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Abiotic Stresses Cause Differential Regulation of Alternative Splice Forms of GATA Transcription Factor in Rice

The GATA gene family is one of the most conserved families of transcription factors, playing a significant role in different aspects of cellular processes, in organisms ranging from fungi to angiosperms. GATA transcription factors are DNA-binding proteins, having a class IV zinc-finger motif CX2CX17−20CX2C followed by a highly basic region and are known to bind a consensus sequence WGATAR. In plants, GATAs are known to be involved in light-dependent gene regulation and nitrate assimilation. However, a comprehensive analysis of these GATA gene members has not yet been highlighted in rice when subjected to environmental stresses. In this study, we present an overview of the GATA gene family in rice (OsGATA) in terms of, their chromosomal distribution, domain architecture, and phylogeny. Our study has revealed the presence of 28 genes, encoding 35 putative GATA transcription factors belonging to seven subfamilies in the rice genome. Transcript abundance analysis in contrasting genotypes of rice—IR64 (salt sensitive) and Pokkali (salt tolerant), for individual GATA members indicated their differential expression in response to various abiotic stresses such as salinity, drought, and exogenous ABA. One of the members of subfamily VII—OsGATA23a, emerged as a multi-stress responsive transcription factor giving elevated expression levels in response to salinity and drought. ABA also induces expression of OsGATA23a by 35 and 55-folds in IR64 and Pokkali respectively. However, OsGATA23b, an alternative splice variant of OsGATA23 did not respond to above-mentioned stresses. Developmental regulation of the OsGATA genes based on a publicly available microarray database showed distinct expression patterns for most of the GATA members throughout different stages of rice development. Altogether, our results suggest inherent roles of diverse OsGATA factors in abiotic stress signaling and also throw some light on the tight regulation of the spliced variants of OsGATA genes in response to different environmental conditions.

INTRODUCTION
Genetic material present in all living system as DNA eventually encodes and governs almost all the fundamental processes in live forms. Selective upregulation and downregulation of the set of genes encoded by DNA, allows an organism to respond to distinct stimuli. Transcription factors (TFs) act as synchronizing elements between action (stimuli) and reaction (gene expression). Plants being sessile, require more efficiently regulated gene expression to cope with the plethora of environmental stresses. TFs are one such key regulators governing gene expression by specifically binding to the promoter/enhancer sequences of the gene. TFs can be grouped into different categories based on the ability to bind the cis-acting elements in the promoter region. These transcription factors have so far been named as MADS, WRKY, MYB, bZIP (basic leucine zipper), PHD (plant homeodomain), zinc-finger, NAC (NAM, ATAF1/2, and CUC1/2), and AP2/EREBP (Apetala2/ethylene responsive element binding protein), depending upon the presence of DNA binding motifs (Shore and Sharrocks, 1995;Krishna et al., 2003;Mizoi et al., 2012;Alves et al., 2013;Ambawat et al., 2013;Sun et al., 2013). Some of these TFs are specific only to plants viz. NAC and AP2/EREBP (Mizoi et al., 2012;Shao et al., 2015). Extensive studies have been carried out to understand the role of these transcription factors in biotic and abiotic stress as well as in crosstalk between these stresses in several crop plants (Cheong et al., 2002;Pandey and Somssich, 2009;Chen et al., 2010;Lindemose et al., 2013;Gupta et al., 2016;Nutan et al., 2017).
Based on the conserved domain structure, members of the zinc-finger TFs are further classified into different families (Takatsuji, 1998). GATA transcription factors, as the name suggest, are characterized by their ability to bind W-G-A-T-A-R (W = T/A, R = G/A) sequence in the promoter region (Merika and Orkin, 1993). These are type-IV zinc-finger motif with the consensus CX 2 CX 17−20 CX 2 C sequence followed by a basic region facilitating DNA binding. GATA zinc-finger with 17-18 residues in the binding loop is a characteristic feature of animal and fungal GATA TFs. While, the plant GATA factors possess 17-20 residues in the zinc-finger loop (Reyes et al., 2004;Behringer and Schwechheimer, 2015). The DNA binding domain of these GATA transcription factors have been well-studied by NMR structures in chicken GATA1 and AreA zinc-finger protein of Aspergillus nidulans (Omichinski et al., 1993;Starich et al., 1998). Based on their studies, it is now known that the interaction between zinc-finger loop and specific DNA element is facilitated by hydrophobic interactions with the nitrogenous bases present in the major groove of the DNA.
The role of these GATA transcription factors have been widely studied in fungi and animals (Tsai et al., 1994;Marzluf, 1997;Scazzocchio, 2000;Tong et al., 2000;Marzluf, 2004;Pikkarainen et al., 2004). First identified in chicken, the GATA TFs have been reported to be involved in haematopoiesis (Omichinski et al., 1993). Apart from their active involvement in cell differentiation, GATA TFs are also documented to be involved in regulation of various stress signaling and metabolic pathways (Crespo et al., 2001;Xu and Kim, 2012). Fungal GATA TFs are the combination of both plant and animal GATA transcription factors in terms of the amino acid residues present in the zinc-finger loop. Diverse roles governed by GATA transcription factors in fungal cells include controlling nitrogen metabolism, circadian regulation, and siderophore production (Teakle and Kay, 1995;Arguello-Astorga and Herrera-Estrella, 1998;Haas et al., 1999;García-Salcedo et al., 2006;Chi et al., 2013).
In plants, GATA TFs have not yet been studied extensively and the knowledge about this class of transcription factors remains elusive. The first plant GATA transcription factor NTL1 identified from tobacco is a homolog of NIT-2 from Neurospora crassa that functions in nitrogen metabolism (Daniel-Vedele and Caboche, 1993). Recent studies carried out in other plants have revealed the involvement of GATA transcription factors in regulation of various stress responsive genes, nitrogen metabolism, flowering, developmental related genes, and in hormone signaling such as GA, auxin, and cytokinin (Richter et al., 2010(Richter et al., , 2013Chiang et al., 2012;Hudson et al., 2013;Behringer et al., 2014;Behringer and Schwechheimer, 2015;Zhang et al., 2015). Furthermore, active involvement of Arabidopsis GATA TFs in the prevention of photooxidative damage via tetrapyrrole biosynthesis (TPB) has been documented very well (Kobayashi and Masuda, 2016). Arabidopsis class-B GATA TFs with C-terminal LLM domain, have been widely studied and characterized as regulator of vegetative growth and development (Behringer et al., 2014). In an interesting finding by Kobayashi et al. (2017), it has been observed that GNC-LIKE (GNL-class B GATA) TF functioned downstream to type B ARRs in Arabidopsis and hence interplayed at the junction of auxin and cytokinin signaling.
With the progression of genomic tools and availability of huge genomic data, studies related to whole genome mining have become more precise and informative. In rice, 28 gene loci encoding GATA proteins have been reported more than a decade ago (Reyes et al., 2004). Since the database is evolving continuously and with the availability of refined tools, we have carried out the search for GATA family members again to identify the new proteins. In the present work, we report 28 rice loci encoding 35 putative GATA transcription factors. Taking alternative splice variants into consideration along with gene structure, number and position of GATA domain as well as the presence of accessory domains other than GATA, we have categorized them into seven subfamilies. Further, a detailed analysis of the conserved GATA domain, sequence similarity between the genes, and the phylogenetic relationship has been performed. To investigate the role of GATA transcription factors in abiotic stress signaling, we have carried out expression analysis of OsGATA genes in two contrasting rice genotypes i.e., salt sensitive variety-IR64 and salt tolerant landrace-Pokkali. Transcript abundant analysis suggests the differential expression of alternative splice products of OsGATA genes under environmental signals. Taken together, our results may open a new path to explore the potential role of GATA transcription factors in abiotic stresses in model crop plant rice.

