their translocation in plants [10]. This is largely dependent on the partition of the elements between solid and liquid soil phases through precipitation-dissolution reactions [10, 11] as a result of pH-dependent charges in mineral and organic soil fractions. For instance, negative charges dominate in high pH whereas positive charges prevail in low pH values [12]. Additionally, the quantity of dissolved organic carbon, which also influences the availability of trace elements, is controlled by soil pH. At low pH, trace elements are usually soluble due to high desorption and low adsorption. At intermediate pH, the trend of trace element adsorption increases from almost no adsorption to almost complete adsorption within a narrow pH range called the pH-adsorption edge [13]. From this point onwards, the elements are completely adsorbed [13]. For instance, Bradl [13] found that at pH 5.3, the adsorption of Cd, Cu, and Zn onto a sediment composite consisting of Al-, Fe-, and Si-oxides was 60%, 62%, and 53%, respectively. In contrast, he found that 50% of Cd and Zn sorbed onto humic acids between pH 4.8–4.9 [13]. The fate of readily available trace elements depends on both the properties of their ionic species formed in the soil solution and that of the chemical system of soil apart from soil pH itself [14]. Research has established that with increasing soil pH, the solubility of most trace elements will decrease, leading to low concentrations in soil solution [14]. Any increase or decrease in soil pH produces distinct effects on metal solubility. This may probably depend on the ionic species of the metals and the direction of pH change. Rengel [15] observed that the solubility of divalent metals decreases a hundred-fold while trivalent ones experience a decrease of up to a thousand-fold. In contrast, Förster [10] found that a decrease in soil pH by one unit resulted in a ten-fold increase in metal solubility. In an experiment, he observed that at pH 7, only about 1 mg Zn·L −1 of the 1200 mg·kg −1 total Zn content was present in soil solution. At pH 6, the concentration reached 100 mg Zn·L −1 while at pH 5, 40 mg Zn·L −1 was present. Aside from adsorption, trace element concentrations at high soil pH may also be caused by precipitation with carbonates, chlorides, hydroxides, phosphate, and sulphates [11, 16]. Apatite and lime applied to soils produced the highest effect on pH and simultaneously decreased the concentrations of available, leachable, and bioaccessible Cu and Cd [16].

2.1.2. Mobility of Soil Organic Fractions

Soil organic matter exists in different fractions ranging from simple molecules such as amino acids, monomeric sugars, etc. to polymeric molecules such as cellulose, protein, lignin, etc. These occur together with undecomposed and partly decomposed plant and microbial residues [17]. The solubility and mobility of the fractions differ during and after decomposition and could lead to the leaching of dissolved organic carbon and nitrogen in some soils. Dissolved organic carbon is defined as the size of organic carbon that passes through a 0.45 mm diameter filter [18]. Soil pH increases the solubility of soil organic matter by increasing the dissociation of acid functional groups [19] and reduces the bonds between the organic constituents and clays [20]. Thus, the content of dissolved organic matter increases with soil pH and consequently mineralizable C and N [20]. This explains the strong effects of alkaline soil pH conditions on the leaching of dissolved organic carbon and dissolved organic nitrogen observed in many soils containing substantial amounts of organic matter [19, 21]. The same observation has been made on the dissolved organic carbon concentration in peatland soils [22]. The pH-dependence of dissolved organic carbon concentration gets more pronounced beyond pH 6 [23].

Within the pH condition in a specific soil system, the solubility of organic matter is strongly influenced by the type of base and is particularly greater in the presence of monovalent cations than with multivalent ones [23]. According to Andersson and Nilsson [24] and Andersson et al. [19], soil pH controls the solubility of organic matter in two major ways: (i) its influence on the charge density of the humic compounds, and (ii) either the stimulation or repression of microbial activity. The former is found to be more pronounced than the latter [19].

2.2. Soil Biological Processes
2.2.1. Microbial Ecophysiological Indicators

Ecophysiology is an interlinkage between cell-physiological functioning under the influence of environmental factors [25]. It is estimated using the metabolic quotient (qCO2) as an index [25] to show the efficiency of organic substrate utilization by soil microbes in specific conditions [26]. A decrease in microbial community respiration makes C available for more biomass production, which yields higher biomass per unit [27]. The metabolic quotient is, therefore, described as a cell-physiological entity that reflects changes in environmental conditions [25]. This implies that any change in environmental conditions towards the adverse state will be indicated by the index [25]. This is controlled by soil pH [28]. Soil pH as a driving force for microbial ecophysical indices stems from its influence on the microbial community together with the maintenance demands of the community [28] and was among the predictors of the metabolic quotient [29, 30]. The metabolic quotient was found to be two-and-a-half times higher in low pH soils compared to neutral pH soils [28]. This has been associated with the divergence of the internal cell pH (usually kept around 6.0) from the surrounding pH conditions, which increases the maintenance requirements and reduces total microbial biomass produced [25].

It is observed from the literature that soil pH conditions required for microbial activity range from 5.5–8.8 [26, 31, 32]. Thus, soil respiration often increases with soil pH to an optimum level [26]. This also correlates with microbial biomass C and N contents, which are often higher above pH 7 [26]. In low pH conditions, fungal respiration is usually higher than bacterial respiration and the vice versa [25] because fungi are more adapted to acidic soil conditions than bacteria.

2.2.2. Soil Enzyme Activities

Extracellular enzymes are produced by soil microorganisms for the biogeochemical cycling of nutrients [33]. Soil pH is essential for the proper functioning of enzyme activity in the soil [34, 35], and may indirectly regulate enzymes through its effect on the microbes that produce them [36]. However, there are myriad of enzymes in biological systems which assist in the transformation of various substances. Besides, enzymes are of different origins and with differing degrees of stabilization on solid surfaces. Thus, the pH at which they reach their optimum activity (pH optima) is likely to differ [33]. It is striking that enzymes that act on the same substrates could vary considerably in their pH optima. This is evident in phosphorus enzymes, which have both acid and alkaline windows of functioning in the range of pH 3–5.5 and pH 8.5–11.5 [33]. In a study on the optimum pH for specific enzyme activity in soils from seven moist tropical forests in Central Panama, Turner [33] classified enzymes into three groups depending on their pH optima as found in the soils. These were: (a) enzymes with acidic optima that appeared consistent among soils, (b) enzymes with acidic pH optima that varied among the soils, and (c) enzymes with optima in both acid and alkaline soil pH. Stursova and Walker [37] found that organophosphorus hydrolase has optimal activity at higher pH. For instance, glycosidases have an optimal pH range between 4 and 6 compared to proteolytic and oxidative enzymes whose optima was between 7 and 9 [35, 36, 38]. Shifts in microbial community composition could potentially influence enzyme production if different microbial groups require lower nutrient concentrations to construct biomass, or have enzymes which differ in affinity for nutrients [39].

2.2.3. Biodegradation

Soil microorganisms are described as ecosystem engineers involved in the transformation of substances in the soil. One of such transformations is biodegradation, a process through which microbes remediate contaminated soils by transforming toxic substances and xenobiotics into least or more toxic forms. Biodegradation is the chemical dissolution of organic and inorganic pollutants by microorganisms or biological agents [34, 40]. Like many soil biological processes, soil pH influences biodegradation through its effect on microbial activity, microbial community and diversity, enzymes that aid in the degradation processes as well as the properties of the substances to be degraded. Soil pH was the most important soil property in the degradation of atrazine [41]. Generally, alkaline or slightly acid soil pH enhances biodegradation, while acidic environments pose limitations to biodegradation [34, 37, 42]. Usually, pH values between 6.5 and 8.0 are considered optimum for oil degradation [43]. Within this range, specific enzymes function within a particular pH spectrum. For instance, the pesticide fenamiphos degraded in two United Kingdom soils with high pH (>7.7) and two Australian soils with pH ranging from pH 6.7 to 6.8. The biodegradation process rather slowed down in three acidic United Kingdom soils (pH 4.7 to 6.7) in 90 days after inoculation [42]. Xu [44] found some strains of bacteria isolated from petroleum-contaminated soil in northern China being able to degrade over 70% of petroleum at pH 7 and 9. In a degradation experiment involving polycyclic aromatic hydrocarbons (PAHs), half of the PAHs degraded at pH 7.5 within seven days representing the highest amount degraded [34]. This was associated with the highest bacterial populations [34]. Furthermore, Houot et al. [41] found increased degradation of atrazine in French and Canadian soils, which occurred with increased soil pH. They observed maximum soil respiration in atrazine-contaminated soils at soil pH values higher than 6.5 compared to those with soil pH value less than 6.0 where metabolites rather accumulated.

2.2.4. Mineralization of Organic Matter

Organic matter mineralization is often expressed as carbon (C), nitrogen (N), phosphorus (P), and sulphur (S) mineralization through microbial action. Soil pH controls mineralization in soils because of its direct effect on the microbial population and their activities. This also has implications for the functions of extracellular enzymes that aid in the microbial transformation of organic substrates. Additionally, at a higher soil pH, the mineralizable fractions of C and N increase because the bond between organic constituents and clays is broken [20]. In a study on the mineralization of C and N in different upland soils of the subtropics treated with different organic materials, Khalil et al. [45] found that soil pH and C/N ratio were responsible for 61% of the decomposition rate, with corresponding increases in CO2 effluxes, net N mineralization, and net nitrification in alkaline than in acid soils. Similar results had earlier on been obtained by Curtin et al. [20].

2.2.5. Nitrification and Denitrification

Nitrification and denitrification are important nitrogen transformation processes of environmental concern. Like many of the biogeochemical processes, the processes, to a large extent, are controlled by soil pH. Nitrification involves the microbial conversion of ammonium to nitrate. It generally increases with increasing soil pH but reaches an optimum pH [45–47]. In a four-year study, Kyveryga et al. [47] observed that soil pH range of 6 to 8 strongly influenced the nitrification rates of fertilizer N. Generally, the nitrification rate decreases at lower soil pH values. In some soils, nitrification and nitrification potential substantially decrease or are negligible below a pH value of 4.2. However, nitrification may still occur even below pH 4.14, suggesting that ammonia-oxidizing and nitrifier communities might remain active at low soil pH [48].

Denitrification is the microbiological process in which oxidized N species such as nitrate ( ) and nitrite (

) are reduced to gaseous nitric oxide (NO), nitrous oxide (N2O), and molecular nitrogen (N2) under limited oxygen conditions [49]. Soil pH affects denitrification rate, potential denitrification, and the ratio between the two main products of denitrification (N2O and N2). The ratio has an inverse relation with soil pH [49]. At pH values below 7, N2O was the main denitrification product whereas N2 prevailed at pH values above 8 [49]. Sun et al. [50] discovered that soil pH was the best predictor of denitrification rate where the ratio of N2/N2O increased exponentially with an increase in soil pH. This is because low pH prevents the assembly of functional nitrous oxide reductase, the enzyme reducing N2O to N2 in denitrification [15, 20] and this mostly depends on the natural soil pH [49]. However, the soil pH at which the highest activity of nitrous oxide reductase occurred was around pH 7.3. This occurred in soils amended with potassium hydroxide (KOH) [51]. This suggests the inhibition of denitrification at high pH, particularly up to pH 9 [50]. Furthermore, maximum denitrification of between 68% and 85% occurred in a sandy and a loamy soil with pH 5.2 and 5.9, respectively [52]. The optimum pH for long-term potential denitrification was between 6.6 and 8.3. Additionally, the short-term denitrifying enzyme activity depended on the natural soil pH [49]. The effect of soil pH on denitrification is partly due to pH controls over the denitrifying microbial populations. The population size of the resident nitrate-reducing bacterial population increased dramatically when the pH of the acid soil was increased [53].

