**2.1 Source of LMWOA**

Low-molecular-weight organic acids (LMWOAs) occur widely in soils and primarily originate from root exudation (Gao *et al.*, 2010; Ling *et al.*, 2009; Lu *et al.*, 2007; White *et al.*, 2003). Moreover, microorganisms, animals, and the degradation of organic matters can also produce LMWOA (Jones, 1998). People have noted the role of root exudates in the early 18th century, and then have a more profound understanding of the composition and the role of plant root exudates in latest 30 years. The composition and transportation of root exudates and its function in soil structure formation, soil mineral weathering, soil nutrient activation, promoting the nutrient absorption and rhizospheric soil nutrient movement and poison resistance (Al, acid) have been reported (Oburger *et al.*, 2009; Strom *et al.*, 2001; van Hees *et al.*, 2002; van Hees *et al.*, 2003). Root exudates are lubricant of the root - soil interface and microbial energy source, and they can improve the rhizosphere environment. They are the key materials for plants to adapt to nutritional stress (Strom *et al.*, 2002; Strom *et al.*, 2005) and environmental stress (do Nascimento *et al.*, 2006; Gao *et al.*, 2010; Guo *et al.*, 2007; Jones *et al.*, 2001; Liao *et al.*, 2006; Luo *et al.*, 2006; Toyama *et al.*, 2011; Zhu *et al.*, 2009). There are over 200 kinds of root exudates, including saccharides, organic acids, amino acids and allelochemicals, as well as small amount of fatty acids and steroids and trace growth substances and enzymes (Baudoin *et al.*, 2003; Muratova *et al.*, 2009). Their types and numbers will vary with the plant types and rhizosphere environment. Among them, the LMWOA are the main component of root exudates, which with one to several carboxyl and the most common LMWOAs identied in soils include citric acid, tartaric acid, maleic acid, malic acid, formic acid, acetic acid, oxalic acid, succinic acid, fumaric acid, propionic acid and so on (Baudoin *et al.*, 2003; Kpomblekou-A & Tabatabai, 2003). Most of them are the intermediate products of tricarboxylic acid cycle. In special cases, plant secretes specific organic acids, such as mugineic acid which is secreted by rice and wheat when they lack iron (Inoue *et al.*, 2009; Kobayashi *et al.*, 2010). Molecular structure and charged characteristics of LMWOA can affect many processes in the soil, some stresses of adversity factor can induce plant roots to secrete a large number of organic acids, and this is an adaptive reaction of plant to ecological environment (Inoue *et al.*, 2009; Kobayashi *et al.*, 2010).

The object of this article is to discusses the dynamic release behavior of several organochlorine pesticides like DDT isomer (DDTs) and HCH isomer (HCHs) with LMWOA from variable charge soil (red soil) with self-designed dynamics device, and provide some reference to the migration and fate of these kinds of substances and also the phytoremediation and ecological risk assess ment of organic pollutants in the

Their source include: root exudates, microorganisms, animals, and the degradation of organic matters. The LMWOA, as one of the typical rhizosphere active components presenting in soil and a key molecule in the solubilization of phosphorus-containing crystalline or amorphous minerals or as a desorbing agent for orthophosphate adsorbed on other soil minerals, is sure to have an influence on the existing state of organochlorine pesticides in soil and on the interaction between organochlorine pesticides and soil

Low-molecular-weight organic acids (LMWOAs) occur widely in soils and primarily originate from root exudation (Gao *et al.*, 2010; Ling *et al.*, 2009; Lu *et al.*, 2007; White *et al.*, 2003). Moreover, microorganisms, animals, and the degradation of organic matters can also produce LMWOA (Jones, 1998). People have noted the role of root exudates in the early 18th century, and then have a more profound understanding of the composition and the role of plant root exudates in latest 30 years. The composition and transportation of root exudates and its function in soil structure formation, soil mineral weathering, soil nutrient activation, promoting the nutrient absorption and rhizospheric soil nutrient movement and poison resistance (Al, acid) have been reported (Oburger *et al.*, 2009; Strom *et al.*, 2001; van Hees *et al.*, 2002; van Hees *et al.*, 2003). Root exudates are lubricant of the root - soil interface and microbial energy source, and they can improve the rhizosphere environment. They are the key materials for plants to adapt to nutritional stress (Strom *et al.*, 2002; Strom *et al.*, 2005) and environmental stress (do Nascimento *et al.*, 2006; Gao *et al.*, 2010; Guo *et al.*, 2007; Jones *et al.*, 2001; Liao *et al.*, 2006; Luo *et al.*, 2006; Toyama *et al.*, 2011; Zhu *et al.*, 2009). There are over 200 kinds of root exudates, including saccharides, organic acids, amino acids and allelochemicals, as well as small amount of fatty acids and steroids and trace growth substances and enzymes (Baudoin *et al.*, 2003; Muratova *et al.*, 2009). Their types and numbers will vary with the plant types and rhizosphere environment. Among them, the LMWOA are the main component of root exudates, which with one to several carboxyl and the most common LMWOAs identied in soils include citric acid, tartaric acid, maleic acid, malic acid, formic acid, acetic acid, oxalic acid, succinic acid, fumaric acid, propionic acid and so on (Baudoin *et al.*, 2003; Kpomblekou-A & Tabatabai, 2003). Most of them are the intermediate products of tricarboxylic acid cycle. In special cases, plant secretes specific organic acids, such as mugineic acid which is secreted by rice and wheat when they lack iron (Inoue *et al.*, 2009; Kobayashi *et al.*, 2010). Molecular structure and charged characteristics of LMWOA can affect many processes in the soil, some stresses of adversity factor can induce plant roots to secrete a large number of organic acids, and this is an adaptive reaction of plant to ecological environment (Inoue *et al.*, 2009;