Characterization and Nomenclature of the GATA Gene Family in Rice
To identify all the putative GATA gene members of rice, MSU rice genome annotation project (RGAP) release 7 database (http://rice.plantbiology.msu.edu/) was scanned with GATA pfam (http://pfam.xfam.org/) domain ID PF00320. List of genes retrieved from MSU rice genome database was further confirmed by BLASTP (protein BLAST) in three different databases: NCBI (https://blast.ncbi.nlm.nih.gov/), phytozyme V.11 (https://phytozome.jgi.doe.gov/pz/portal.html), and plant genome database release 187 (http://www.plantgdb.org/), using Arabidopsis GATA1 protein sequence as the reference. Redundant sequences were omitted manually. Functional domains in the full-length protein sequence were identified using pfam, Interpro (https://www.ebi.ac.uk/interpro/), and SMART (http://smart.embl-heidelberg.de/) databases. To avoid ambiguity because of multiple names, nomenclature of these GATA genes was retained same as that of Reyes et al. (2004). However, newly identified alternative spliced products were denoted as gene number extended with suffix "a" and "b" as suggested earlier by Pareek et al. (2006).

Chromosomal Distribution of the GATA Gene Members
For locating the GATA members on rice chromosomes, CDS coordinates were retrieved from MSU RGAP database version 7 for each GATA gene and were placed on each of the rice chromosomes according to the physical location of the gene. Plant genome duplication database (http://chibba.agtec.uga. edu/) was used to search for the duplication events (segmental duplication and tandem duplication) of the OsGATA genes. Duplicated genes have been connected by dotted lines.

Multiple Sequence Alignment and Phylogenetic Tree Construction
Multiple sequence alignment of protein sequences of only GATA domain was performed using ClustalW program available in MEGA 7. The Neighbor-Joining tree was generated based on the MUSCLE alignment of the full-length OsGATA protein sequences using Jones-Taylor-Thornton (JTT) model under default setting in the MEGA 7 program. To infer phylogeny, 1,000 bootstrap replicates were taken. Gene structure display server version 2.0 (http://gsds.cbi.pku.edu.cn) was used to analyze the gene structure and for calculating the number of exon and intron in the gene sequence. The cis-acting elements in the promoter region of OsGATA genes were deduced using PlantPan version 2 (http://plantpan2.itps.ncku.edu.tw/).

Plant Material and Stress Treatment
Seeds of Oryza sativa L. cv IR64 and landrace Pokkali were rinsed thoroughly in sterile water, germinated in hydroponic set-up and raised for 7 days on half-strength Yoshida medium, at 28 ± 2 • C for 12 h light and dark cycles in a plant growth chamber. For stress treatment, 7 days old seedlings were transferred to half-strength Yoshida medium supplemented with either 200 mM NaCl (for salinity), 20% PEG (for drought) or 100 µM ABA and shoot samples were harvested after 4 and 24 h of stress application. Un-treated seedlings growing in half-strength Yoshida medium were taken as control. After harvesting, samples were frozen immediately in liquid nitrogen and stored at −80 • C until further use.

Extraction of Total RNA
Total RNA was isolated from the shoots of seedlings using TRIzol Reagent (Life Technologies, USA). For extraction, 100 mg tissue was homogenized to a fine powder with liquid nitrogen using pre-chilled mortar and pestle. RNA was extracted as described earlier by Soda et al. (2013). Purity and integrity of the total RNA was analyzed using spectrophotometry (Thermo Scientific, USA) and denaturing agarose gel electrophoresis respectively. The quality of RNA was checked by A 260 /A 280 ratio and samples having A 260 /A 280 > 1.8 were used for further analysis.

Synthesis of First Strand cDNA
First-strand cDNA was synthesized using first strand cDNA synthesis kit (Fermentas Life Sciences, USA) as per manufacturer's instructions. For removing genomic DNA contamination, DNAse (Epicenter, USA) treatment was done before proceeding for the cDNA synthesis as described earlier (Soda et al., 2013).