2.2.6. Ammonia Volatilization

The volatilization of ammonia is a phenomenon that occurs naturally in all soils [54] and has been attributed to the dissociation of to NH3 and H + shown in equation (1) [55]

The dissociation approaches equilibrium through the acidification of the medium. The rate of acidification depends on the initial and final concentrations of ammonium as well as on the buffering capacity of the medium [55]. When solution pH increases above 7, H + is consumed in the reaction. Thus, the dissociation of ammonium to ammonia in equation (1) will favour ammonia volatilization. In neutral and acid soils, containing fertilizers are less subject to NH3 loss than urea and urea-containing fertilizers [54]. However, the degree will also depend on the specific fertilizer and its effect on soil pH. In a study involving ammonia volatilization from an alkaline salt-affected soil cultivated with rice, Li et al. [56] found that ammonia volatilization increased rapidly with pH and peaked at pH 8.6. Ammonia volatilization is strongly correlated with pH and calcium carbonate, which suggested that the soil pH was a key factor in ammonia volatilization because calcium carbonate increases soil pH which in turn controls the concentration of ammonia and ammonium in soil solution [57].

3. Biogenic Regulation of Soil pH

Soil biological processes from living organisms and biochemical transformations of the remains of dead organisms induce changes in soil pH. This can either occur through the direct effect of biochemical processes occurring in the living organisms in the soil system, mostly through rhizosphere processes or through the direct and indirect effects of applied organic residues, whether in unburnt, burnt, or charred forms as well as their decomposition.

3.1. Rhizosphere Processes

The rhizosphere is the volume of soil in the neighbourhood of roots that is influenced by root and microbial activities [58–60] Hiltner 1904 cited by [60]. It is a longitudinal and radial gradient [61], ranging from 0 to 2.0 mm from the root mat [62, 63]. In this small soil volume, roots take up water and nutrients, undergo root elongation and expansion, release exudates, respire, and thus have higher microbial activity [59, 63]. Through some of these biological processes, plant roots have the ability to induce pH changes in the rhizosphere either by releasing protons (H + ) or hydroxyl ions (OH − ) to maintain ion balance [58, 64], depending on the nutritional status of the plants [65]. Therefore, rhizosphere pH could increase or decrease depending on the prevailing process and types of ions released.

Plant root-induced soil pH change in the rhizosphere is controlled by specific processes and factors such as (i) ion uptake coupled with the release of inorganic ions that maintain electroneutrality, (ii) the excretion of organic acid anions, (iii) root exudation and respiration, (iv) redox-coupled processes, (v) microbial production of acids after the assimilation of released root carbon, and (vi) plant genotype [58, 59]. Surprisingly, roots have a greater tendency to raise the pH of the rhizosphere rather than lower it [65, 66]. The dominant mechanism responsible for pH changes in the rhizosphere is plant uptake of nutrients in the form of cations and anions [58, 59, 65], primarily due to plant uptake of the two major forms of inorganic nitrogen ( and ), which is usually taken up in large quantities [59]. Nitrogen is taken up by plants in three major forms: ammonium ( ), nitrate ( ), and molecular nitrogen (N2) [59], although amino acids can also be taken up [58]. The uptake of each of the three forms of nitrogen accompanies the release of corresponding ions to maintain electroneutrality in the rhizosphere. When nitrate dominates in soil or when its uptake dominates, plants must release bicarbonate (

) or hydroxyl ions (OH − ) to maintain electrical neutrality across the soil-root interface resulting in rhizosphere pH increase [58, 59, 64]. In contrast, protons are released by plants in response to uptake, causing a decrease in rhizosphere pH [58, 62]. It has been revealed that 15, 6, and 0%, respectively, of the N from the total N present in the soil is required to decrease rhizosphere pH decrease by 1.2 units, maintain it, or increase it by 0.4 pH unit [62].

The extent of effects of the processes and factors controlling rhizosphere pH change depends on plant species and growth stages [65]. For instance, in a study on rhizosphere acidification interactions, Faget et al. [67] found differences between rhizosphere acidification in maize (Zea mays L.) and beans (Phaseolus vulgaris L.). Maize initially acidified the rhizosphere and gradually alkalized it over time while beans showed opposite effects. They found an interaction effect of the two plant species on the rhizosphere pH change whereby the degree of acidification or alkalization was weaker when roots grew within the same neighbourhood than when the roots were not growing near each other. However, the rhizosphere pH changes with time as a result of variable uptake of nitrogen ions, plant species, and their growth stages of the plants [67]. This was revealed in an experiment on apple trees (Malus pumila Miller), buckwheat (Fagopyrum esculentum Moench), corn (Zea mays L.), cowpeas (Vigna unguiculata (L) Walp.), kaffir lime (Citrus hystrix DC.), lettuce (Lactuca sativa L.), pine trees (Pinus sp. L.), and wheat (Triticum aestivum L.), where Metzger [66] found maximum concentrations of in the rhizosphere during the blooming and fruiting stages (Figure 2), which was 10–29% higher compared to the bulk soil. The concentrations of in the rhizosphere of the plants was in the order, lettuce = buckwheat > pine > apple > kaffir > cowpeas > corn > wheat. These values were much lower than those obtained in the rhizosphere of soybean (Glycine max (L.) Merr.) [64]. Furthermore, Turpault et al. [59] found that 93% of NO3-N was taken up by a Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) stands during April–September compared to 83% uptake during the October-March period. This likely increased rhizosphere pH and implies that during periods of low nitrate uptake, soil pH may decrease due to buffering or due to a response to the uptake of .

3.2. Raw and Combusted Organic Materials

When unburnt organic materials or raw plant residues are applied to the soil, the pH increases to a peak and decrease afterwards. For instance, Forján et al. [68] found initial increases in soil pH when they applied a mixture of sludge from a bleach plant, urban solid waste and mine wastes, and a mixture of sludge from a purification plant, wood chips, and remnants from agri-food industries to the soil. Furthermore, the addition of young Kikuyu (Pennisetum clandestinum L.) shoots also increased soil pH by up to one pH unit [69]. The major causes of this pH change is due to the (i) release of excess residue alkalinity attributed to the basic cations such as Ca, K, Mg, and Na [70] (ii) decarboxylation of organic anions that occurs during C mineralisation, causing the consumption of protons and release of OH − [71, 72] (iii) ammonification of the residue N (iv) nitrification of mineralised residue N and (v) association/dissociation of organic compounds [70]. These processes are determined by the quantity applied and the prevailing soil and environmental conditions [70]. According to Xu et al. [70] direct chemical reactions and oxidation of the organic anions during residue decomposition are the main mechanisms involved in organic anion-induced soil pH increase. Additionally, organic anions and other negatively charged chemical functional groups present in organic matter can undergo association reactions with H + ions [71, 73].

The increase in soil pH after residue application also depends on the type of residue (either from monocots or dicots), which is related to the amount of alkalinity present, residue quality (C/N ratio), the rate of residue application and decomposition, the initial pH, and buffering capacity of the soil [70, 71]. Different residues have different chemical and biochemical compositions, which determine the processes responsible for soil pH change. This was detected in an incubation experiment involving three soils and five different residue types where soil pH increased according to lucerne > chickpea > medic > high-N wheat > low-N wheat [70]. Furthermore, in a 59-day laboratory incubation [71] and field experiments [74], it was found that the magnitude of soil pH increase following residue amendment was in the order chickpea > canola > wheat [71, 74]. They observed that 40–62% of soluble alkalinity in canola and chickpea residues were responsible for the pH increases. It is obvious from these, and many other studies [69], that the residues of dicots, particularly legumes, have high alkalinity and produce larger effects on soil pH change than monocots. The pH increase after residue addition often reaches a peak and declines thereafter as a result of nitrification. Residues with low carbon-nitrogen (C/N) ratios are often associated with sharp pH decline after a certain period and the extent varies with soil type and soil buffering capacity [70, 71, 74], whereas those with high C/N ratios produce smaller pH increase, or none at all [70].

The initial pH and buffering capacity of soils receiving plant residues have a profound role in the extent of pH change after application. For instance, three soil types of different initial soil pH, namely, Wodjil sandy loam with pH(CaCl2) 3.87, Bodallin sandy loam soil with pH 4.54, and Lancelin sandy soil with pH 5.06, were incubated with residues of chickpea, lucerne, medic, high-N wheat, and low-N wheat. Thereafter, the pH increased by about 3.3 units with lucerne in the Wodjil soil (3.87), 1.6 with chickpea, 1.5 with medic, and 0.5 with high-N wheat, and no increase with low-N wheat. The pH increased and peaked at 42 days of incubation for Bodallin and Wodjil sandy loams followed by a decline whereas, in the Lancelin sandy soil, the pH peaked at day 14 before declining [70]. In another incubation study [71], a Podzol with an initial pH of 4.5 and a Cambisol with an initial pH of 6.2 were amended with residues of canola, chickpea, and wheat. For all the residues, the pH increase in the moderately acidic Cambisol was up to sixfold larger than in the more acidic Podzol. This peaked at 14 days after application and declined afterwards. However, in a field study on the same soils [74], the application of chickpea residue increased soil pH by 1.3 units in both soils and reached a maximum at 3 months, whereas canola residue increased pH by 0.82 and 1.02 units in the Podzol and Cambisol, respectively, and reached a maximum pH at 9 months.

Similar to unburnt organic materials, burnt or charred plant residues contain a larger amount of alkalinity due to the volatilization of organic constituents under thermal conditions leading to the concentration of alkaline constituents. The actual alkalinity depends on the type of biomass involved, their origin, and burnt temperature. Burnt and charred forms of organic materials include biochar and ash. Biochar is a solid consistent product pyrolysis, while ash is a loose powdery material obtained by combustion. The pH of biochar produced at 500–600°C was 6.4–9.3 and showed a strong relationship with the total alkalinity (i.e., organic and inorganic alkalinities) [75]. The inorganic alkalinity increased with increasing pyrolysis temperature and with increasing divalent cation contents [75] because the organic constituents volatilize during pyrolysis. This alkalinity of biochar neutralizes acidity and increases soil pH depending on the amount of alkalinity and soil buffering capacity [76]. Biomass ash contains substantial alkalinity, which is often expressed as percent calcium carbonate equivalence (% CCE). It ranges from 17–95% [77, 78]. Similarly to biochar, the combustion temperature has effects on the alkalinity of biomass aside the biomass type and source. Recently, Neina et al. (submitted) found that ash from charcoal had higher CCE, pH, and K contents than firewood ash. Depending on the alkalify and buffering capacity of the soil receiving the biomass ash, soil pH increase can be high or low. For instance, in two Ghanaian Acrisols, biomass ash applied at 2.5 g·kg −1 soil increased soil pH by about 1 unit after 12 weeks of laboratory incubation [79]. This pH change is mostly short-lived due to other biogeochemical processes.

4. Conclusions

The content of this paper highlights the role of soil pH as a master soil variable that has a bidirectional relationship with soil biogeochemical processes. Although not all biogeochemical processes were discussed in this paper, those discussed have substantial influences on soil health, nutrient availability, pollution, and potential hazards of pollutants as well as their fate in the food chain. The mobility of unwholesome substances through the hydrological cycle cannot be overlooked here because of the intimate relationship between soil and water. Thus, an understanding of this can form a basis and a guide to decisions and choices of soil management, remediation, rehabilitation, and the maintenance of soil quality. The observed soil pH-biogeochemistry relationships provide insight for future applications for increased yields for specific crops through nutrient recycling and availability, which enhances crop growth. The transient rhizosphere soil pH could also be used to enhance the availability of certain nutrients in certain soil conditions [80]. More importantly, soil pH could be useful for soil pollution control through the distribution and removal of harmful substances from systems. For instance, the mineralization and degradation processes such as those of C and N mineralisation and the degradation of pesticide occur between pH 6.5 and 8, while the maximum degradation of petroleum and PAHs occur between pH 7 and 9. These, as well as pH maxima for various microbial enzymes, could be utilized in many soil remediation strategies, particularly in bioremediation. Ultimately, soil pH can broadly be applied in two broad areas, i.e., nutrient cycling and plant nutrition and soil remediation (bioremediation and physicochemical remediation).

Conflicts of Interest

The author declares that there are no conflicts of interest regarding the publication of this article.