environment .

colloids.

**2. Research advance**

**2.1 Source of LMWOA**

Kobayashi *et al.*, 2010).

#### **2.2 Residual characteristic of organochlorine pesticides in soil and rhizosphere**

Organochlorine pesticides (OCPs) could quickly be adsorbed or bound to the soil or soilorganic matter due to their high hydrophobicity and low water solubility after they were introduced into the soil. With time, the diminishes of these OCPs's bioavailability due to an "aging" effect and the formation of "bound-residues", which takes place during processes of decomposition and humication of organic matter (Alexander, 2000). The residual charactaristics of OCPs in rehizosphere and bulk soil maybe related to the properties of OCPs (water solubility, degradability, volatility,etc.), characteristic of soil mineral (the type and content of organic matter, diameter composition of soil mineral particle, content of oxidationreduction materials, moisture content, etc.), properties of plant (root system characteristic, kind and quantity of root exudates, lipoprotein content, specific surface, etc.) (Alexander, 2000; Calvelo Pereira *et al.*, 2006; Chen *et al.*, 2007; Gonzalez *et al.*, 2005; Inui *et al.*, 2008; Mikes *et al.*, 2009; Mo *et al.*, 2008; Skaates *et al.*, 2005; White *et al.*, 2002; Yang *et al.*, 2008; Yao *et al.*, 2007). For the OCPs with high volatility and relative easy degradation, the concentrations in rehizosphere are generally lower than that in bulk soil, and the results generally appear in the indoor simulation experments. For the OCPs with low volatility and relative harddegradation, the concentrations in rehizosphere are generally higher than that in bulk soil, and the results generally appear in the field experments. The results of pot experiment maybe also differ with field experiment for the same compounds. This kind of contradictory results is mainly due to the pollution sources can be repeatedly inputted by irrigation and dry and wet deposition in field, pollutants would be enrich in rehizosphere with water flow; and the pollution source is single input in indoor simulation experiment, plant absorption or biodegradation result in the concentration of OCPs in rhizosphere soil is lower then in the bulk soil.

For example, Chen et al. (2007) showed that the measured DDXs in the rhizosphere soils were significantly higher than those in the bulk soils. p,p'-DDT, p,p'-DDD, and p,p'-DDE in the soil accounted for 38%, 47% and 15% of the total. For total DDXs, approximately one third remained on the outer surface of the roots. The partition of DDXs between rhizosphere soil and root surface depended on contaminant affinity to soil organic matter, soil organic matter content and root specific area (Chen *et al.*, 2007), Calvelo Pereira et al. (2006) reported that the roots of *Avena sativa L., Chenopodium spp., Solanum nigrum L., Cytisus striatus (Hill) Roth, and Vicia sativa L.* tended to reduce levels of the HCH isomers in the rhizosphere (Calvelo Pereira *et al.*, 2006),white et al. (2002) found that the chlordane concentration in the rhizosphere (soil attached to roots) was signicantly less than that in the bulk soil. However, the enantiomeric ratio of the chiral components and overall component ratios had changed little in the rhizosphere relative to the bulk soil. (White *et al.*, 2002).