Primer Designing and Quality Check
Primers for OsGATA genes were designed from the region corresponding to the junction of unique 3 ′ UTR region and CDS sequence, using Primer Express 3.0 software (Applied Biosystems, USA). In the case of alternative splice variants, primers were designed from a unique region within the CDS sequence. The uniqueness of each primer pair to amplify a selected gene was confirmed by BLASTN using the RGAP database and NCBI databases.

Real-Time Quantitative PCR Analysis
The qRT-PCR analysis was performed with a Sequence Detection System ABI Prism 7500 (Applied Biosystems, USA). Reactions (final volume, 10 µl) were set up with the 2X SYBR Green PCR Master Mix (Applied Biosystems, USA), 3 µl cDNA sample and 0.5 mM of gene-specific forward and reverse primers. All the PCR reactions were performed under the following conditions: 2 min at 50 • C, 10 min at 95 • C and 40 cycles of 15 s at 95 • C, 1 min at 58-62 • C (annealing temperature range for different genes) and 30 s at 72 • C in 96-well optical reaction plates (Applied Biosystems, USA). The specificity of the amplification was tested by dissociation curve analysis. Three technical replicates were analyzed for each sample and the data analysis was performed using SDS 1.4 software (Applied Biosystems, USA). For data normalization, the rice eukaryotic elongation factor 1 alpha (eEF-1α) was taken as internal control. Transcript abundance of the selected group of genes was analyzed by qRT-PCR using the 2 −ddCT and 2 −dCT method for the calculation of fold change and transcript abundance respectively (Livak and Schmittgen, 2001).

Analysis of the Transcript Abundance of the OsGATA Genes at Different Developmental Stages of Rice
To analyze the expression of the OsGATA genes at different developmental stages of rice, publicly available microarray database (https://www.genevestigator.com/gv/) was scanned with locus ID listed in Table 1. Expressions of these GATA genes were analyzed at germination, seedling, tillering, stem elongation, booting, heading, flowering, milking, and dough stages of rice plant development.

RESULTS
Members of the GATA Family Show Huge Diversity in Their Size, Gene Structure, and Isoelectric Point (pI) BLASTP search in NCBI using full-length protein sequence from Arabidopsis GATA1 as query identified 35 sequences which contain at least one GATA zinc-finger domain ( Table 1). Further, MSU RGAP database version 7 was scanned for putative numbers of GATA genes using GATA domain ID PF00320 retrieved from pfam. Domain search also yielded 35 putative GATA transcription factors encoded by 28 gene loci. Our analysis yielded additional 7 OsGATA transcripts which were not reported earlier. Protein BLAST searches in RGAP database also yielded similar results. These 28 GATA genes were named as OsGATA1-OsGATA28 as described earlier by Reyes et al. (2004) ( Table 1). The alternative spliced forms were named as "a" and "b" along with the GATA gene number ( Table 1).
All the 35 GATA proteins contain at least one conserved GATA domain with a typical CX 2 CX 18−20 CX 2 zinc-finger motif except OsGATA8b which has partially truncated zincfinger loop. Protein domain analysis using pfam, SMART, INTERPRO databases confirmed that two of the GATA genes have more than one GATA domains in the encoded protein sequences. OsGATA26 has two, and OsGATA24 has three and one truncated zinc-finger loop in their encoded proteins. Protein sequences encoded by 15 GATA genes that contained accessory domains other than GATA might play additional roles in different physiological responses (Table 1). Among them, six members, OsGATA17, OsGATA18, OsGATA19, OsGATA20, OsGATA22, and OsGATA23 possess CX 2 CX 20 CX 2 zinc-finger loop in GATA domain. While rest of the GATA members contain a CX 2 CX 18 CX 2 type of domain structure. All the rice GATA members are listed in Table 1 along with the gene nomenclature, domain details, and amino acid length. However, we found many differences in the amino acid length as well as in exon/intron structure of GATA TFs from earlier reported information. Predicted amino acid length of OsGATA1, OsGATA2, OsGATA5, OsGATA6, OsGATA10, OsGATA13, OsGATA16, OsGATA20, OsGATA21, OsGATA22, OsGATA23, and OsGATA26 are 387,431,376,386,142,225,390,292,450,732,742, and 383 respectively but earlier reports by Reyes et al. (2004) showed the amino acid length as 386, 387, 390, 387, 140, 155, 348, 332, 303, 778, 786, and 415 respectively. In addition to amino acid length, we found differences in the number of exons. Predicted exon numbers in the OsGATA5, OsGATA7, OsGATA10, OsGATA13, OsGATA16, OsGATA19, OsGATA20, OsGATA22, and OsGATA23 are 2, 2, 2, 5, 3, 8, 6, 5, and 5 respectively which were previously reported as 3, 3, 3, 3, 4, 9, 8, 7, and 6. This variation could be because of rapidly evolving genomic data and availability of refined annotation tools in the rice genome database version 7.
The GATA TFs vary in amino acid length from 101 to 742 with a predicted isoelectric point (pI) ranging from 4.56 to 10.03 and molecular weight ranging from 10.98 to 84.43 kDa. OsGATA8b was found to be the smallest protein having amino acid length 101 and molecular weight 10.98 kDa. On the other hand, OsGATA23a was the largest protein with an amino acid length of 742 and molecular weight of 84.43 kDa.