  1. R. A. Dahlgren, “Biogeochemical processes in soils and ecosystems: from landscape to molecular scale,” Journal of Geochemical Exploration, vol. 88, no. 1–3, pp. 186–189, 2006. View at: Publisher Site | Google Scholar
  2. FAO, Revised World Soil Charter, Food and Agriculture Organization, Rome, Italy, 2015.
  3. FAO and ITPS, Status of the World’s Soil Resources (SWSR)—Main Report, Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy, 2015.
  4. A. Jones, H. Breuning-Madsen, M. Brossard et al., Soil Atlas of Africa, European Commission, Publications Office of the European Union, Brussels, Belgium, 2013.
  5. Millennium Ecosystem Assessment, Ecosystems and Human Well-Being: Desertification Synthesis, World Resources Institute, Washington, DC, USA, 2005.
  6. N. C. Brady and R. R. Weil, The Nature and Property of Soils, Prentice Hall, Upper Saddle Hall, NJ, USA, 1999.
  7. B. Minasny, S. Y. Hong, A. E. Hartemink, Y. H. Kim, and S. S. Kang, “Soil pH increase under paddy in South Korea between 2000 and 2012,” Agriculture, Ecosystems & Environment, vol. 221, pp. 205–213, 2016. View at: Publisher Site | Google Scholar
  8. R. E. White, Principles and Practice of Soil Science: The Soil as a Natural Resource, Blackwell Publishing, Oxford, UK, 2006.
  9. D. Kulikowska and E. Klimiuk, “The effect of landfill age on municipal leachate composition,” Bioresource Technology, vol. 99, no. 13, pp. 5981–5985, 2008. View at: Publisher Site | Google Scholar
  10. U. Förstner, “Land contamination by metals—global scope and magnitude of problem,” in Metal Speciation and Contamination of Soil, H. E. Allen, C. P. Huang, G. W. Bailey, and A. R. Bowers, Eds., pp. 1–33, CRC Press, Inc., Boca Raton, FL, USA, 1995. View at: Google Scholar
  11. J. S. Rieuwerts, I. Thornton, M. E. Farago, and M. R. Ashmore, “Factors influencing metal bioavailability in soils: preliminary investigations for the development of a critical loads approach for metals,” Chemical Speciation & Bioavailability, vol. 10, no. 2, pp. 61–75, 1998. View at: Publisher Site | Google Scholar
  12. G. P. Gillman, “An analytical tool for understanding the properties and behaviour of variable charge soils,” Soil Research, vol. 45, no. 2, pp. 83–90, 2007. View at: Publisher Site | Google Scholar
  13. H. B. Bradl, “Adsorption of heavy metal ions on soils and soils constituents,” Journal of Colloid and Interface Science, vol. 277, no. 1, pp. 1–18, 2004. View at: Publisher Site | Google Scholar
  14. A. Kabata-Pendias, Trace Elements in Soils and Plants, CRC Press, Boca Raton, FL, USA, 2011.
  15. Z. Rengel, “Genotypic differences in micronutrient use efficiency in crops,” Communications in Soil Science and Plant Analysis, vol. 32, no. 7-8, pp. 1163–1186, 2001. View at: Publisher Site | Google Scholar
  16. H. Cui, Y. Fan, G. Fang, H. Zhang, B. Su, and J. Zhou, “Leachability, availability and bioaccessibility of Cu and Cd in a contaminated soil treated with apatite, lime and charcoal: a five-year field experiment,” Ecotoxicology and Environmental Safety, vol. 134, pp. 148–155, 2016. View at: Publisher Site | Google Scholar
  17. J. A. Baldock, “Composition and cycling of organic carbon in soil,” in Nutrient Cycling in Terrestrial Ecosystems, P. Marschner and Z. Rengel, Eds., pp. 1–35, Springer-Verlag, Berlin, Germany, 2007. View at: Google Scholar
  18. F. Vogel, J. Harf, A. Hug, and P. R. von Rohr, “The mean oxidation number of carbon (MOC)-a useful concept for describing oxidation processes,” Water Research, vol. 34, no. 10, pp. 2689–2702, 2000. View at: Publisher Site | Google Scholar
  19. S. Andersson, S. I. Nilsson, and P. Saetre, “Leaching of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in mor humus as affected by temperature and pH,” Soil Biology and Biochemistry, vol. 32, no. 1, pp. 1–10, 2000. View at: Publisher Site | Google Scholar
  20. D. Curtin, C. A. Campbell, and A. Jalil, “Effects of acidity on mineralization: pH-dependence of organic matter mineralization in weakly acidic soils,” Soil Biology and Biochemistry, vol. 30, no. 1, pp. 57–64, 1998. View at: Publisher Site | Google Scholar
  21. C. D. Evans, T. G. Jones, A. Burden et al., “Acidity controls on dissolved organic carbon mobility in organic soils,” Global Change Biology, vol. 18, no. 11, pp. 3317–3331, 2012. View at: Publisher Site | Google Scholar
  22. T. Oulehle, S. Shi, W. Zhang, Y. Wu, M. Yang, and P. Wang, “Environmental factors and dissolved organic carbon content in a Jinchuan peatland,” Acta Ecologica Sinica, vol. 36, no. 3, pp. 160–165, 2016. View at: Publisher Site | Google Scholar
  23. D. Curtin, M. E. Peterson, and C. R. Anderson, “pH-dependence of organic matter solubility: base type effects on dissolved organic C, N, P, and S in soils with contrasting mineralogy,” Geoderma, vol. 271, pp. 161–172, 2016. View at: Publisher Site | Google Scholar
  24. S. Andersson and S. I. Nilsson, “Influence of pH and temperature on microbial activity, substrate availability of soil-solution bacteria and leaching of dissolved organic carbon in a mor humus,” Soil Biology and Biochemistry, vol. 33, no. 9, pp. 1181–1191, 2001. View at: Publisher Site | Google Scholar
  25. T.-H. Anderson, “Microbial eco-physiological indicators to asses soil quality,” Agriculture, Ecosystems & Environment, vol. 98, no. 1–3, pp. 285–293, 2003. View at: Publisher Site | Google Scholar
  26. J. C. A. Pietri and P. C. Brookes, “Nitrogen mineralisation along a pH gradient of a silty loam UK soil,” Soil Biology & Biochemistry, vol. 40, no. 3, pp. 797–802, 2008. View at: Publisher Site | Google Scholar
  27. T.-H. Anderson and K. H. Domsch, “Carbon link between microbial biomass and soil organic matter,” in Perspectives in Microbial Ecology, F. Megusar and M. Gantar, Eds., pp. 467–471, Mladinska Knjiga, Ljubljana, Slovenia, 1986. View at: Google Scholar
  28. E. V. Blagodatskaya and T.-H. Anderson, “Interactive effects of pH and substrate quality on the fungal-to-bacterial ratio and qCO2 of microbial communities in forest soils,” Soil Biology and Biochemistry, vol. 30, no. 10-11, pp. 1269–1274, 1998. View at: Publisher Site | Google Scholar
  29. D. Neina, A. Buerkert, and R. G. Joergensen, “Microbial response to the restoration of a Technosol amended with local organic materials,” Soil and Tillage Research, vol. 163, pp. 214–223, 2016. View at: Publisher Site | Google Scholar
  30. D. Neina, A. Buerkert, and R. G. Joergensen, “Effects of land use on microbial indices in tantalite mine soils, Western Rwanda,” Land Degradation & Development, vol. 28, no. 1, pp. 181–188, 2017. View at: Publisher Site | Google Scholar
  31. J. C. A. Pietri and P. C. Brookes, “Relationships between soil pH and microbial properties in a UK arable soil,” Soil Biology & Biochemistry, vol. 40, no. 7, pp. 1856–1861, 2008. View at: Publisher Site | Google Scholar
  32. N. Fierer and R. B. Jackson, “The diversity and biogeography of soil bacterial communities,” Proceedings of the National Academy of Sciences, vol. 103, no. 3, pp. 626–631, 2006. View at: Publisher Site | Google Scholar
  33. B. L. Turner, “Variation in pH optima of hydrolytic enzyme activities in tropical rain forest soils,” Applied and Environmental Microbiology, vol. 76, no. 19, pp. 6485–6493, 2010. View at: Publisher Site | Google Scholar
  34. R. M. Pawar, “The effect of soil pH on bioremediation of polycyclic aromatic hydrocarbons (PAHS),” Bioremediation & Biodegradation, vol. 6, no. 3, pp. 1–14, 2015. View at: Google Scholar
  35. R. L. Sinsabaugh, C. L. Lauber, M. N. Weintraub et al., “Stoichiometry of soil enzyme activity at global scale,” Ecology Letters, vol. 11, no. 11, pp. 1252–1264, 2008. View at: Publisher Site | Google Scholar
  36. S. L. Keeler, R. L. Sinsabaugh, C. Crenshaw et al., “Pulse dynamics and microbial processes in aridland ecosystems,” Journal of Ecology, vol. 96, no. 3, pp. 413–420, 2008. View at: Publisher Site | Google Scholar
  37. B. K. Stursova and A. Walker, “Microbial degradation of organophosphorus compounds,” FEMS Microbiology Reviews, vol. 30, no. 3, pp. 428–471, 2006. View at: Publisher Site | Google Scholar
  38. R. L. Sinsabaugh, M. M. Carreiro, and S. Alvarez, “Enzyme and microbial dynamics during litter decomposition,” in Enzymes in the Environment: Activity, Ecology, and Applications, R. G. Burns and R. P. Dick, Eds., pp. 249–266, CRC Press, Inc., Oxford, UK, 2002. View at: Google Scholar
  39. V. J. Allison, L. M. Condron, D. A. Peltzer, S. J. Richardson, and B. L. Turner, “Changes in enzyme activities and soil microbial community composition along carbon and nutrient gradients at the Franz Josef chronosequence, New Zealand,” Soil Biology and Biochemistry, vol. 39, no. 7, pp. 1770–1781, 2007. View at: Publisher Site | Google Scholar
  40. S. Dass, M. Muneer, and K. R. Gopidas, “Photocatalytic degradation of wastewater pollutants. Titanium-dioxide-mediated oxidation of polynuclear aromatic hydrocarbons,” Journal of Photochemistry and Photobiology A: Chemistry, vol. 77, no. 1, pp. 83–88, 1994. View at: Publisher Site | Google Scholar
  41. S. Houot, E. Topp, A. Yassir, and G. Soulas, “Dependence of accelerated degradation of atrazine on soil pH in French and Canadian soils,” Soil Biology and Biochemistry, vol. 32, no. 5, pp. 615–625, 2000. View at: Publisher Site | Google Scholar
  42. B. K. Singh, A. Walker, J. A. W. Morgan, and D. J. Wright, “Role of soil pH in the development of enhanced biodegradation of Fenamiphos,” Applied and Environmental Microbiology, vol. 69, no. 12, pp. 7035–7043, 2003. View at: Publisher Site | Google Scholar
  43. M. Vidali, “Bioremediation. An overview,” Pure and Applied Chemistry, vol. 73, no. 7, pp. 1163–1172, 2001. View at: Publisher Site | Google Scholar
  44. J. Xu, “Bioremediation of crude oil contaminated soil by petroleum-degrading active bacteria,” in Introduction to Enhanced Oil Recovery (EOR) Processes and Bioremediation of Oil-Contaminated Sites, L. Romero-Zerón, Ed., pp. 207–244, InTech, Rijeka, Croatia, 2012. View at: Google Scholar
  45. M. I. Khalil, M. B. Hossain, and U. Schmidhalter, “Carbon and nitrogen mineralization in different upland soils of the subtropics treated with organic materials,” Soil Biology and Biochemistry, vol. 37, no. 8, pp. 1507–1518, 2005. View at: Publisher Site | Google Scholar
  46. M. Šimek and J. A. Cooper, “The influence of soil pH on denitrification: progress towards the understanding of this interaction over the last 50 years,” European Journal of Soil Science, vol. 53, no. 3, pp. 245–254, 2002. View at: Publisher Site | Google Scholar
  47. P. M. Kyveryga, A. M. Blackmer, J. W. Ellsworth, and R. Isla, “Soil pH effects on nitrification of fall-applied anhydrous ammonia,” Soil Science Society of America Journal, vol. 68, no. 2, pp. 545–551, 2004. View at: Publisher Site | Google Scholar
  48. B. J. Zebarth, T. A. Forge, C. Goyer, and L. D. Brin, “Effect of soil acidification on nitrification in soil,” Canadian Journal of Soil Science, vol. 95, no. 4, pp. 359–363, 2015. View at: Publisher Site | Google Scholar
  49. M. Šimek, L. Jíšová, and D. W. Hopkins, “What is the so-called optimum pH for denitrification in soil?” Soil Biology & Biochemistry, vol. 34, no. 9, pp. 1227–1234, 2002. View at: Publisher Site | Google Scholar
  50. P. Sun, Y. Zhuge, J. Zhang, and Z. Cai, “Soil pH was the main controlling factor of the denitrification rates and N2/N2O emission ratios in forest and grassland soils along the Northeast China Transect (NECT),” Soil Science and Plant Nutrition, vol. 58, no. 4, pp. 517–525, 2012. View at: Publisher Site | Google Scholar
  51. C. Anderson, M. Peterson, and D. Curtin, “Base cations, K + and Ca 2+ , have contrasting effects on soil carbon, nitrogen and denitrification dynamics as pH rises,” Soil Biology and Biochemistry, vol. 113, pp. 99–107, 2017. View at: Publisher Site | Google Scholar
  52. B. W. Hütsch, S. Zhang, K. Feng, F. Yan, and S. Schubert, “Effect of pH on denitrification losses from different arable soils,” in Plant Nutrition. Developments in Plant and Soil Sciences, W. J. Horst, Ed., pp. 962-963, Springer, Berlin, Germany, 2001. View at: Google Scholar
  53. N. Wolfgang and R. Conrad, “Influence of soil pH on the nitrate-reducing microbial populations and their potential to reduce nitrate to NO and N2O,” FEMS Microbiology Ecology, vol. 7, no. 1, pp. 49–57, 1990. View at: Publisher Site | Google Scholar
  54. S. L. Tisdale, W. L. Nelson, J. D. Beaton, and J. L. Havlin, Soil Fertility and Fertilizers, Macmillan Publishing Company, New York, NY, USA, 5th edition, 1985.
  55. Y. Avnimelech and M. Laher, “Ammonia volatilization from soils: equilibrium considerations,” Soil Science Society of American Journal, vol. 41, pp. 1080–1084, 1997. View at: Google Scholar
  56. Y. Li, L. Huang, H. Zhang, M. Wang, and Z. Liang, “Assessment of ammonia volatilization losses and nitrogen utilization during the rice growing season in alkaline salt-affected soils,” Sustainability, vol. 9, no. 1, pp. 132–139, 2017. View at: Publisher Site | Google Scholar
  57. D. Zhenghu and X. Honglang, “Effects of soil properties on ammonia volatilization,” Soil Science and Plant Nutrition, vol. 46, no. 4, pp. 845–852, 2000. View at: Publisher Site | Google Scholar
  58. P. Hinsinger, C. Plassard, C. Tang, and B. Jaillard, “Origins of root-mediated pH changes in the rhizosphere and their responses to environmental constraints: a review,” Plant and Soil, vol. 248, no. 1-2, pp. 43–59, 2003. View at: Publisher Site | Google Scholar
  59. M.-P. Turpault, G. R. Gobran, and P. Bonnaud, “Temporal variations of rhizosphere and bulk soil chemistry in a Douglas fir stand,” Geoderma, vol. 137, no. 3-4, pp. 490–496, 2007. View at: Publisher Site | Google Scholar
  60. P. J. Gregory, “A history of rhizosphere research—roots to a solution,” in Proceedings of the 19th World Congress of Soil, Soil Solutions for a Changing World, Brisbane, Australia, 1–6 August 2010. View at: Google Scholar
  61. N. C Uren, “Types, amounts, and possible functions of compounds released into the rhizosphere by soil-grown plants,” in The Rhizosphere: Biochemistry and Organic Substances at the Soil- Plant Interface, R. Pinton, Z. Varanini, and P. Nannipieri, Eds., pp. 19–40, Marcel Dekker Inc., New York, NY, USA, 2000. View at: Google Scholar
  62. T. S. Gahoonia and N. E. Nielsen, “The effects of root-induced pH changes on the depletion of inorganic and organic phosphorus in the rhizosphere,” Plant and Soil, vol. 143, no. 2, pp. 185–191, 1992. View at: Publisher Site | Google Scholar
  63. C. Bertin, X. Yang, and L. A. Weston, “The role of root exudates and allelochemicals in the rhizosphere,” Plant and Soil, vol. 256, no. 1, pp. 67–83, 2003. View at: Publisher Site | Google Scholar
  64. D. Riley and S. A. Barber, “Bicarbonate accumulation and pH changes at the soybean (Glycine max (L.) Merr.) root-soil Interface 1,” Soil Science Society of America Journal, vol. 33, no. 6, pp. 905–908, 1969. View at: Publisher Site | Google Scholar
  65. P. H. Nye, “Processes in the root Environment 1,” Journal of Soil Science, vol. 19, no. 2, pp. 205–215, 1968. View at: Publisher Site | Google Scholar
  66. W. H. Metzger, “The effect of growing plants on solubility of soil nutrients,” Soil Science, vol. 25, no. 4, pp. 273–280, 1928. View at: Publisher Site | Google Scholar
  67. M. Faget, S. Blossfeld, and P. Gillhaussen vonU. Schurr and V. M. Temperton, “Disentangling who is who during rhizosphere acidification in root interactions: combining fluorescence with optode techniques,” Frontiers in Plant Science, vol. 4, pp. 1–8, 2013. View at: Publisher Site | Google Scholar
  68. R. Forján, V. Asensio, A. Rodríguez-Vila, and E. F. Covelo, “Effect of amendments made of waste materials in the physical and chemical recovery of mine soil,” Journal of Geochemical Exploration, vol. 147, pp. 91–97, 2014. View at: Publisher Site | Google Scholar
  69. T. T. Nguyen and P. Marschner, “Soil respiration, microbial biomass and nutrient availability in soil after addition of residues with adjusted N and P concentrations,” Pedosphere, vol. 27, no. 1, pp. 76–85, 2017. View at: Publisher Site | Google Scholar
  70. J. M. Xu, C. Tang, and Z. L. Chen, “The role of plant residues in pH change of acid soils differing in initial pH,” Soil Biology and Biochemistry, vol. 38, no. 4, pp. 709–719, 2006. View at: Publisher Site | Google Scholar
  71. C. R. Butterly, B. Bhatta Kaudal, J. A. Baldock, and C. Tang, “Contribution of soluble and insoluble fractions of agricultural residues to short-term pH changes,” European Journal of Soil Science, vol. 62, no. 5, pp. 718–727, 2011. View at: Publisher Site | Google Scholar
  72. F. Rukshana, C. R. Butterly, J. A. Baldock, and C. Tang, “Model organic compounds differ in their effects on pH changes of two soils differing in initial pH,” Biology and Fertility of Soils, vol. 47, no. 1, pp. 51–62, 2011. View at: Publisher Site | Google Scholar
  73. C. Tang and Q. Yu, “Impact of chemical composition of legume residues and initial soil pH on pH change of a soil after residue incorporation,” Plant and Soil, vol. 215, no. 1, pp. 29–38, 1999. View at: Google Scholar
  74. C. R. Butterly, J. A. Baldock, and C. Tang, “The contribution of crop residues to changes in soil pH under field conditions,” Plant and Soil, vol. 366, no. 1-2, pp. 185–198, 2013. View at: Publisher Site | Google Scholar
  75. R. B. Fidel, D. A. Laird, M. L. Thompson, and M. Lawrinenko, “Characterization and quantification of biochar alkalinity,” Chemosphere, vol. 167, pp. 367–373, 2017. View at: Publisher Site | Google Scholar
  76. A. Obia, G. Cornelissen, J. Mulder, and P. Dörsch, “Effect of soil pH increase by biochar on NO, N2O and N2 production during denitrification in acid soils,” PLoS One, vol. 10, no. 9, Article ID e0138781, 2015. View at: Publisher Site | Google Scholar
  77. A. Demeyer, J. C. Voundi Nkana, and M. G. Verloo, “Characteristics of wood ash and influence on soil properties and nutrient uptake: an overview,” Bioresource Technology, vol. 77, no. 3, pp. 287–295, 2001. View at: Publisher Site | Google Scholar
  78. T. Ohno and M. Susan Erich, “Effect of wood ash application on soil pH and soil test nutrient levels,” Agriculture, Ecosystems & Environment, vol. 32, no. 3-4, pp. 223–239, 1990. View at: Publisher Site | Google Scholar
  79. D. Neina and G. Dowuona, “Short-term effects of human urine fertiliser and wood ash on soil pH and electrical conductivity,” Journal of Agriculture and Rural Development in the Tropics and Subtropics, vol. 114, pp. 89–100, 2013. View at: Google Scholar
  80. P. Hinsinger, “Bioavailability of soil inorganic P in the rhizosphere as affected by root-induced chemical changes: a review,” Plant and Soil, vol. 237, no. 2, pp. 173–195, 2001. View at: Google Scholar