#### **2.3 Interaction mechanism between LMWOA and pesticides in soil**

LMWOAs have been shown to disrupt the sequestering soil matrix, thereby enhancing the desorption of organic pollutants in soil (White *et al.*, 2003). Consequently, it is expected that LMWOAs, in theory, may affect OCPs availability in soil environment. However, to date, few research has been conducted in this area, and there is limited information on the availability and sorption–desorption behaviour of OCPs from natural soils by organic acids. Gonzalez et al. (2010) showed that sodium citrate and oxalate, at levels usually exuded by plant roots, effectively enhanced desorption of p,p'-DDT, p,p'-DDE and α-cypermethrin, while

no effects were observed for α-endosulfan and endosulfan sulfate, the non-ionic surfactant

Effects of Low-Molecular-Weight-Organic-Acids on the

p,p'-DDT 1.2~5.51) 6.2~6.91

p,p′-DDE 65 5.69~6.96

o,p′-DDT 1.2~5.5 6.76

o,p′-DDE 65 6.94

1) Solubility in water at 24~25℃, µg/L

2000)

a teflon filter disc.

**3.1.2 Chromatogram conditions**

**3.1.3 Dynamic experiment methods**

Release Kinetic of Organochlorine Pesticides from Red Soil 523

container before use); Organochlorine pesticides standard (o, p'-DDT, o, p'–DDE, p, p'-DDT, p, p'–DDD, p, p'-DDE, α-, β-, γ- and δ-HCH)were purchased from Dr. Ehrenstorfer

DDTs solubility log*K*ow HCHs solubility log*K*ow

Table 1. Some properties of the organochlorine pesticides (Chiou et al., 1987; Harald et al.,

Institute of National Chromatogram Center in Dalian, P.R.China).

calculation was conducted with external standard method.

Agilent-6890 GC/ECD gas chromatography and HP7683 automatic sampler with HP chemical workstation (Hewlet-Packard, USA) and HP-5 fused capillary column (30 m ×320 μm ×0.25μm) as chromatographic column; Sigma 2-16K high speed freezing centrifuge (Sigma, Germany); water bath rotary vacuum evaporator (Yarong Biochemical Instrument Plant, Shanghai, P.R.China); HS-10360D ultrasonic cleaning machine (Heng'ao Science and Technology Company, Tianjin, P.R.China); BS200S-WE1 electronic balance (1/10000, Sartorius Company, Germany); SPP cartridge and filter disc (Chemical and Physical

Celite 545 (chromatographic grade, 0.020-0.045 mm, Serva Company) was dried in muffle furnace at 550 °C for 4 h, then treated at 200 °C in oven before addition of 3 % of deionized water to deactivate it, and stored in a sealed container before use; Purified SPE column was prepared by packing with a teflon filter disc + 1g celite 545 + 1g anhydrous sodium sulfate +

Initial oven temperature was kept at 60 °C for 1 min. Then the temperature was increased to 140 °C at a speed of 20 °C min-1 and subsequently increased to 280 °C at a speed of 12 °C min-1 and kept at 280 °C for 4 min. The injector temperature was set as 220 °C, while the detector temperature was set as 280 °C. High purity N2 (99.999%) was used as carrier gas at a flow rate of 2 mL min-1. 2 µL of sample was injected in a splitless mode. Quantitative

**Soil sample** The red soil, Hydragric Acrisols - according to WRB (World Reference Base for soil resources) (ISSS/ISRIC/FAO, 1998), was sampled at depths of 5 cm to 20 cm from the Red Soil Ecologic Experimental Station of Chinese Academy of Science in Yingtan, Jiangxi province, P. R. China (28°12′34.1″N, 116°55′32.3″E), lyophilized and sieved (≤1 mm) for further analysis. The clay minerals of soil sample were mainly composed of kaolinite and hydroxyaluminum vermiculite, and contained a spot of hydromica and a trace amount of gibbsite. The main physical and chemical characteristics of the soil were as follows: pH 5.07, clay content 32.3%, organic matter content 1.14 %, Cation Exchange Capacity (CEC) 10.10 Cmol (+) kg-1. **Spiked soil** Soil samples were sieved to <2mm and stored at room temperature until spiking procedure. Spiked soil samples were prepared by adding 500 µL of standard mixture of 13 kinds of OCPs (the concentration is 10 ng µL−1 for each compound dissolved

α

β

γ

δ

*-*HCH 10 3.8

Company, Germany. The characteristics of pesticides studied are listed in table 1.

Tween 80 behaved similarly to the acids, whereas the anionic sodium dodecyl sulfate enhanced desorption of all pesticides (Gonzalez et al., 2010). Luo et al. (2006) reported that soil organic carbon played a predominant role in the retention of DDT. Oxalate significantly increased the desorption of p,p'-DDT, with the largest increments ranging from 11% to 54% for different soils. Oxalate addition also resulted in the increased release of dissolved organic carbon and inorganic ions from soils. Root exudates had similar effects to those of oxalate and significantly increased DDT desorption from the soils. So, Low molecular weight dissolved organic carbon amendments caused partial dissolution of the soil structure, such as the organo-mineral linkages, resulting in the release of organic carbon and metal ions and thus the subsequent enhanced desorption of DDT from the soils (Luo *et al.*, 2006).