Chromosomal Location and Phylogenetic Relationships among the GATA Family Members Reveal Their Random Distribution in Rice Genome
The OsGATA family members are randomly distributed on all the rice chromosomes, except VIII and IX (Figure 1). Maximum GATA genes i.e., six have been found to be present on chromosome III. On the other hand, only one each GATA gene has been annotated each on chromosomes VII and XI. The number of GATA genes vary from two to four on other rice chromosomes. OsGATA5 and OsGATA21 were clustered on chromosome IV between 27 and 27.2 Mb segments.
OsGATA14 and OsGATA15 were present on chromosome V between 28.2 and 28.9 Mb region (Figure 1). Gene duplication has always been one of the well-known basis for the expansion of a gene family. Duplication can be either tandem; if duplicated genes are located on the same chromosome and closely linked or segmental; if duplicated genes are located on different chromosomes. We have observed eight segmental duplication events between OsGATA gene members ( Figure 1) and one tandem duplication between OsGATA18 and OsGATA19 located between 27.5 and 30 Mb region of chromosome III (Figure 1).
Further, to compute the evolutionary distance between the genes, a Neighbour-joining tree was constructed in the Mega 7 program using the Jones Taylor Thornton (JTT) model. In this analysis, proteins with similar kind of domains got clustered in one group (Figure 2). In the course of setting up a new structural classification criteria for monocots, we have re-categorized the GATA proteins on the basis of their gene structure, the number of GATA domains, the position of GATA domain, and accessory domains (Figure 2). On the basis of homology in the GATA domain as well as the presence of accessory domain other than GATA, all the GATA genes have been subdivided into seven subfamilies (Figure 2). Typical domain structures of these TFs belonging to diverse subfamilies are presented in Figure 3. Subfamily-I has eight gene members including OsGATA1, OsGATA2, OsGATA3, OsGATA4, OsGATA5, OsGATA6, OsGATA7, and OsGATA25 (Figure 2). All these GATA proteins carry a single GATA domain at the C-terminal end (Figure 3). Among them, OsGATA1, OsGATA3, OsGATA6, OsGATA7, and OsGATA25 show the highest homology within the GATA domain ( Figure 5). Subfamily-II is the largest and comprises of nine GATA genes, OsGATA8, OsGATA9, OsGATA10, OsGATA11, OsGATA12, OsGATA13, OsGATA14, OsGATA15, and OsGATA16 with the GATA domain being centrally located. OsGATA8b, one of the alternative splice variant of OsGATA8, contains a partially truncated GATA domain.
TABLE 1 | List of the OsGATA genes: chromosome no; locus ID of the genes as annotated in RGAP version 7; nomenclature of the putative GATA transcripts where genes are named according to Reyes et al. (2004) and a and b denotes the alternative splice products of the same gene; subfamily of the GATA gene member; domain composition of the putative GATA transcription factor as deduced by SMART and pfam databases; start and end of the CDS coordinates on the respective chromosome; orientation of the promoter; number of exons in the gene structure; number of introns in the gene; amino acid length; predicted pI and molecular weight of the OsGATA proteins. Chr  Members of the subfamily-II are complex in terms of their domain architecture (Figure 3) and have been functionally sub-categorized as B-class of GATA transcription factors in rice and Arabidopsis (Behringer et al., 2014;Behringer and Schwechheimer, 2015). OsGATA9, OsGATA14, and OsGATA15 possess a HAN (HANABA TARANU) domain at the Nterminal of the protein (Figure 3). On the other hand OsGATA8, OsGATA10, OsGATA11, OsGATA12, OsGATA13, and OsGATA16 possess a highly conserved LLM (leucineleucine-methionine) domain at the C-terminal of the protein (Figure 3). Members of the subfamily-III include OsGATA27 and OsGATA28 (Figure 2) which contain an N-terminal located GATA domain. Though they possess similar domain structure but differ in their gene structure (Figure 4). These are intronless genes (Figure 4). The lone member of subfamily-IV, OsGATA21 came out as an outlier with an extreme N-terminal GATA domain (Figure 3). OsGATA21 possesses six exons and five introns (Figure 4). The two members of subfamily-V, OsGATA26 and OsGATA24 contain unique 2 and 3 & 1 truncated GATA domains respectively, in the encoded protein. Subfamily-VI comprises of GATA genes which encode for GATA protein having GATA domain along with two accessory domains namely TIFY and CCT (Figure 2). OsGATA17, OsGATA18, OsGATA19, and OsGATA20 belong to subfamily-VI. The alternative splice variants of the genes OsGATA17 (a and b), OsGATA18 (a and b), and OsGATA19 (a and b) possess typical GATA domain with CX 2 CX 20 CX 2 zinc-finger loop. Gene structure of the members of this subfamily is complex, having 6-7 exons in the coding sequence (Figure 4). The GATA subfamily-VII comprises of only two members, OsGATA22 and OsGATA23 (Figure 2). The coding sequence for these genes are interrupted by four introns and hence possess five exons (Figure 4). Typical domain structure of this subfamily includes GATA, FAR1, MULE, and SWIM domain (Figure 3). Both FAR1 (FAR Red Impaired Response1) and MULE (Mutator-like transposases) domains show sequence homology and possess C 2 H 2 zinc-finger-like FIGURE 2 | Phylogenetic tree and subfamily categorization of GATA members in rice. The Neighbor-Joining tree was constructed based on MUSCLE alignment in MEGA 7 using the Jones-Taylor-Thornton model using 1,000 bootstrap replicates under default setting. Based on the conserved domain structure in the full-length OsGATA proteins, these were grouped into 7 subfamilies. Each member belonging to one group were given similar colored shape. 0.1 scale bar corresponds to amino acid substitution rate per site.
motif and SWIM domain (found in SWI2/SNF and MuDR transposases). Further, to analyze the conserved amino acid residues in the GATA zinc-finger loop; we have carried out multiple sequence alignment of the GATA domains from all the peptide sequences ( Figure 5). In the case of OsGATA26, both the GATA domains were kept in analysis and numbered as OsGATA26_1 and OsGATA26_2. Similarly, all the three GATA domains of OsGATA24; OsGATA24_1, OsGATA24_2, and OsGATA24_3 were aligned along with the other GATA domains. Partially truncated GATA domains of OsGATA8b and OsGATA24_4 were not included in this analysis. Close inspection of the aligned protein sequences revealed that apart from the conserved Cys residues at Cys-1, Cys-4, Cys-25, and Cys-28 in the zinc-finger loop, few amino acid residues in between the Cys-4 and Cys-28 are also conserved. The residues Thr-11, Pro-12, Gly-17, Pro-18, Lys-24, Asn-26, and Ala-27 (Figure 5), contribute to the formation of α-helix in the zinc-finger loop; suggesting their role in maintaining the structural integrity of the domain.
The OsGATA Family Members Are Differentially Regulated in Contrasting Rice Genotypes in Response to Salinity, Drought, and ABA To comment on the possible roles of OsGATA family members in abiotic stress response, transcript abundance of OsGATA genes were analyzed in two contrasting rice genotypes, IR64 and Pokkali in response to distinct abiotic stresses such as salinity, drought, and stress responsive phytohormone ABA (Figure 6).