Copyright © 2019 Dora Neina. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


BAF60 represses hypocotyl elongation

Because BAF60 null alleles generate severe pleiotropic developmental defects [53], we used Arabidopsis RNA interference (RNAi) lines in which its expression is downregulated to analyze its influence on hypocotyl growth [47, 52, 54]. Under both long-day (LD) and short-day (SD) photoperiods, the hypocotyl length of the two tested independent RNAi lines was significantly longer than for wild-type seedlings (Fig. 1a, b Additional file 1: Figure S1a, b). Reciprocally, two independent BAF60-CFP overexpressing lines displayed a significantly shorter hypocotyl compared to wild-type plants (Additional file 1: Figure S1c, d), suggesting that BAF60 could repress hypocotyl growth. This function was further tested upon growth at 28 °C or in the absence of light, conditions that trigger morphogenic responses characterized by enhanced hypocotyl elongation [1, 12]. Under such treatments, it was observed that BAF60 RNAi lines displayed highly elongated hypocotyls at 28 °C (Additional file 1: Figure S1e), while in darkness the BAF60 RNAi lines displayed wild-type etiolated phenotypes (Additional file 1: Figure S1f). In addition, two independent BAF60 overexpressing lines displayed significantly shorter hypocotyls than wild-type seedlings under dark condition (Additional file 1: Figure S1f). Altogether, these observations indicate that BAF60 negatively influences hypocotyl elongation under light and high temperature conditions.

BAF60 represses hypocotyl elongation. a Seven-day-old wild-type (WT) and Baf60 RNAi lines grown under LD conditions. The red arrowheads point to the top of the hypocotyl. Bar = 1 mm. b Hypocotyl length of 7-day-old wild type (WT) and Baf60 RNAi lines (RNAi_1 and RNAi_2) grown under LD conditions. Values are average ± standard deviation (n > 100). Asterisks indicate significantly different values (Student’s t-test, P < 0.05). c Ploidy level distribution of nuclei isolated from 14-day-old seedlings of the wild type (WT black) and Baf60 RNAi lines (red) grown in LD conditions. The y-axis shows the quantity of DNA in a haploid cell in G1. Data are average ± standard deviation from four independent experiments. d Cell length distribution of hypocotyls from 14-day-old wild type (WT black) and Baf60 RNAi lines (red) grown under LD conditions. For each sample, a total of 120 cells were measured

Considering that hypocotyl growth is mostly driven by cell elongation [55, 56] and that hypocotyl cell size strongly correlates with endoreduplication levels, we quantified by flow cytometry the nuclear DNA content of hypocotyls dissected from wild-type and RNAi seedlings. In agreement with their long hypocotyl phenotype, the two BAF60 RNAi lines displayed higher ploidy levels (Fig. 1c Additional file 1: Figure S1g) and larger hypocotyl cells (Fig. 1d) compared to wild-type seedlings. Taken together, these observations show that BAF60 can repress hypocotyl elongation under light and high temperature conditions, possibly through the control of both cell elongation and endoreduplication.