We can put forward some mechanisms about the LMWOA or root exudates with OCPs desorption: (1) Mechanism of minerals dissolution. LMWOA induced the dissolution of soil minerals and resulted in the desorption of OCPs adsorbed by soil minerals; (2) Mechanism of indirect release. Soil inherent organic matter is dissolved and released by LMWOA and resulted in the desorption of OCPs adsorbed by soil inherent organic matter. (3) Mechanism of direct release. The LMWOA directly chelated with OCPs and released it. (4) Mechanism of locking and unlocking. LMWOA interacted with soil inherent organic matter like a key and induced the change of its conformation and properties, resulted in the OCPs were locked or unlocked by soil inherent organic matter (Figure 1).

Fig. 1. Mechanism obout the stimulating release of OCPs by LMWOA

#### **3. Release kinetic of organochlorine pesticides from soil in LMWOA system**


**3. Materials and methods**

The experiment was established in April 2009 at the State University of Ponta Grossa Farm (Fazenda Escola Capão da Onça) in the city of Ponta Grossa, Paraná, in southern Brazil (25° 05′ S and 50° 05′ W). The climate is classified as Cfb according to the Köppen system [19], with cold and humid winters and occasional frosts between May and July. The annual mean precipitation during the experimental period of 44 years is 1545 mm, with higher precipitation levels in the summer and no dry period defined. The mean maximum temperature is 24°C, and the minimum is 13.3°C. The soil is classified as Haplic Cambisol with medium texture, and it represents 27% of the region [35]. The results of the soil fertility analysis performed before the experiment were pH (CaCl2, 1M), cation exchange capacity = 11.2 cmolc dm−3, soil density = 1.35 Mg m−3, total organic C = 11.9 g kg−1, total organic nitrogen = 16.12 g kg−1, available P = 38.1 mg kg−1 and available K = 0.24 cmolc dm−3.

The experimental design was of completely randomized blocks with six treatments and three replications. The following treatments were performed: T1 = control, with no slaughterhouse organic residue (SOR) or synthetic mineral fertilizer (SMF) applications; T2 = 100% SMF, with all plant nutrient supply applied via synthetic mineral fertilizer; T3 = 100% SOR, with all plant nutrient supply applied via slaughterhouse organic residue; T4 = 75% SOR + 25% SMF; T5 = 50% SOR + 50% SMF; and T6 = 25% SOR + 75% SMF. For T4, T5 and T6, the rates of SORs applied were equivalent to 75%, 50% and 25% of the residues used in T2, respectively. The rates of SMFs applied were based on the soil fertility analysis and the recommendation for the crops used in the region.

In T3 (100% SOR), 94, 21 and 19 kg ha−1 of N, P and K, respectively, were applied. These were equivalent to 2 Mg ha−1 bio‐fertilizer. The crop sequence from 2009 to 2012 was the alternation of crops used in the summer and winter seasons in the region.

Crop yield was determined by harvesting 5 m of the three central rows in the summer and winter season crops. The grains were submitted to a cleaning process to remove impurities and then dried for humidity correction. Grain weight was corrected to 14% humidity for beans and 13% for the other crops. The unit was converted to kg ha−1 and then to Mg ha−1. For black oat, we determined the dry mass production collecting two points with 0.17 m2 at each plot. The soil total organic carbon (TOC) content was determined through the dry combustion method using an elementary C/N analyzer (TruSpec CN LECO® 2006, St. Joseph, EUA).

The results for content and the TOC stock in whole samples, the particle‐size fractions and the SOM labile compartments were subjected to analysis of variance (ANOVA). The means that were significantly different from the *F* test were compared using the LSD test at 5% probability (*I* = 0.05) using SISVAR 5.1 [36].


**About the Editors**

**Samuele Segoni** graduated in Geological Sciences at the University of Firenze, Italy, and in 2008, he obtained a PhD in Earth Sciences, also at the University of Firenze. Since 2005, he has been carrying out research and teaching activities at the Department of Earth Sciences of the University of Firenze, where he is currently enrolled as Assistant Professor. Since 2019, he has been Chair Associate at the UNESCO Chair on the Prevention and Sustainable Management of Geo-hydrological Hazards. His main research topics are: the prediction and mapping of landslide hazards; application of physically based models for the triggering of shallow landslides at regional scale; landslide susceptibility assessment, statistical rainfall thresholds for landslide triggering, regional scale landslide early warning systems; civil protection, land planning, landslide risk assessment. Within these topics, he authored more than 50 peer-reviewed articles in international journals, and he is the member of Editorial Boards of various international journals.