To check the expression of all the 35 transcripts, unique primer combinations (Table S2), from the junction of 3 ′ UTR and CDS sequence were designed. However, OsGATA6, OsGATA7, OsGATA9, OsGATA14, OsGATA15, OsGATA19b, OsGATA21a, OsGATA24, OsGATA27, and OsGATA28 could not be amplified from any of the cDNA used in this analysis; therefore these were kept out of the expression analysis. Expression data of remaining OsGATA genes has been presented in the form of heat map (Figures 6A-F).  The qRT-PCR analysis revealed unique findings for the OsGATA family expression. Differential accumulation of the OsGATA transcripts was observed in IR64 and Pokkali (Figures 6A-I). Basal level expression of OsGATA2b, OsGATA8b, OsGATA11, OsGATA16, OsGATA17b, OsGATA20, OsGATA22, OsGATA23b, and OsGATA25 were comparatively higher in both the rice genotypes (Figures 6A-F). However, expression of some of the genes was found to be genotype specific. GATA members such as OsGATA17a, OsGATA18a, OsGATA18b, OsGATA19a, and OsGATA21b were highly expressed in IR64 under control conditions. On the other hand, expression of OsGATA1 and OsGATA10 were higher only in Pokkali genotype under the control conditions (Figures 6D-F). Also, the transcript levels of the OsGATA2a and OsGATA13 in IR64 and Pokkali were not affected by any of the aforesaid stresses (Figures 6A-F).
OsGATA3 was upregulated in response to exogenous ABA in both the genotypes (Figures 6C,F). However, induction was more pronounced in Pokkali. OsGATA26 accumulated in response to salinity and drought in both the genotypes, but accumulation pattern varied with duration of stress given (Figures 6A,B,D,E). In Pokkali, the gene was upregulated after 4 h of salinity and drought stress. However, in IR64, under drought stress, transcripts accumulated at 24 h of stress while under salinity stress, induction is marked at 4 h (Figures 6A,B).
In IR64, members of subfamily-VI such as OsGATA17a, OsGATA17b, OsGATA18a, OsGATA18b, OsGATA19a, and OsGATA20 as well as OsGATA22, a member of subfamily-VII, maintained higher transcript level at 24 h of all the applied stresses (Figures 6A-F). OsGATA23a also maintained higher transcript level at 24 h but only under drought in IR64. Contrarily, in Pokkali, the expression pattern of the gene members from subfamily VI and VII varied with the duration of stress (Figures 6D-F). Though induction was observed with the onset of stress (4 h) in response to salinity, drought as well as ABA (Figures 6D-F); but the transcripts level declined as the stress continued for 24 h, in salinity and ABA (Figures 6D,F); finally maintaining a level higher than the control. Under drought, transcript levels maintained a similar accumulation at the end of 24 h as that of 4 h level (Figure 6).
In this study, differential regulation of the alternative spliced variants of some of the OsGATA genes was also identified (Figures 6A-I). The transcript level of OsGATA2a, a spliced variant of OsGATA2, remained unchanged in all the applied stresses for both the rice genotypes (Figures 6A-F). Interestingly, expression of OsGATA2b was induced in response to salinity, drought, and ABA in both the genotypes at early stress duration (4 h). However, responses varied from one genotype to another during the later duration of stresses. In case of IR64, OsGATA2b showed higher transcript levels at 24 h post salinity and drought while the expression was downregulated in the ABA treated samples (Figures 6A-C). On the other hand, downregulation was seen in the Pokkali samples for salinity as well as ABA stress treatment at the end of 24 h while a higher level was seen in case of drought stress imposed samples for the same duration of time (Figures 6C-F).
Expression of OsGATA8b, the alternative spliced variant of the gene OsGATA8, was higher than the OsGATA8a in both the genotypes under all the three conditions tested (Figures 6A-F). Similarly, splice variants of OsGATA23 also varied in their expression pattern in both the genotypes in response to abiotic stresses (Figures 6A-F). In IR64 as well as in Pokkali, OsGATA23a showed an induction at 4 h stress duration and continued to maintain higher transcript till the end of 24 h of salinity and drought but it was downregulated in response to ABA at 24 h in both the genotypes (Figures 6A-F). On the other hand, transcripts of OsGATA23b were relatively low as compared to the OsGATA23a in both the genotypes. It can be said that, though the gene showed induction at the onset of stress but ultimately its transcript levels declined as the stress prolonged for 24 h in both the genotypes (Figures 6A-F).
In terms of fold change, maximum induction i.e., 40-folds was observed for OsGATA8a and OsGATA26 in IR64 under salinity stress ( Figure 6G). Furthermore, in Pokkali salinity induced transcript levels of OsGATA8b, OsGATA18a, and OsGATA23a by more than 100-folds ( Figure 6G). Interestingly in IR64, drought stress and ABA application lead to induction of OsGATA23a by more than 150-and 35-folds respectively (Figures 6H,I).
Similarly in Pokkali, it was seen that drought and ABA modulate the expression of a set of genes that included OsGATA4, OsGATA8a, and OsGATA21b upregulating them by more than 100-and 35-folds with respect to control under drought and ABA treatment respectively (Figures 6H,I). Similarly, OsGATA11 and OsGATA26 show more than 200-and 100-fold change respectively in transcript level under the drought stress. On the other hand, OsGATA18a showed ABA-dependent upregulation up to 300-folds and OsGATA23a showed a 250-folds change in response to ABA ( Figure 6I).
The expression of the OsGATA23a was also found to be higher as compared to the OsGATA23b. In our study, OsGATA23a was identified as multi-stress responsive gene as it showed maximum upregulation for salinity, drought as well as ABA stress. In both the genotypes, this gene was induced at 4 h of stress. In the case of IR64, induction was 40-fold while in Pokkali more than 60fold induction was observed. Since ABA is a stress hormone, it not only governs stomatal opening but also acts as a master regulator of abiotic stress signaling. Our data suggested that some of the OsGATA genes are highly responsive to ABA, hence these OsGATA genes might also be interplaying an important role at the junction of stress signaling cascade.
To understand the regulation of these GATA transcription factors, we analyzed the cis-acting elements in the promoter region of OsGATA genes (Table S1). Binding sites of various stress responsive transcription factors such as AP2 (Apetala2), ERF (Ethylene Response Factors), bHLH (basic helix loop helix), bZIP (basic leucine zipper), MADF (myb/SANT-like domain in Adf-1), Myb, NAC (NAM, ATAF1/2 and CUC1/2), WRKY, and MADS box were found. Interestingly, we found that promoter region of almost all the OsGATA genes possess GATA binding sites indicating that the expressions of OsGATA genes might be regulated by GATA transcription factors themselves.