Regulation of BAF60 gene expression by light and the circadian clock

The observation that BAF60 RNAi lines display a long-hypocotyl phenotype under photocycles but not in continuous darkness suggests that BAF60 could be differentially active under these conditions. To test this hypothesis, we quantified BAF60 transcripts by RT-qPCR in light and dark-grown seedlings. Interestingly, BAF60 RNA levels were lower in etiolated than in photomorphogenic seedlings (Fig. 2a Additional file 1: Figure S2a). Regulation of the BAF60 gene by light was further tested by monitoring its expression during a 32-h period: BAF60 was more expressed during the day than during the night (Fig. 2b), in contrast to ST2a, which is mainly expressed during the night (Additional file 1: Figure S2b). Similarly, we assessed the BAF60 levels in a promBAF60::BAF60-CFP line through a western-blot assay, finding significantly higher levels of the protein during the daytime than in darkness (Fig. 2c). Furthermore, we assessed the stability/half-life of the BAF60 protein by measuring the level of the protein in dark and light conditions in the presence or absence of MG132, a proteasome inhibitor. The performed immunoblot analysis revealed that nuclear BAF60-CFP levels are increased by MG132 treatment in the same ratio in both dark and light conditions (Figs. 2d Additional file 1: Figure S2c), suggesting that BAF60-CFP is degraded by the 26S proteasomes, but light-independently. However, we noticed that the decrease in BAF60 gene expression occurred at dusk, before the beginning of the dark period (Fig. 2b). To investigate the potential role of the circadian clock on BAF60 diurnal regulation, we performed a time-course analysis throughout a continuous light 20-h period after entrainment by long-day photocycles. Interestingly, BAF60 was highly expressed during the period corresponding to the subjective night (Fig. 2e), indicating that light perception and the circadian clock may both contribute to fine-tune the expression of this gene. Finally, to confirm the potential effect of light on its expression, its transcript levels were measured upon a light-to-dark shift, finding, as expected, that BAF60 mRNA level decreased after 3 h of darkness (Fig. 2f).

BAF60 expression is regulated by both light and circadian rhythm. a RT-qPCR of Baf60 expression in a 7-day-old wild-type line (Ws) grown in LD (7-d-old LD) or in darkness (7-d-old darkness) conditions. Values are average ± standard deviation obtained from three independent replicates. Asterisks indicate significantly different values (Student’s t-test, P < 0.05). b RT-qPCR data showing the relative expression of Baf60 in wild-type plants under LD conditions in comparison to TOC1, a circadian clock-regulated gene. Total RNA samples were collected every 4 h from 14-day-old plants during a 32-h period (Light and Dark represent day and night periods, respectively). The grey area behind the trace represents the night period. Values are average ± standard deviation obtained from three independent replicates. c Immunoblot analysis showing the amount of BAF60 protein under light and dark conditions, obtained from a promBAF60::BAF60-CFP line. d Immunoblot analysis showing that MG132 can prevent BAF60 degradation through proteasome 26S in the same ratio under light and dark conditions. This result indicates that BAF60 degradation is independent of light. e RT-qPCR data showing the relative expression of BAF60 in wild-type plants under continuous light. The seedlings were grown under LD conditions and were then transferred to continuous day conditions for the indicated period of time. Total RNA samples were collected every 4 h from 14-day-old plants during a 20-h period. The grey area behind the trace represents the period that would have corresponded to the night. Values are average ± standard deviation obtained from three independent replicates. f RT-qPCR quantification of BAF60 expression in 7-day-old wild type seedlings grown in LD conditions and then transferred to darkness (3hrs Dark, grey) or kept in the light (Light, white) for 3 h. Values are average ± standard deviation and are representative of three biological replicates. Asterisks indicate significantly different values (Student’s t-test, P < 0.05). g BAF60 promoter activity observed by RT-qPCR quantification of GFP expression in 7-day-old BAF60::WPP-GFP-BLRP seedlings grown in LD conditions and then transferred to darkness (3hrs Dark, grey) or not (Light, white) for 3 h. Values are average ± standard deviation obtained from three independent replicates. Asterisks indicate significantly different values (Student’s t-test, P < 0.05)

To test the potential direct effect of light on BAF60 transcription, the BAF60 promoter was cloned upstream of a green fluorescent protein (GFP) coding sequence and stably introduced into wild-type plants (BAF60::WPP-GFP-BLRP). GFP transcript levels were quantified upon transferring light-grown plants to darkness, showing a significant decrease after 3 h (Fig. 2g), similar to the phenomenon observed when measuring the expression levels of the BAF60 locus through RT-qPCR. Comparison of GFP RNA levels between dark- and light-grown seedlings also showed more BAF60 promoter activity in the light condition (Additional file 1: Figure S2d). Finally, analysis of public genome-wide data allowed us to determine that the BAF60 promoter domain becomes more susceptible to DNase I footprinting during de-etiolation [29] (Additional file 1: Figure S2e), corroborating its enhanced transcriptional activity in response to light. Taken together, these results indicate that BAF60 expression is regulated at the transcriptional level, and possibly also at the post-transcriptional level, by light and the circadian clock.

BAF60 binds nucleosome-free regions of expressed genes

In order to assess how BAF60 influences seedling development, we explored the genomic loci at which BAF60 could exert its activity by chromatin immunoprecipitation-sequencing (ChIP-seq) of BAF60-CFP, using an anti-GFP antibody in light-grown plants. Through this method we identified a wide repertoire of 5853 BAF60-CFP binding peaks (Fig. 3a) corresponding to 3729 genes. The large majority (74%) of BAF60 binding sites corresponded to intergenic regions and to 500-bp domains upstream of transcription start sites (TSSs), which usually correspond to promoter regions (Fig. 3b). Interestingly, the average profile of all mapped reads over a gene model further revealed that BAF60 is enriched over both the 5′ and 3′ ends of genes, confirming our previous studies [47, 50,51,52] (Additional file 1: Figure S3). Because it has been previously reported that BAF60 is a component of plant CRCs [46], the relationship between nucleosome regions and BAF60-enriched loci was explored by mapping MNase hypersensitive sites using MNase-seq assay on wild-type plants. Through this method we found that the BAF60-CFP peaks largely anti-correlate with nucleosomal occupancy in wild-type seedlings (Fig. 3c). Furthermore, we performed an ATAC-seq (assay for transposase-accessible chromatin using sequencing), a technique that allows precise positioning of nuclesosome-free regions (NFRs) [57]. Interestingly, this revealed a perfect correlation between NFR profiles and BAF60 positioning around the TSSs (Fig. 3d). Altogether, these analyses uncovered that BAF60 is frequently enriched over the 5′ NFR of hundreds of genes, in agreement with a potential direct influence of BAF60 on local transcriptional controls.

BAF60 binds nucleosome-free regions of transcribed genes. a Comparison between BAF60 and input of tag density in the region ±5 kb around the BAF60 peaks. ChIP-seq was performed on 14-day-old BAF60-CFP overexpressing plants (OE BAF60-CFP_1) grown under LD conditions. b Pie chart representation of the distribution of BAF60 peaks identified by ChIP-seq in four different genomic regions. The definition of each region is described above. TES transcription end site, TSS transcription start site. c Mean profile of BAF60 ChIP-seq and MNase-seq read density with respect to a gene model from TSS to TES. Normalization of coverage using spline algorithm was performed over the genes and flanking 2-kb region. d Merged profiles of BAF60 ChIP-seq and ATAC-seq read density over TSS and flanking 2-kb region. e Average enrichment profile of BAF60 is correlated with gene expression variation. Gene expression is categorized from low (first quantile) to high (fourth quantile) expression. Mean-normalized ChIP-seq densities of equal bins along the gene and 2-kb region flanking the TSS or the TES are plotted. Highly expressed genes show higher enrichment for binding of BAF60. f Co-ocurrence matrix representing the colocalization of H3K9ac marks and BAF60 binding along the BAF60 targets

To understand the relationship between BAF60 and gene expression, we tested how its enrichment relates to mRNA levels. This revealed a positive correlation between the binding frequency of this protein and the number of transcripts of specific loci (Fig. 3e). We also analyzed the relationship between gene expression and DNA accessibility in a genome-wide fashion, finding that the most expressed genes display a highest level of accessibility in their 5′ and 3′ ends compared to less transcribed genes (Additional file 1: Figure S4). Finally, to determine whether BAF60 associates with specific chromatin contexts, we compared its genome-wide positioning with publically available epigenomic profiles of histone modifications. This showed that more than 50% of the BAF60 target genes are marked by histone 3 lysine 9 acetylation (H3K9ac), confirming that BAF60 frequently binds active chromatin regions (Fig. 3f). On the other hand, BAF60 binding co-localizes, but to a much lower extent, with the repressive marks H3K27me3 and H3K9me2, not only in the gene body but also in the promoter regions, upstream of the TSS (Additional file 1: Figure S5).

BAF60 targets G-box motifs and acts antagonistically to PIF4

Given that BAF60-CFP distribution is enriched over NFRs, we hypothesized that BAF60-associated CRCs could recognize specific DNA-sequence motifs rather than chromatin signatures. De novo motif discovery using the HOMER software identified the G-box consensus sequence (CACGTG) as an over-represented cis element within the BAF60 peaks (Fig. 4a Additional file 1: Figure S6a). This motif is known to recruit multiple transcription factors such as PIF4 [58]. Using the known repertoire of PIF4-binding sites identified by ChIP-seq using pifQ/pPIF4::PIF4myc transgenic plants [58], we firstly found that 17.96% of PIF4 targets displayed a G-box motif positioned 500 bp upstream of the TSS (Additional file 1: Figure S6b). Interestingly, the same analysis performed with BAF60-CFP peaks identified that 17.94% of the BAF60 targets (669 targets out of 3730) also contain a CACGTG motif over the 500-bp upstream domains (Fig. 4b). Furthermore, direct comparison of the PIF4 and BAF60 target genes identified a large overlap, 39.6% of BAF60 targets with PIF4 targets, suggesting that these two proteins regulate common genes (Fig. 4c). Accordingly, dot-plot analysis of the precise BAF60-CFP and PIF4 peaks along gene structures further showed frequent clustering along the same positions (Fig. 4d). Finally, visual inspection of BAF60-CFP and PIF4 peaks confirmed their similar distributions over many genomic positions, the majority of which correspond to G-box motifs (Fig. 4e). A gene ontology analysis, using the agriGO tool, revealed that there is a significant enrichment of genes involved in the response to light stimuli, to far red and blue light, and to temperature in the common targets of BAF60 and PIF4 (Additional file 1: Figure S7). Altogether, these analyses uncover a potential role of BAF60 at hundreds of PIF4 binding sites that represent targets for both proteins, involved in the regulation of photomorphogenesis and heat responses.