**Stefano Luigi Gariano**, Ph.D., is a research scientist at the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) in Perugia, Italy. He graduated in Environmental Engineering from the University of Calabria, Italy, and achieved a Ph.D. in Geology with a thesis on the impact of climate change on landslides from the University of Perugia, Italy. His main research topics include: (i) analysis of rainfall time series and of series of landslide occurrences; (ii) objective and reproducible reconstruction of rainfall conditions responsible for landslide occurrence; (iii) definition and validation of empirical rainfall thresholds for the possible initiation of landslides; (iv) evaluation of the impact of climate and environmental changes on landslides; (v) national and regional early warning systems for rainfall-induced landslides, in different climatic and physiographic environments. He is involved in several projects, mostly focused on the operational prediction of rainfall-induced landslides. He has authored more than 100 publications, including more than 40 peer-reviewed articles in international journals. He is organizing and convening scientific sessions on landslide early warning systems and on climate change effects on landslides at various international conferences. He is a founding member of "LandAware", the international network on Landslide Early Warning Systems and he is an Alternative Board Member for CNR IRPI at the International Consortium on Landslides.

**Ascanio Rosi** is currently working as a fixed-term research scientist at the Department of Earth Sciences of the University of Florence, where he obtained his PhD in Earth Sciences in 2013, with a thesis on the use of satellite radar data for subsidence and landslide mapping. From 2019, he has been a Chair Associate at the UNESCO Chair on Prevention and Sustainable Management of Geo-hydrological Hazards. His main research topics are the use of new technologies to create landslide databases; the identification of rainfall condition associated with the triggering of landslides; the use of artificial intelligence techniques to identify the geological and meteorological condition associated with the initiation of landslides; the development of landslide early warning system based on empirical and statistical analyses and landslide risk assessment. From 2012, he authored and co-authored over 20 articles in international journals; he also served as a reviewer and an editor for several international journals.


**5. HIV/HCV-coinfected immunological nonresponders**

A current concern is that as many as 30% of treated HIV-infected patients experience poor CD4+ T-cell restoration despite prolonged suppression of viral replication during ART (discordant immune response). These "immune nonresponders" (INRs) contrast with "immune responders" who recover their CD4+ T-cells to the normal range (>500/μL) while being treated. The discordant immune response is linked to an additional risk of mortality and non-AIDS-defining morbidities [153]. Still, the mechanisms responsible for insufficient CD4+ T-cell recovery during ART have not been fully discerned.

Our own studies have provided some additional information on the negative impact of hepatitis C on the natural course of an HIV infection. In our Russian cohort, we showed that HCV coinfection is a sufficient risk factor for the formation of a discordant CD4+ T-lymphocyte response to ART [154]. At the same time, we found that in coinfected patients when compared with patients monoinfected with HIV the increase in systemic inflammation was associated with liver damage and destruction of the hepatic barrier that protects against microbial products coming from the intestine [155]. INRs have a higher degree of hepatic tissue destruction and express more sclerosing processes [156]. We also found that HIV/HCV-coinfected INR subjects are characterized not only by a deep CD4+ T-lymphocyte deficiency but also by decreased IL-2 production. Earlier, it was demonstrated [157] that through the secretion of this cytokine, T-cells can stimulate the natural killer cell's antifibrotic activity (realized through the killing of liver stellate cells). Thus, in HIV/HCV-coinfected INR patients, the IL-2 deficiency may be regarded as a liver fibrosis-accelerating factor.

In summary, it can be concluded that HIV/HCV-coinfected INRs are characterized by both a CD4+ T-lymphocyte deficiency and reduced IL-2 production which leads to liver destruction and cirrhosis formation. Under such conditions increased intestinal permeability (due to HIV infection) is supplemented by destruction of the hepatic barrier, accompanied by the entry of microbial products into the bloodstream. As a result, despite the ART-mediated HIV replication suppression, a pronounced systemic inflammation develops, leading to non-AIDSdefining diseases. Therefore, in coinfection, not only HIV infection therapy but also hepatitis С treatment should be actively carried out.