Expression of the OsGATA Genes Is Developmentally Regulated
To comment on the role of OsGATA transcription factors in rice developmental processes, we analyzed the expression data from publicly available microarray database, Genevestigator (Table S3). Transcript abundance of distinct members of the OsGATA gene subfamilies at various developmental stages like germination, seedling, tillering, stem elongation, booting, heading, flowering, milking, and dough stage was checked (Figure 7). Interestingly, transcripts of members of the subfamily IV and VI were found to be comparatively abundant throughout all the developmental stages of the rice plant. On the other hand, members of the subfamily II showed huge variation in terms of fold change at the different stages from seedling to maturity. The members of subfamily I and II showed mixed expression pattern. Our analysis showed that the OsGATA12 gene was induced the most during seedling stage amongst all OsGATA genes. Expression of OsGATA22, members of subfamily VII, was observed to be low as compared to other GATA gene members (Figure 7). On the other hand OsGATA23 falls under higher expression group (Figure 7).

DISCUSSION
Regulated expression of a gene is essential for distinct physiological and biochemical processes in a living system. Transcription factors play a key role in governing gene regulation and exhibiting differential expressions under different physiological and environmental conditions. In this study, we present a detailed analysis of GATA transcription factors in rice. Total 28 OsGATA genes were identified in corroboration with the previous finding by Reyes et al. (2004). Besides, we have reported 35 putative OsGATA TFs encoded from 28 OsGATA genes. Newly identified alternative spliced products of the OsGATA genes-OsGATA8, OsGATA12, OsGATA17, OsGATA18, OsGATA19, OsGATA21, and OsGATA23, contribute for the expansion of complex rice GATA TFs family. Alternative splicing is the hallmark of the complex transcriptome in eukaryotes, as the splice variants can drive the diverse functions of a gene (Park and Graveley, 2007;Syed et al., 2012). In our analysis, we have observed that the splice variants of the OsGATA2, OsGATA8, OsGATA18, and OsGATA23 respond differently to diverse environmental conditions.
Taking into account, the distinct domain architecture of the GATA proteins, their complex gene structure and phylogenetic analysis, the rice GATA genes were categorized into seven subfamilies. Subfamily II is the largest with nine GATA members viz. OsGATA8,OsGATA9,OsGATA10,OsGATA11,OsGATA12,OsGATA13,OsGATA14,OsGATA15,and OsGATA16. In this case, the GATA binding domain is centrally located and the GATA domain coding region is split into two halves by an intron sequence. However, members of the subfamily-II have been well characterized and functionally categorized into B-class of GATA gene family (Behringer and Schwechheimer, 2015). Additional domains like LLM and HAN present in the members of subfamily-II, have been wellstudied and identified as functional component of plant growth in Arabidopsis, tomato, Brachypodium, and barley (Behringer et al., 2014). They are involved in regulation of various physiological as well as structural transitions in plants such as germination, hypocotyl elongation, embryo development, flower development, and senescence (Behringer et al., 2014;Behringer and Schwechheimer, 2015). These additional domains present in rice GATA TFs might also be involved in various stages of rice plant development With the eight members grouping together, subfamily I is the second largest. Members of the subfamily I possess C-terminal GATA domain with typical CX 2 CX 18 CX 2 zinc-finger loop. Subfamily III possesses two GATA members wherein the GATA domain is located at the N-terminal. OsGATA21 is the only member of subfamily IV with unique six exons in the gene sequence and extreme N-terminal zinc-finger loop. Subfamily V includes OsGATA26 and OsGATA24 with 2 and 3 GATA domains respectively. Members of the subfamily VI and VII are the most peculiar as they possess CX 2 CX 20 CX 2 like zinc-finger loop for DNA binding. Apart from GATA domain, members of subfamily VI have TIFY and CCT domains. On the other hand, members of the subfamily VII contain FAR1, MULE, and SWIM domains. Previous studies related to GATA transcription factors in animals suggested that the C-terminal GATA finger proteins are involved in recognizing DNA elements in the promoter region, while N-finger GATA either assist this binding by stabilizing the DNA-protein complex or are involved in other physiological processes (Ko and Engel, 1993;Pedone et al., 1997). Like in animal system, extra GATA domain present in rice OsGATA24 and OsGATA26 might also be playing crucial roles in diverse cellular processes. Expression analysis carried out under salinity and drought stress resulted in more than 50fold change in OsGATA26 transcripts. Furthermore, exogenous application of ABA also upregulated expression of OsGATA26 by more than 10-folds. These findings indicate that the additional GATA domains present in OsGATA26 might be regulating its role in abiotic stress signaling.
GATA TFs have been shown to play an integral role in light-mediated signaling (Putterill et al., 1995;Chen et al., 2009). In our genome-wide investigation, we have found that members of subfamily VI possess a unique CCT domain which is also found in TOC1 (Timing of Cab Expression 1) and CO (CONSTANS) proteins. Both of these proteins are important components of light signaling, circadian clock, and flowering (Robson et al., 2001;Más et al., 2003;Wenkel et al., 2006;Gendron et al., 2012). Our analysis also revealed the presence of FAR1 domain along with GATA domain in members of subfamily VII. The FAR1 domain is found in proteins involved in phytochrome signaling (Hudson et al., 1999;Lin et al., 2008;Li et al., 2011). Although, no clear evidence for the involvement of rice GATA TFs in light signaling has been established yet, the domain analysis presented here indicates that OsGATA22 and OsGATA23 bearing FAR1 domain might be crucial components of phytochrome signaling and the circadian clock. In higher plants, proteins with FAR1 and SWIM domains are involved in phytochrome signaling. This suggested that GATA TFs having accessory domains like FAR1, SWIM and MULE domain might play a role in light-regulated signaling in plants. Luo et al. (2010) have demonstrated that one GATA TF, GATA2 from Arabidopsis functions at the junction of brassinosteroid and phytochrome signaling. This indicates that GATA transcription factors with similar domain structures may be functioning via a similar pathway.