BAF60 preferentially associates with G-box motifs and shares a large repertoire of target genes with PIF4. a HOMER motif search identifies a major BAF60-associated motif defined as the G-box (CACGTG). P value = 1e-296. b Venn diagram representing the overlap between BAF60 target genes and genes containing a G-box motif in their promoter (500-bp upstream domains). c Venn diagram representing the overlap between BAF60 and PIF4 target genes. d Density plot showing overlap of PIF4 and BAF60 using hexagonal binning routine. As a large number of data points may overlap, hexagonal binning gives an additional dimension of differentiation of overlapping points based on count. Each point represents the distance of the midpoint of a peak to the nearest gene. On the y-axis is the location of the midpoint of a PIF4 peak in comparison to gene position on the x-axis is the location of a midpoint of BAF60 in comparison to the nearest gene. A large number of points occurs along the positive correlation line, showing the co-occurrence pattern of PIF4 and BAF60. e Genome Browser snapshots of BAF60 (green) and PIF4 (red) ChIP-seq peaks on two representative genomic regions of chromosome 4 [chr4:10,235,000–10,361,000] (left) and chromosome 1 [chr1:28,285,000–28,381,000] (right). Genes are shown in black and G-boxes in pink. f Venn diagram representing the overlap between the BAF60 targets, found through ChIP-seq, and the missregulated genes in the Baf60 RNAi lines. The values in parentheses (grey) correspond to the common elements expected by chance, while those in black or red represent the observed results. The values in red represent significant enrichment, while those in black are not significantly different from those expected by chance (Chi-squared test). g Venn diagram representing common missregulated genes in the Baf60 RNAi line and the pifq mutant. The values in parentheses (grey) correspond to the common elements expected by chance, while those in black or red represent the observed results. The values in red represent significant enrichment, while those in black are not significantly different from those expected by chance (Chi- squared test)

To understand the impact of BAF60 on gene expression we performed a transcriptomic analysis of BAF60 RNAi lines and found that 1103 of the BAF60 targets displayed missregulation in both of the BAF60 RNAi lines, 404 downregulated and 699 upregulated (Fig. 4f). This result suggests that BAF60 regulates either positively or negatively the expression of several of its targets. A gene ontology analysis of the BAF60 target genes that are upregulated in both RNAi lines revealed that there is a significant enrichment of genes involved in the response to light stimuli and hormone-mediated signaling (Additional file 1: Figure S8).

Having shown that BAF60 and PIF4 bind to hundreds of common genes, we compared the RNA-seq data obtained from BAF60 RNAi lines and a pifq mutant, finding that a representative group of genes displayed opposite transcriptomic profiles 264 genes are downregulated in BAF60-RNAi lines but upregulated in pifq, while 196 are upregulated in BAF60-RNAi lines but downregulated in pifq (Fig. 4g). In addition, from the shared BAF60 and PIF4 targets, 49 genes are upregulated in the BAF60 RNAi line and downregulated in the pifq mutant (Fig. 5a). In all these cases the number of enriched genes is greater than what would be expected by chance, supporting the antagonistic function of the two proteins regarding the expression of this set of genes. Interestingly, a gene ontology analysis revealed that, of the 49 oppositely regulated targets in the two accessions, a significant group is involved in processes such as response to hormones (notably auxin), to light, and more specifically, to red light (Fig. 5b).

BAF60 has opposite effects than PIF4 on the expression of hypocotyl elongation regulatory genes. a Venn diagram displaying the elements in common between the BAF60 targets missregulated in the Baf60 RNAi line and the PIF4 targets downregulated in the pifq mutant. The values in parentheses (grey) correspond to the common elements expected by chance, while those in black or red represent the observed results. The values in red represent significant enrichment, while those in black are not significantly different from those expected by chance (Chi2 square test). b Gene ontology analysis of the common targets oppositely regulated by PIF4 and BAF60. The red bars represent the input and the black bars the reference. This group of loci is significantly enriched in genes involved in response to auxin, hormones, and light. c Genome Browser snapshots of ATAC-seq, BAF60, and PIF4 ChIP-seq peaks on six common target genes (IAA19, ST2a, XTR7, SDR, HFR1, and BEE1). BAF60-associated peaks are shown in green, PIF4-binding peaks in red, ATAC-seq peaks in blue, annotated genes in black, and G-box motifs in pink. d RT-qPCR analysis showing the relative expression of the indicated genes in 7-day-old seedlings in LD conditions. Values are average ± standard deviation obtained from three independent replicates and asterisks represent significant difference from the wild type (WT Student’s t-test, P < 0.05). e DNA accessibility measured by FAIRE-qPCR in 7-day-old wild-type and BAF60 RNAi seedlings grown in LD conditions. Higher values correspond to more accessible DNA. Primer pair 1 was used for ST2a, SDR, and HFR1 loci, pair 3 for IAA19 and BEE1 loci, and pair 2 for the XTR7 locus. Error bars represent the standard deviation from three biological replicates and asterisks represent significant difference from the WT (Student’s t-test, P < 0.05)

Given the prominent role of PIF4 and the negative effect of BAF60 on hypocotyl elongation, we analyzed in detail the functional relationship of BAF60 with cell size regulatory genes that impact this process. These were found to be targeted by both PIF4 and BAF60 and upregulated in the BAF60 RNAi line and downregulated in the pifq mutant (IAA19, ST2a, XTR7, SDR, HFR1, and BEE1). Our ChIP-seq and ATAC-seq analyses indicated that both BAF60 and PIF4 target the promoter and/or the gene body of these genes around a G-box motif at loci with high DNA accessibility (Fig. 5c). Targeted ChIP-qPCR further confirmed that BAF60-CFP is enriched over these loci (Additional file 1: Figure S9). To confirm its influence on their expression, we quantified the transcripts of these genes upon knocking down BAF60. RT-qPCR analysis showed highly increased mRNA levels in the two BAF60 RNAi lines compared to wild-type seedlings for the six genes (Fig. 5d). Consistently, expression of these genes was reduced in overexpressing (OE) lines (Additional file 1: Figure S10a). Altogether, these observations indicate that BAF60 represses these genes in cis.

Given the implication of BAF60 in modulating histone composition and occupancy at the FLC locus [47], as well as its enrichment over the IAA19, ST2a, XTR7, SDR, HFR1, and BEE1 genes, we proposed that the BAF60 CRC may potentially repress transcription of these genes through nucleosome remodeling. We therefore tested the effect of knocking down BAF60 on local chromatin accessibility, using a targeted formaldehyde-assisted isolation of regulatory elements assay (FAIRE) [59]. Interestingly, this approach showed that the promoter region of the six tested genes displayed a more open conformation in the two analyzed BAF60 RNAi lines compared to wild-type seedlings (Fig. 5e). On the other hand, and coherently, the OE line presented reduced promoter accessibility in the studied genes in darkness, reaching levels similar to those of the WT under light conditions (Additional file 1: Figure S10b). Taken together, these analyses indicate that BAF60 antagonizes the role of PIF4 in the expression of genes controlling hypocotyl elongation by decreasing their accessibility and thereby repressing their transcription. Furthermore, through a ChIP-qPCR assay, we found that, in the BAF60 RNAi lines, the enrichment of PIF4 at its target loci is increased, in contrast to the BAF60 OE lines, where PIF4 binding is reduced (Fig. 6). These results indicate that BAF60 and PIF4 actively compete in vivo for their targets.

BAF60 and PIF4 actively compete in vivo for their targets. Seven-day-old plantlets of the two BAF60 RNAi lines, a BAF60 OE line, and the corresponding WT (WS and Col0, respectively) were used to analyze PIF4 binding on selected target genes (IAA19, ST2A, and XTR7) by ChIP-qPCR. PIF4 binding to targets is increased in the darkness in the BAF60 RNAi lines in comparison with the WT. On the contrary, its binding was diminished in the BAF60 OE line, indicating that BAF60 and PIF4 compete for their targets and that changes in BAF60 levels affect PIF4 binding. Data are average of three technical replicates ± standard deviation. Asterisks indicate significantly different values (Student’s t-test, P < 0.05). D dark, L light


Power cascade growth is not accounted for by existing models of tooth growth

There are currently two general models that each strive to describe or explain various aspects of tooth development. Enamel knots produce inhibitory signals that prevent new enamel knots forming close to an existing knot [17]. The ‘patterning cascade’ model describes how this inhibition, along with the folding of the epithelial-mesenchyme interface, creates limitations on the size and position of successive cusps during development [33]. First described in seal postcanine teeth, the patterning cascade model has since been extended to primate molars [34, 35]. The second model, the ‘inhibitory cascade’, describes the relative size of sequentially produced teeth, such as molars, as a linear change in size along a tooth row [2, 36]. Neither of these models addresses the shape of cusps.

The power cascade model proposed here is a third general model of tooth development complementary to the two existing models, indicating how the shapes of unicuspid teeth and individual cusps are generated. After determination of cusp shape by the power cascade model, we postulate that cusp spacing is dictated by inhibition of enamel knots according to the patterning cascade [33], and number of cusps is controlled by the number of enamel knots that can fit in the total area of the tooth. The sizes of sequential teeth are then directed by the inhibitory cascade [2, 36]. Therefore, cusp shape, cusp number, and tooth size can be simulated according to this trio of models to generate the main features of an entire tooth row.

The power function has been used to represent or measure a limited set of teeth in previous studies, including the tips of shapes designed for mechanical penetration testing [37], using an average Slope of 0.5. Felid canine profiles measured using power functions [38] showed that they generally had a Slope of

0.55. Both of these studies are consistent with the current findings in many mammal canines, but they did not generalise this pattern to all teeth or cusps.

Detailed developmental computer simulations of tooth morphogenesis have used a 3D reaction-diffusion-like model that calculates bending stresses to form cusps and teeth [39, 40]. This model produces cusp positions that can have morphological variation similar to biological teeth [39, 41]. Here we tested whether the cusp shapes produced by that model conform to the power cascade model. Varying five parameters of the model that simulates the development of ringed seal postcanine teeth [41] shows that most of the cusp shapes produced do not closely resemble the expected power cascade, with R 2 between 0.59 and 0.97 (Additional file 1: Figure S6). Therefore, the power cascade model describes cusp shape (or cross-sectional profile) substantially better than complex in silico models, although this may be a result of the limited number of cells in the simulations.

Given the power of this new model to define the limits of tooth shape in animals, we expanded our focus to compare it with existing models of growth in other morphological systems. Wren’s [19] model of shells growing as a cone bending to form a logarithmic spiral has since been used to model shells and teeth [6, 7, 20]. Starting with a cone, a logarithmic spiral is generated when one side grows faster than the other, causing the cone to bend to one side (Fig. 1b Additional file 1: Figure S7d). A mechanism to generate a logarithmic spiral is the unequal growth rates of the two sides A and B. Logarithmic spirals have a formula in polar coordinates S = a e b θ , where θ is the angle of rotation around the origin, S is the resulting radius of the logarithmic spiral, and a and b are parameters affecting the size and rate of expansion of the spiral, respectively (Additional file 1: Figure S7a). The radius of the shell opening expands linearly with the angle of rotation (Radius = c θ, where c is a parameter affecting the rate of growth of the shell opening), which creates a cone spiralling around the central axis (Additional file 1: Figure S7b). This model was used to generate shell shapes of many types by modifying relative rates of growth [7, 42, 43].

The Raup [7] shell equation describes shell growth using a cone, which is the shape where Slope = 1 in our Log Distance-Radius plots (Fig. 6 Additional file 1: Figure S2). If this model accurately describes shell growth, all shells should fall on the right-hand edge of the morphospace in Fig. 6. The shells of molluscs (scaphopod Dentalium sp. and gastropod Bembicium auratum) and cephalopods (nautilus Nautilus pompilius and ram’s head squid Spirula spirula) each apparently form logarithmic spirals, but follow the power cascade with Slope between 0.37 and 0.88 (Fig. 6). This shows that power cones can bend to form logarithmic spirals in an analogous manner to that first proposed by Wren [19] for cones (a specific power cone Fig. 1d). It also establishes that not all shell shapes can be generated by the existing model of development [7]. In order to accommodate such shapes, the Raup [7] model must have the Slope parameter added, such that Radius = c θ Slope . In the first description of the shell growth model, Raup [44] assumes that ‘the rate of expansion of the generating curve is approximately constant’, i.e. Slope = 1, and so this parameter was not included in his model. In contrast, Thompson [6] suggested that the growth may not be constant in some shells but in fact vary ‘in accordance with some simple law’, and Ackerly [45] showed that for some shells there is an allometric component to the change in radius. Our power cascade model accounts for this important feature of growth.