**4. Future directions: translational genomics**

The wheat genome is very complex as it is a polyploid species consisted of three diploid progenitor genomes. Recent advances in genome sequencing technologies have accelerated efforts to complete genomes of many crop species, which has opened the door for discovery and knowledge of the genetic basis of a number of important agronomic traits, with the final

**Species**

**Trait** Flowering

Early

At1g28380 At5g44040

HM133570

43.85

XM\_015777033

37.49

KJ711537

43.1

KP202720.1

42.53

26 Wheat Improvement, Management and Utilization

KR706151

42.43

XM\_015770121

36.86

KX161741.1

44.78

EU241899.1

41.78

flowering

Late

At1g01580 At1g04400

AB476614.1

43.88

XM\_015756636

36.72

EU331897.1

44.13

XM\_015787003

38.04

KJ711539.1

43.16

flowering

Growth

Dwarf/small

At5g19530 At1g02730 At4g10180

Growth

At4g01690 At1g65260

Stems

Waxy

At1g09560 At1g02205

> Fasciation

At1g64670

AJ567377.2 AK354338.1 AK356998.1

> Short petiole

> At4g10180

At3g03860

Twisted

At4g27060

petiole

Leaves Abnormal

At3g16830 At1g61940

shape

AK353983.1

37.12

GQ389628.1

EF190873.1

JX828333.1

58.57

AY831792.1

JX878122.1

58.41

56.52

58.4

EU189093.1

54.23

58.62

DQ457416.2

54.2

AK360706.1

36.39

AY585350.1

54.03

M11336.1

55.81

59.25

57.95

EU981914.2

60.39

60.29

FJ501983.1

59.64

EU981913.1

59.82

AK360068.1

58.91

FJ487949.1

58.86

KU376267.1

58.98

AK366020.1

58.91

FJ487950.1

58.98

KU376264.1

58.75

defective

KT247893.1

42.47 At1g02910 JF965395.1

EU331690.1

43.14

AY747605.1

43.1

KR816810.1 KT750252.1 AY244509.2

42.79

42.97

42.82

42.96

AY750996.1

42

AB630963.1

43.05

AY747606.1

43.1

**Locus**

**Locus**

**Identity**

**Locus**

**Identity**

**Locus**

**Identity**

**Locus**

**Identity**

**Arabidopsis**

**Barley**

**Rice**

**Wheat**

**Maize**

**Table 1.** Comparative analysis of yield-related genes in Arabidopsis, wheat, barley, and maize. aim of crop improvement and production. Moreover, the completion of sequences has enabled assessment of translational genomics which is an effective way for researchers and breeders to transfer knowledge of genetic and genomic information among related species, such as rice and wheat [110]. The translational genomics tool is known as 'model to crop' translation that can be contributed to the implementation of genetic and genomics in crop species [111]. There are three well-characterized model grass species rice [112, 113], *Brachypodium* [114], and barley [115], which can be used to accelerate the application of translational genomics among the Poaceae family. The large amount of insufficient knowledge such as biological processes that are controlled by genes can be filled from well-studied species through translational biology approach [110]. In translational genomics, comparative genomics studies can take advantage from available genomes and can provide information on the extrapolation of knowledge of gene functions among species [110]. Genome sequences are currently available for wheat, but information QTL mapping as compared to other model grass species have been less reported. Comparative analysis based on the candidate gene approach (CGA) is known as the most powerful tool for exchange information among species. We conducted a brief comparative analysis of yield-related genes in Arabidopsis, barley, rice, wheat, and maize. The 37 Arabidopsis genes controlling flowering, growth, stems, leaves, flowers, and fruits, have been used to investigate homologous sequences in the barley, rice, wheat, and maize genomes using BLAST [116]. Using the detected homologs among the grass species, we found a number of homologous sequences with the sequence identity values ranging between 36.39 and 60.39 based on MUSCLE v3.51 [117] (**Table 1**).

The release of genomic sequence of wheat [11], barley [115], and maize [118], provides a new opportunity for translational genomics. Since comparative genomics focuses on comparing genomes among plant species looking for similarities and differences of DNA sequence, protein sequence, and gene orders, information from well-studied and analyzed species can be applied for less studied crops to improve a specific target trait, which can be implemented in crop breeding and improvement. For example, genome-wide comparative analysis of floweringrelated genes in Arabidopsis, wheat, and barley has revealed that there are 900 and 275 putative orthologs in wheat and barley, respectively [119]. In addition, they showed many orthologous genes having similar expression profiles in different tissues of wheat and barley based on their *in silico* expression analyses. Such a work will help researchers to investigate candidate genes controlling the time of flowering in rice and barley which can be incorporated into molecular breeding for early flowering in wheat and barley in short-season cropping region [119].