To comment on the role of these GATA TFs in abiotic stresses, we have analyzed the relative transcripts level in response to salinity, drought, and multi-stress responsive phytohormone ABA. We found that under non-stress conditions, the transcript level of some of the GATA genes was higher in salt tolerant genotype Pokkali as compared to salt sensitive variety IR64. OsGATA1 and OsGATA10 maintained higher transcript levels in non-stress conditions in Pokkali. Earlier, from our lab, it has been reported that salt tolerant Pokkali maintains higher constitutive level of stress related genes which are otherwise induced in salt sensitive IR64 (Karan et al., 2009;Kumari et al., 2009;Soda et al., 2013;Nutan et al., 2017). Recent study on expression of Saltol QTL localized transcription factors revealed that Pokkali has an abundance of these transcripts which are otherwise induced under stress conditions only in IR64 genotype (Nutan et al., 2017). Our findings also indicate that above mentioned GATA TFs, OsGATA1, and OsGATA10 might also be acting as mediators of abiotic stress signaling and response.
Furthermore, expression of some of the OsGATA genes are stress specific but not genotype-specific. OsGATA3 is specifically induced in response to ABA and OsGATA26 in response to salinity and drought in both the genotypes. On the other hand, expression of members of subfamily VI and subfamily VII were higher in all the applied stresses. These findings suggest that OsGATA proteins with accessory domains may be function via a cross talk between different abiotic stress signaling pathways. Moreover, no change in the expression of OsGATA2a and OsGATA13 was observed with respect to any of the applied stresses in both the genotypes. This shows that both of these genes might not be part of abiotic stress signaling rather might be functioning in the rice developmental pathways. There are reports confirming the role of GATA TFs in distinct plant development and physiological processes (Liu et al., 2005;Lu et al., 2017).
In addition to this, differential regulation of alternative spliced forms of an OsGATA gene is also observed in response to abiotic stresses. OsGATA23a, a spliced variant of OsGATA23 is highly expressed in all the three stresses while relative expression of OsGATA23b was very low under salinity, drought, and ABA. Numerous reports are available justifying the role of alternative spliced forms in different environmental circumstances (Mastrangelo et al., 2012). Recent studies carried out by Jiang et al. (2017) reveal that heat stress alters the expression of some RNA binding proteins which in turn promotes the alternative splicing in grape. Our data also shows stress specific expression of the alternative spliced variants of OsGATA genes suggesting a similar mechanism operating in rice for the regulation of OsGATA genes under abiotic stresses.
To further gain insight into the regulation of OsGATA genes, we looked for the various cis-acting elements in the promoter region of OsGATA genes (Table S1). Binding sites for various stress responsive TFs were studied. Interestingly, OsGATA23 is the only gene having MADS box binding site in the promoter region. In rice, it has been reported that members of OsMADS gene family are regulators of abiotic stress signaling (Arora et al., 2007). Recently, it was reported that many of the OsMADS TFs are localized in Saltol QTL and are differentially regulated in contrasting rice genotypes (Nutan et al., 2017). As such it can be said that the multistress responsive nature of the OsGATA23 may be because of the binding and regulation via MADS-box TFs.
We have also examined the expression of OsGATA genes at different stages of rice plant development. In this analysis, we observed that members of subfamily IV and VI vary in their expression pattern from a medium to high level as the development proceeds from seedling to dough stage. In contrast, OsGATA gene members from subfamily I, II, and III showed varied expression level ranging from a low to high. At seedling stage, expression of OsGATA12 was observed to be highest. In an interesting study carried out in Arabidopsis, it has been reported that BME3 which is a GATA transcription factor, plays a significant role in seed germination (Liu et al., 2005). Transgenic Arabidopsis seeds under-expressing BME3 showed delayed germination. Our analysis based on genomewide expression analysis clearly shows that OsGATA genes might be playing a crucial role in rice developmental pathways. Behringer et al. (2014) have documented that mutation in LLM domain of B-GATA transcription factor of Arabidopsis affects plant growth and hypocotyl elongation. Besides this, distinct GATA factors are identified as functional component of shoot apical meristem (SAM) development, chloroplast development, flowering, growth, and cell division in Arabidopsis (Zhao et al., 2004;Chiang et al., 2012). Therefore, it is clearly evident that the GATA factors are one of the important messengers for plant cell transitions and differentiation.
Our in silico analysis and expression data markedly sheds some light on the complex circuitry of rice GATA transcription factors and their potential role in various physiological processes as well as in abiotic stress signaling. Additionally, it also opens a path for the future exploration and characterization of OsGATA genes to further understand the molecular regulatory network of transcription factors in rice.