Pointed structures in vertebrates, invertebrates, and plants follow the power cascade model. a Log Distance vs log Radius for structures found in animal and plant classes. b Occupation of non-tooth structures in Slope-Aspect Ratio morphospace. Note that none of the structures, including shells, fall at Slope = 1 where the shape is a cone. Bird beak and gastropod shell labels indicate different specimens in the two graphs

The long axis of each tooth grows as a logarithmic spiral [6, 46], which can be seen in an extreme form in the curved upper tusks of the babirusa Babyrousa celebensis. However, we find that the Slope of these tusks (0.25) is considerably less than 1, and therefore, they are not conical (Fig. 5): their high Aspect Ratio can make them appear more conical. This means that teeth cannot be modelled by the Raup [7] shell equation. The radius of the circle must change logarithmically with the angle of rotation to form a power cone, rather than a straight-sided cone with Slope = 1.

A general model of growth for horns, claws, spines, beaks, and thorns

Thompson [6] expected that pointed and spiral structures such as horns and claws would follow the same growth pattern as shells, which has been used to model some horn-like structures [47]. If horns grow according to the shell model and are spiralled cones, then their Slope parameter will be 1. From measurements of bony horn cores from vertebrates including mammals, non-avian dinosaurs (referred to here as dinosaurs) and reptiles, we have found that Log Distance-Radius plots are linear and the Slope is typically between 0.4 and 0.8 (Fig. 6 Additional file 1: Figure S8), demonstrating that they do follow the power cascade but are not growing according to the original conical shell model.

Other structures throughout vertebrates also show power cascade growth, including mammal, bird and dinosaur claw and hoof bones (unguals), the bony beaks of birds and dinosaurs, and spines of fish (Fig. 6). Outside vertebrates, the power cascade model is also followed in arthropod fangs and cephalopod beaks. Beyond animals, it is found in thorns and prickles in plants (Fig. 6).

The rose prickle (generally called a thorn) represents an interesting exception. While the concave shape of a mature prickle does not follow the power cascade prediction, a young prickle does (Additional file 1: Figure S9). It appears that the prickle is initially generated following the power cascade growth with Slope = 0.6, but then as the stem to which it is attached grows, the base of the prickle is stretched along the long axis of the branch. The result is the typical concave shape of a rose prickle, where only the top half follows the power cascade, not the basal half that has been stretched (Additional file 1: Figure S9). In general, it appears that deviations from the power cascade are more likely in pointed structures controlled by multiple growth processes.

The power cascade model can be added to the logarithmic spiral model to generate a ‘power spiral’ that can simulate realistic shapes of pointed, curved structures (Additional file 1: Figure S7c). Figure 7 shows some comparisons between real teeth and power spiral models, using both circular cross-sections that would be generated in surfaces of revolution and other cross-sectional shapes (elliptical, lenticular, truncated circle) implemented in a Mathematica notebook (v. 12.0, Wolfram Research Inc., Champaign, IL) available in the Supplementary Information (see also Additional file 1: Figure S10).

Power spiral (power cascade with a central axis of a logarithmic spiral) can closely emulate real teeth from all vertebrate groups. 3D scan models (grey) and simulated teeth (orange) in two views for megalodon shark Carcharocles megalodon (NMV P28786), mosasaur Globidens alabamensis (USNM 54078), tyrannosaurid Tyrannosaurus rex (UWBM 99000), African elephant Loxodonta africana (NMV C30765), babirusa pig Babyrousa celebensis (ZMB MAM033677), and sabre-tooth cat Smilodon fatalis (LACM HC2000R43)

The majority of the structures that are closely emulated by the power cascade grow from tip to base, including teeth, horns, thorns, and prickles. These shapes are presumably formed as each addition of material increases the radius by a constant proportion for a proportional increase in length. For example, bovid horns grow from tip to base, increasing in radius down the horn, and they generally follow the power cascade model. In contrast, cervid antlers grow from base to tip, with the growing antler branching, and the antler points being the last structures to form. Despite this directional difference in growth—and the antler starting from a wider base and narrowing towards the tip—antler points also follow the power cascade (Fig. 6). This shows that the proportional growth pattern can act both when increasing the radius of the structure as it cascades downwards from the tip to the base, and also when decreasing the radius to cascade upwards from base to tip. It appears that only the direction of radial growth differs between these two scenarios.

Since many of the structures examined here (including teeth and claws) are used to penetrate food or other materials, it may be argued that selection to maximise penetration ability or structural strength is the cause of the underlying similarity in shape as described by the power cascade model. However, many structures that are not for penetration (such as shells, rounded teeth or backward-curving horns) still follow the power cascade pattern. Given that structures that conform to the power cone can vary from sharp and long to blunt and short, we argue that the most parsimonious explanation for the model fit is an underlying biophysical or developmental mechanism rather than strong selection for shapes that coincidentally fit a power cascade-like pattern. The power cascade generates a base set of allowed variations (Fig. 5), and selection chooses from among these shapes, as occurs with the selection of relative tooth size in hominins according to the inhibitory cascade [36].

Mechanism and generality of power cascade

The log-log linear pattern of the power cascade can be compared with allometric plots of the relative sizes of body components during growth [20], such as head size versus body size in humans. A linear allometric relationship is produced when two components grow exponentially at different rates. The power cascade relationship shows that there is an allometric relationship within the same structure due to differential growth rates of Radius and Distance.

We can demonstrate this growth process by examining power function growth in Distance and Radius over time (Fig. 8a): DistanceTime rD and RadiusTime rR , where rD and rR are the growth rates for Distance and Radius, respectively. Power function growth is very common in biology, including for human height [48] and elephant tusks (Fig. 4b). When both axes of the growth over time curves are logged, the plot log(Distance) vs log(Time) is linear with slope rD (similarly for Radius and rR Fig. 8b). By solving the log(Distance) equation for log(Time) and substituting into the log(Radius) equation, the relationship between log(Distance) and log(Radius) through time becomes apparent (Fig. 8c). If rD and rR are equal, then Radius increases linearly with Distance (Fig. 8d) and produces a conical shape (with Log Distance-Radius power cascade Slope of 1). If instead the rates of growth of Distance and Radius differ (e.g. rD = 2rR), then the log-log growth over time trajectories will not be parallel (Fig. 8e-f), and the result will be a power cone such as a paraboloid (Fig. 8h). The Log Distance-Radius power cascade Slope of such a structure will be rR/rD = 0.5 (Fig. 8g).

Generation of power cones through allometric growth of Distance and Radius. From power function growth of both Distance and Radius (with growth rates rD and rR, respectively) through time (a, b, e, f), the shape of the structure is determined by the ratio of the growth rates (c, g). Where rD = rR, a cone is formed (d), while where rD > rR, a curved-sided power cone is generated (h). The general equations are shown on the left, while example parameters are shown in the graphs and accompanying equations. See Additional file 1: Supplementary Equations for mathematical derivation

Therefore, the power cascade is an expression of allometry as a shape: power cones show unequal power growth within the same structure, or ‘constant differential growth-ratios’ in the terminology of Huxley [20]. The cone is produced through isometric growth between Distance and Radius, while a power cone results from allometric growth (rDrR). The same shapes can also be generated through exponential (as opposed to power) growth of body parts, although this is not commonly found in organisms. Constant differential growth of the two sides of a structure must generate a logarithmic spiral ([20] Fig. 1b). In the same manner, differential power growth of Distance and Radius must generate a power cone (Fig. 1c). Both mechanisms could operate at the same time, forming a power cone on a logarithmic spiral, or a power spiral (Fig. 1d).

The power cascade, and likewise the logarithmic spiral, can be seen as ‘dynamical patterning modules’ [49] that generate patterns and structures in metazoans and plants. Despite over three centuries of research [19], the specific molecules driving logarithmic spiral growth are not known (although recent work has begun to reveal some components in gastropod shells [50]). Likewise, the identity of signalling molecules and genes that influence the differential growth of the power cascade very likely must vary widely across animals and plants. Here we show that common growth patterns in animals and plants generate power cones. These shapes may be considered the default family of shapes for pointed structures, meaning they are more likely to independently evolve multiple times and will be a likely source of homoplasy in evolution.


Hybrid Euryale ferox Salisb. expressed significant heterosis, resulting in non-prickly, thin-coated, large seeds, which accounted for the significantly larger yield of HL than that of WT. Through the study, we found that some SAURs may act as a positive mediator of the auxin transduction pathway, thereby contributing to the observed larger seed. The gene functions of these SAURs in Euryale ferox Salisb., and the underlying mechanisms deserve further investigation.

10.11: Plant Growth - Biology

2) Vascular Tissue System
Function: Conduction of water, nutrients, sugars and hormones throughout the plant.
a) xylem - conducts water and nutrients up roots, stems and leaves.
b) phloem - conducts water, sugar, hormones, etc. down and up roots, stems and leaves
moves from where produced (called sources ) to where needed (called sinks ).

  • thin, non-lignified primary cell walls
  • filler, storage, protection, photosynthesis
  • examples: flesh of potato, lettuce leaf
  • unevenly thickened, non-lignified primary cell walls
  • support in growing tissues
  • example: strings in celery stalks

2 Types
a) fiber

  • evenly thickened, lignified (tough) secondary cell walls
  • dead at maturity
  • support in mature tissue
  • examples:
    fiber - bamboo cane
    sclereid - seed coat
    stone cell - pear fruit
    a) polysaccharide - a polymer or chain of sugars
      1) cellulose - forms a matrix of microfibrils (chains of b -1,4-linked glucose , see below)
      2) hemicellulose - filler between cellulose microfibrils (chains of misc. sugar)
      3) pectin - cementing agent or filler high in middle lamella and fruit (chains
      of galacturonic acid)

    3) plasmodesmata - tubular plasma membrane extensions through cell walls that connect
    adjacent cells.

      a) cytosol - much of the cytoplasm is a water solution of dissolved compounds
      b) organelles - specialized structures in cytoplasm, each with specific functions.
        1) nucleus - location of DNA and some of the RNA
          2) mitochondria - major site of respiration called the "power house" of the cell.
          3) plastid - double membrane-bound bodies for storage and photosynthesis
            a)leucoplast - colorless plastids
              1) amyloplast - starch storage (chains of a -1,4-linked glucose , seebelow)
              2) elaioplast - fat and oil storage
              a) tonoplast - membrane that surrounds the vacuole

            Base Pairing of Nucleic Acids between the double strands of DNA
            A - T (adenine-thymine)
            G - C (guanine-cytosine)
            Base Pairing of Nucleic Acids between DNA strands and RNA strands
            A - U (adenine-uracil)
            G - C (guanine-cytosine)

            meristem - discrete regions or groups of cells that possess continued cell division for the
            life of the plant or that organ.

            1) Primary Growth - growth in length that gives rise to primary (herbaceous) tissues
            called the primary plant body.

            lateral meristem - meristematic regions along the sides of stems and roots.

            2 Types of lateral meristems give rise to secondary growth
            a) vascular cambium or cambium - a sheet-like meristem between the bark and wood
            along the sides of woody stems and roots it gives rise to secondary xylem (commonly called wood ) on
            the inside and secondary phloem on the outside.
            b) cork cambium or phellogen - gives rise to the periderm (commonly called bark ).

            1) photosynthesis site where primarily occurs
            2) regulate water loss e.g. by opening and closing stomata
            3) storage ex. carbohydrates and water in garlic, aloe vera
            4) support ex. tendrils on grape
            5) protection ex. spines on cacti bud scales
            6) attraction ex. bracts on poinsettia or dogwood
            7) propagation ex. bryophyllum with plantlets on leaves

            terminal bud - a bud at the tip of a stem responsible for terminal growth.

            axillary bud or lateral bud - buds along side the axis of a stem they were produced by the terminal bud during growth once they grow out and form a lateral stem they become terminal buds of the lateral branch.

            flower bud - a bud containing a floral meristem which develops into flowers usually larger than vegetative buds.

            leaf scar - a scar marking the former point of attachment of a leaf or petiole to the stem.

            internode - the part of the stem between nodes

            node - part of stem marking the point of attachment of leaves, flowers, fruits, buds and other stems.

            lenticel - rough areas on stems (and some fruits, ex. apple) composed of loosely packed cells extending from the cortex through the ruptured epidermis serve as "breathing pores" for gas exchange. Only occur on young stems.