Temporal strategies, such as circadian clocks (Wolf and Arkin, 2003), anticipatory control (Mitchell et al., 2009; Katz and Springer, 2016), energy and resource storage (Schulz et al., 1999; Grover, 2011), and dormancy (Lewis, 2010) are hallmarks of biology, yet they are often not given much consideration when theory and models are developed for understanding biogeochemistry, even though temporal strategies have also been observed in microbial communities (Ottesen et al., 2014). Here our modelresults indicate that different organizational timescales can dramatically impact biogeochemistry and how microbial communities function. For instance, the SIO simulation does not invest resources in phytoplankton growth, because over the short 0.25 day optimization, water dissipates more electromagnetic potential than a small increase in phytoplankton biomass over the short interval. Instead, the SIO solution allocates resources to growth of green sulfur bacteria (GSB) and photoheterotrophs (PH) deeper in the water column to dissipate light not adsorbed by water in the surface. The SIO solution also places more resources on decomposing refractory carbon, but also on respiring the liberated labile carbon. Grazing rates, especially under aerobic conditions, are higher in the SIO simulations as well. These types of resource allocations in the SIO solution appear more consistent with R∗ or resource-ratio theory (Tilman, 1982) and r-selection (Pianka, 1970; Fierer et al., 2007); that is, emphasis on fast growth. On the contrary, the LIO solution appears more similar to K-selection where resources are invested for longer term outcomes. These differences are also evident in the control variables. In the SIO simulations, the control variables show rapid bang-bang control fluctuations as resources vary (**Figure 8D**), while the LIO solution produces more gradual changes in control variables (**Figure 8I**). A microbial community that implements temporal strategies should outcompete a community that lacks temporal strategies, because long-term strategies result in greater acquisition and utilization of food resources under non steady-state conditions than short-term strategies (Cole et al., 2015), which is evident by the 71% greater entropy production over the 6 day simulations using LIO versus SIO (36.5 GJ K−1 vs. 21.3 GJ K−1 ).

One of the main questions that arise in applying MEP is what is the appropriate timescale over which biology organizes? That is, in the current implementation of the model, what is an appropriate value for △t used in the optimal control problem? In this study two values were explored, 0.25 days for the SIO and 3 days for the LIO. These choices were based on the observation time scale, where 0.25 day is "short" compared to 1 day, and 3 days is "long" in comparison to 1 day. Our results indicate that the SIO solution did not match observations or expectations as well as the LIO solution (**Table 3**). Comparing MEP model output to observations for differing values of △t is one means to explore this fundamental property of ecosystems. But is a 3 d optimization window sufficient? It seems the appropriate MEP time scales do not depend on just the characteristic time scales of organisms, because our study on periodically forced methanotroph communities showed that the communities were well adapted to 20 day cyclic inputs of energy, even though the characteristic turnover time of bacteria is closer to hours (Vallino et al., 2014; Fernandez-Gonzalez et al., 2016). We know that terrestrial systems function on long timescales, at least on the order of the four seasons, but their time scales are likely much

longer given that individual trees can live hundreds of years, and forest succession takes longer than that (Odum, 1969; Finegan, 1984). At this time, we do not have an answer to the question regarding an appropriate optimization time scale for microbial communities, but it is likely related to the time scales over which biological predictions, acquired by evolution, can be reliably made, such as the sun will return tomorrow, or winter is coming. Predicting the future has obvious evolutionary advantages, but it also results in greater dissipation of free energy and entropy production, as evident by the higher entropy produced by the LIO solution. We believe that determining the temporal scales over which microbial communities operate will be important for developing predictive biogeochemistry models, but space scales are also import.

Strategies that coordinate function over space also lead to greater free energy dissipation (Vallino, 2011).  Our original intent was to compare local versus global MEP optimization, but global optimization in the Siders Pond model did not produce biologically relevant results due to the speed at which electromagnetic potential is dissipated abiotically. Because water and sediments rapidly absorb light, any water column of sufficient depth will dissipate all incoming radiation as entropy regardless of the presence of organisms, so an infinite number of solutions exist that all produce the same global entropy production. However, maximizing local entropy production at discrete depths, as was conducted in this study, does select for a unique solution, which our results show is biologically relevant. Our previous study with purely chemical reactions showed that global entropy production results in greater chemical potential destruction (Vallino, 2011). This study indicates that if abiotic processes can destroy an energy potential faster than biotic ones, then local MEP optimization will be the preferred solution. Global maximization of entropy production is more likely to be found in

systems where energy potentials are stable with respect to abiotic decay, such as chemical potentials. However, it seems possible that both local and global optimization could be operating simultaneously in systems given that both light and chemical potentials often exist, so further studies are needed.

Other areas that could improve the MEP modeling approach are as follows. Our metabolic network (**Figure 1**) was largely based on prior knowledge about Siders Pond biogeochemistry, but condensing genome-scale models may also be a means to construct more realistic whole community metabolic networks from genomic surveys (Hanson et al., 2014; Hanemaaijer et al., 2015), and exometabolomics could be used to identify metabolite nodes in the distributed metabolic network that are widely exchanged between functional groups (Klitgord and Segre, 2010; Baran et al., 2015; Fiore et al., 2015; Ponomarova and Patil, 2015). Also, our current approach relies on non-linear optimal control to locate MEP solutions, but this is a computationally intensive problem, and falls under the class of problems known as control of partial differential equations (Coron, 2007). Building on expertise from that field could reduce computation requirements, especially for problems involving two or three spatial dimensions, but it might be possible to avoid the formal optimization problem altogether. One possibility would be to use Darwinian inspired trait-based modeling approaches (Follows et al., 2007; Coles et al.,

(D,H) for the SIO (A–D) and LIO (D–H) simulations.

2017), which is how biology actually solves the MEP problem. On the other hand, MEP could be useful for developing trait models that improve entropy production over time and space, such as traits that include resource storage, time delays (i.e., dormancy), migration across fronts and boundaries to acquire resources, clocks and oscillators, distribution and packaging of metabolic function, predation, remineralization, and other regulatory motifs (Wolf and Arkin, 2003). By placing emphasis on the mechanisms of how energy potentials are destroyed over time and space, rather than on the peculiarities of how organisms grow and survive, can lead to new insights that can improve our understanding of biogeochemistry and model predictions thereof.

### CONCLUSIONS

Our results demonstrate that models based on MEP can reasonably simulate how microbial communities organize and function in Siders Pond over time and space while using a minimum of adjustable parameters. The improved qualitative and quantitative agreement between model predictions and observations using long (3 day, LIO) versus short (0.25 day, SIO) interval optimization supports the hypothesis that biological systems maximize entropy production over long time scales. The modeling presented here extends the MEP approach to include an explicit spatial dimension, and new metabolic reactions were introduced to model phototrophs and entropy production associated with the destruction of electromagnetic potential. By considering the dissipation of both chemical and electromagnetic potentials, the MEP model shows that heterotrophs, such as bacteria, dissipate far more free energy in the photic zone by passive light absorption than by chemical respiration. Short interval optimization results in higher grazing rates and turnover of organic carbon, as well as rapid (bang-bang) changes in the reaction control variables, while long interval optimization supports higher phytoplankton growth and standing stocks near the surface of the pond. We also found that maximization of local entropy production, as opposed to global entropy production, must be used for energy potentials that are quickly dissipated by abiotic processes, such as light absorption by water and particles. Taken together, results validate our general conjecture that biological systems evolve and organize to maximize entropy production over the greatest possible spatial and temporal scales, while abiotic processes maximize instantaneous and local entropy production.

### AUTHOR CONTRIBUTIONS

Both JV and JH contributed equally to project concepts and design, and both conducted the 24 h sampling of Siders Pond. Preliminary results from metatranscriptomics and metagenomics from JH were used to improve development of the metabolic network. JV developed the model and wrote first draft of the manuscript with editing and inputs during the process from JH. Both authors contributed to manuscript revision, read and approved the submitted version.

### FUNDING

Primary funding for this project was from NSF GG grant EAR-1451356 to JV and JH, with additional support from Gordon and Betty Moore Foundation grant GBMF 3297. JV also received support from NSF Grants OCE-1637630 and OCE-1558710 and Simons Foundation grant 549941. The NSF Center for Dark Energy Biosphere Investigations (C-DEBI; OCE-0939564) also

### REFERENCES

supported the participation of JH. This is C-DEBI contribution number 441.

### ACKNOWLEDGMENTS

We thank Petra Byl for assistance in field work preparation and sampling of Siders Pond, as well as insightful conversations about the project. We also thank Jane Tucker for dissolved inorganic carbon analyses, Suzanne Thomas for hydrogen sulfide analyses, Sam Kelsey for dissolved organic carbon analyses, Kate Morkeski for sulfate, phosphate and particulate organic carbon analyses, and Rich McHorney, Leslie Murphy, Emily Reddington, and Gretta Serres for assistance with Siders Pond sampling logistics. We also thank the Semester in Environmental Science Program at MBL for use of their Hydrolab and Jon Boat used for sampling, and a special thank you to Tom Gregg who allowed us to access Siders Pond through his property as well as for providing logistical support.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs. 2018.00100/full#supplementary-material

Finegan, B. (1984). Forest succession. Nature 312, 109–114. doi: 10.1038/312109a0

rates. Geochim. Cosmochim. Acta 90, 96–109. doi: 10.1016/j.gca.2012.0 5.011

Odum, E. P. (1969). The strategy of ecosystem development. Science 164, 262–270.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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