            Mechanism of Opening
            a) open when guard cells are turgid (due to water uptake in response to potassium influx)
            b) closed when guard cells are flaccid (due to water loss in response to potassium efflux)

            Daily Cycle
            C-3 and C-4 Plants
            a) open during day
            b) closed during night
            CAM Plants
            a) open during night
            b) closed during day

            Designed for gas exchange
            a) CO2 in and 02 out for photosynthesis
            b) CO2 out and 02 in for respiration
            c) H20 out during transpiration

            Palisade parenchyma
            a) Contains 70-80% of the chloroplasts in the leaf.
            b) Specialized for photosynthesis - because it contains a large number of chloroplasts
            and it occurs towards the top side of leaf
            Spongy mesophyll
            a) Contains large air spaces
            b) Specialized for gas exchange - because of the large air space and more stomata occur in
            the epidermis of lower leaf surface

            Customer reviews

            Top reviews from the United States

            There was a problem filtering reviews right now. Please try again later.

            Botany good books about plants lately?

            I homeschool some friends' children. Two are seventh graders this year and one a ninth. I asked them what they would like to so for science -- each could choose a subject and I would prepare a ten week course on it. One chose botany. I have never taught botany before, although I thought I knew a pretty decent amount in that area. So I ordered this book.

            I learned so much! And I was able to take most of the material and simplify it to a jr. hi level. The book is very well done and the part about strange plants at the end got me going on the net for some more examples the kids would like. Because we did cell structure last year, I only need to review it this year. This book, however, does an excellent job of going 'ground up' in terms of the need for information to build on. Highly recommended. (Note -- it is aimed at students who are in college, to help them if something in their course was difficult or confusing, but it is very lay friendly)

            I demand a refund. The book was broken.

            I’m disappointed, I downloaded this book for a class and blocks of the text are whited out. I can select and copy those blocks of text, but I can’t directly read them. Not sure how to resolve this.

            Plant Hormone

            Question 3 What is the function of growth regulators.Give example?

            Question 4 What are growth inhibitors?Give example?

            Question 5 What are auxins?

            Question 6 What are Gibberellins?

            Question 7 What is the function of cytokinins?

            Question 8 Name the stress hormone in plants that function during night?

            Question 9 Name plant hormone which are growth promoters?

            Question 10 Which plant hormone help in ripening of fruits?

            Question 11 How does control and coordination take place in plants?

            Plant Hormone(Phytohormones)
            Phytohormones are naturally occurring organic chemical substances present in plant which bring about control and coordination of various activities in them.

            These phytohormones are synthesised in minute quantities in one part of plant body and simply diffuse to other parts where they influence specific physiological process.

            Growth Regulators
            Growth in plants mainly occur by the activity of meristematic cells.The new cells are continually Produced by cell division in a meristem.

            Meristematic cells are present at the apex of every root and shoot.
            Three phases of cell growth are:
            1)Cell division
            2)Cell enlargement
            3)Cell maturation
            They are controlled by phytohormones.

            5 Major type of phytohormones which are involved in control and coordination are:
            Abscisic acid

            Growth promoters:These stimulate the plant growth .
            For Ex:Auxins,Gibberellins,cytokinins,ethene

            Growth Inhibitors:These retard the plant growth.

            Auxins:When a plant detect light,auxin hormone is synthesised at the shoot tip,help the cells to grow longer.
            When light is coming from one side of plant,auxin diffuse towards shady side of shoot.This concentration of auxin stimulate the cells to grow longer on the side of shoot which is away from light.Thus plant appear to bend towards light.

            It promotes stem,fruit,growth,regulates tropism.

            Gibberellins:They help in growth in stem and fruits,cell enlargement,cell differentiation.

            Cytokinins:They promote cell division,help in breaking dormancy of seeds,delay the ageing of leaves,promote opening of stomata,promote fruit growth.

            Ethene:It promote growth and ripening of fruit,help in breaking the dormancy in bud.

            Abscisic acid:It promotes dormancy in seeds and buds and thus inhibits growth,promote closing of stomata,falling of leaves.

            Bo Liu

            We are cell biologists devoted to advancing our knowledge of the cytoskeleton and intracellular motility in plant and fungal cells. Our ongoing investigations include the dynamics of microtubules and actin microfilaments during plant cell division and cell growth Functions of kinesin motor proteins in mitosis and cytokinesis Molecular mechanisms of cytoskeleton-mediated hyphal growth in filamentous fungi. Experiments are carried out in organisms like Arabidopsis thaliana, Oryza sativa (rice), and Gossypium hirsutum (cotton) which are used as reference systems for plant studies, and Aspergillus nidulans as a model for fungal studies.

            Grad Group Affiliations

            Specialties / Focus


            • PLB 113 Molecular and Cellular Biology of Plants, Spring
            • BIS 104 Regulation of Cell Function (Cell Biology), Fall
            • 2203 & 2209 Life Sciences
              • Ms. M. Ximena Anleu Gil, Graduate Student. B.A., Swarthmore College, Swarthmore, PA
              • Mr. Xiaojiang Guo, Graduate Student. B.S., Sichuan Agricultural University, Chengdu, China
              • Mr. Calvin H. Huang, Graduate Student. B.S., University of California, Davis, CA
              • Mr. Huan Huo, Graduate Student. B.S., Sun Yat-sen University, Guangzhou, China
              • Dr. Yuh-Ru Julie Lee, Research Plant Biologist. Ph.D., University of Georgia, Athens, GA

              Professional Societies


              • 1985, B.S. Cell Biology and Genetics, Peking University
              • 1988, M.S. Cell Biology, Peking University
              • 1995, Ph.D. Botany, University of Georgia


              Xu, J., Y.-R.J. Lee, and B. Liu. 2020. Establishment of a mitotic model system by transient expression of the D-type cyclin in differentiated leaf cells of tobacco (Nicotiana benthamiana). New Phytologist. 226(4):1213-1220. (A traditionally 6-12-month experiment becomes a one-week journey!)

              Miao, H., R. Guo, J. Chen, Q. Wang, Y.-R.J. Lee, and B. Liu. 2019. The gamma -tubulin complex protein GCP6 is crucial for spindle morphogenesis but not essential for microtubule reorganization in Arabidopsis. Proc Natl Acad Sci U S A 116 (52):27115-27123 .

              Boruc*, J., X. Deng*, E. Mylle, N. Besbrugge, M. Van Durme, D. Demidov, E. Tomastikova, C. T.-R. Tan, M. Vandorpe, D. Eeckhout, T. Beeckman, M. Nowack, G. De Jaeger, H. Lin, B. Liu#, and D. Van Damme#. 2019. The TPX2Like protein 3 is the primary activator of alpha-Aurora kinases and essential for embryogenesis. Plant Physiology, 180:1389–1405. PMID: 31097675 DOI: 10.1104/pp.18.01515

              Lee, Y.-R.J. and B. Liu. 2019. Microtubule nucleation for the assembly of acentrosomal microtubule arrays in plant cells (Tansley Review). New Phytologist. 222:1705-1718. PMID: 30681146,

              Zhang*, H., X. Deng*, B. Sun, S. Van, Z. Kang, H. Lin, Y.J. Lee, and B. Liu. 2018. Role of the BUB3 protein in phragmoplast microtubule reorganization during cytokinesis. Nature Plants (article), 4:485–494 . PMID: 29967519. doi: 10.1038/s41477-018-0192-z.

              Tseng, K.-F., P. Wang, Y.-R. J. Lee, J. Bowen, A.M. Gicking, L. Guo, B. Liu, and W. Qiu. 2018. The preprophase band-associated kinesin-14 OsKCH2 is a processive minus-end-directed microtubule motor. Nature Communications, 9(1):1067. DOI: 10.1038/s41467-018-03480-w.

              Lee, Y.-R. J., Y. Hiwatashi, T. Hotta, T. Xie, J. Doonan, and B. Liu. 2017. The mitotic function of augmin is dependent on its microtubule-associated protein subunit EDE1 in Arabidopsis thaliana. Current Biology, Volume 27, Issue 24, 18 December 2017, Pages 3891–3897.e4. PMID: 29225022 DOI:

              Lv*, S., H. Miao*, M. Luo, Y. Li, Q. Wang, Y.-R. J. Lee, and B. Liu. 2017. CAPPI: a Cytoskeleton-based localization Assay reports Protein-Protein Interaction in living cells by fluorescence microscopy. Molecular Plant 10 (11), 1473–1476. PMID: 28939449 DOI: 10.1016/j.molp.2017.09.006, (Try CAPPI - it tells you unambiguously inside living cells whether two proteins interact with each other or not!)

              Li*, H., B. Sun*, M. Sasabe, X. Deng, Y. Machida, H. Lin, Y.-R.J. Lee, and B. Liu. 2017. Arabidopsis MAP65-4 plays a role in phragmoplast microtubule organization and marks the cortical cell division site. New Phytologist 215(1):187-201. PMID: 28370001 DOI: 10.1111/nph.14532.

              Citovsky, V., and B. Liu. 2017. Myosin-driven transport network in plants is functionally robust and distinctive. Proc Natl Acad Sci U S A. 2017 Feb 8. pii: 201700184. PMID: 28179563 PMCID: PMC5338398 DOI: 10.1073/pnas.1700184114.

              Lee, Y.-R. J., and B. Liu. Cytokinesis. 2016. In Plant Cell Biology, S. Assmann, B. Liu (eds.). Springer, New York. DOI 10.1007/978-1-4614-7881-2_9-1

              Zhang, B., G. Yang, Y. Chen, Y. Zhao, P. Gao, B. Liu, H. Wang, and Z.-L. Zheng. 2016. C-terminal domain (CTD) phosphatase links Rho GTPase signaling to Pol II CTD phosphorylation in Arabidopsis and yeast. Proc. Natl. Aca. Sci. USA. 113(50):E8197-E8206. PMID: 27911772 PMCID: PMC5167197

              Lee, Y.-R.J., W. Qiu, and B. Liu. 2015. Kinesin motors in plants: from subcellular dynamics to motility regulation. Curr Opin Plant Biol. 28:120-126. PMID: 26556761

              Kong, Z., M. Ioki, S. Braybrook, S. Li, R. Zhong, Z. Ye, Y.-R.J. Lee, T. Hotta, A. Chang, J. Tian, G. Wang, and B. Liu. 2015. Kinesin-4 functions in vesicular transport on cortical microtubules and regulates cell wall mechanics during cell elongation in plants. Molecular Plant. 8(7):1011-1023. PMID: 25600279

              Liu, T., J. Tian, G. Wang, Y. Yu, C. Wang, Y. Ma, X. Zhang, G. Xia, B. Liu, Z. Kong. 2014. Augmin triggers microtubule-dependent microtubule nucleation in interphase plant cells. Current Biology. 24:2708-2713.

              Zeng, C.T., H.R. Kim, I. Vargus Arispuro, J.-M. Kim, A.-C. Huang, and B. Liu. 2014. Contributions of microtubule plus end-tracking proteins to robust microtubule dynamics and sustained directional extension of hyphae in the filamentous fungus Aspergillus nidulans. Molecular Microbiology. 94:506-521.

              Lee, Y.-R.J. and B. Liu. 2013. The rise and fall of the phragmoplast microtubule array. Curr Opin Plant Biol. 16:757–763.

              Liu, B. 2013. Microtubule disassembly: when a sleeper is activated. Current Biology. 23: R932-933.

              Watch the video: Ημερίδα Μοριακή Βιολογία u0026 Γενετική - Επ. Παιδείας ΠΕΒ (June 2022).


  1. Jarman

    interestingly, but the analogue is?

  2. Mazugrel

    Let's Talk.

  3. Shakinos


  4. Lambrett

    You admit the mistake. We will consider.

  5. Fek

    A woman is the complete opposite of a dog. The dog understands everything, but cannot say anything ... Yesterday was standing, and you came today. Despite the fact that for several million years a woman has lived next to a person, there is still a lot of mysterious and incomprehensible in her behavior and lifestyle. An insane woman is a woman who, at the end of sexual intercourse, screams "Not into me !!!" What you sow - then you will find hell

Write a message