CONCLUSIONS
In this study, whole genome analysis of OsGATA gene family in rice was done to identify putative OsGATA transcription factors encoded by rice genome. Their gene, as well as protein structure, phylogeny, chromosomal location, was deduced and expression pattern under environmental stresses as well as at various rice developmental stages was analyzed. A total of 35 OsGATA TFs encoded from 28 loci were found to be randomly distributed on rice chromosomes and were categorized into seven subfamilies. Members of the OsGATA gene family were differentially expressed under abiotic stresses. OsGATA23a is multi-stress responsive as it showed high transcript levels induced by salinity, drought as well ABA treatment. Overall, the present work in aimed at providing not only an insight into the diversity of OsGATA TFs, guides toward functional cataloging of OsGATA TFs in response to environmental signals, but to establish leads for understanding the mechanism governing abiotic stress adaptability in rice via GATA TFs.


s2fieldsofstudy: Biology Medicine

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Malignant Brenner tumor associated with a germline BRCA2 mutation

Highlights • The first woman with a Malignant Brenner tumor and a BRCA2 mutation is described.• Not all women with epithelial ovarian cancers are referred for genetic counseling.• Women should be referred for genetics regardless of how rare the histology.

Background
Germline mutations in BRCA1 and BRCA2 account for at least 13% of invasive ovarian cancers, although some studies place their actual incidence closer to 20% (Zhang et al., 2011). Given this high mutation rate, since October 2014 the Society of Gynecologic Oncology (SGO) has recommended offering genetic testing to all patients diagnosed with epithelial ovarian, primary peritoneal, or fallopian tube carcinoma (SGO Clinical Practice Statement, 2014). The National Comprehensive Cancer Network (NCCN) guidelines make the same recommendation (Daly et al., 2016). While the majority of epithelial ovarian cancers associated with germline BRCA1/2 mutations are high-grade serous carcinomas, other types of epithelial ovarian cancers have been noted to harbor deleterious BRCA mutations (Norquist et al., 2016). One rare type of epithelial ovarian cancer is the Brenner tumor, which represents < 1% of all ovarian cancers. The vast majority of Brenner tumors are benign, with malignant Brenner tumor representing < 5% of all Brenner tumors. Malignant Brenner tumor is characterized by atypical transitional cells, squamous or undifferentiated carcinoma with intervening stroma (Han and Soslow, 2011).
In 2013, Kuhn et al. suggested a fallopian tube origin for Brenner tumors based on morphologically identical cilia on the fallopian tube and ovarian surface, as well as a very similar immunohistochemistry profile in the two tissues (Kuhn et al., 2013). Although rare and thus difficult to characterize, fallopian tube cancers are estimated to be forty-eight times higher in BRCA1 mutation carriers than in the general population (Brose et al., 2002). Furthermore, the fallopian tube has been found to be an important site of epithelial ovarian cancer precursors in women with germline BRCA1/2 mutations (Kindelberger et al., 2007). Until recently, no identifiable connection had been made between malignant Brenner tumor and a BRCA1/2 germline mutation; however, in 2016, Norquist identified a BRCA1 mutation in a patient with malignant Brenner tumor. We present a case of a patient with a malignant Brenner tumor and a deleterious BRCA2 germline mutation.

Case
A 54 year-old Caucasian female para zero originally presented in 2008 with a one-year history of vague gastrointestinal complaints and lower abdominal pain to a local emergency department (ED). The patient's medical history was significant for malignant melanoma in 1997, which was treated with surgical excision and did not require adjuvant therapy. Her family history was significant for colon cancer in her father and brother, as well as gallbladder cancer in her mother. Upon arrival to the ED, a CT scan demonstrated masses lateral to the ascending colon and anterior to the cecum. She underwent an exploratory laparotomy, hysterectomy, bilateral salpingo-oophorectomy, and omentectomy. A postoperative CA-125 was 675 U/ml. Her surgical pathology was reviewed and was consistent with malignant Brenner tumor (Figs. 1 and 2) with immunohistochemical stains, including positive CK7, WT-1, and CA-125, and negative CDX2 and CK20, confirming primary ovary origin (Fig. 3). Negative MART-1 and S100 stains ruled out recurrence of her melanoma.
After her initial tumor cytoreductive surgery, she underwent six cycles of combined intravenous and intraperitoneal platinum and taxane therapy at our institution. After normalization of her CA-125 she remained in clinical remission for four years. After her first recurrence she was treated at an outside institution with two additional tumor reductive surgeries and an additional six cycles of taxane and platinumbased chemotherapy. While being treated for a fourth recurrence in 2016, she was referred to our genetics clinic for testing. Testing demonstrated a pathogenic mutation in BRCA2, specifically c.4965C > G, which is a nonsense mutation that changes a Tyrosine to a premature stop codon and results in loss of function of the BRCA2 protein. This nonsense mutation is also implicated in prostate cancer and was shown to be associated with a more aggressive phenotype and overall poorer survival in BRCA2 carriers (Castro et al., 2013). After revelation of the BRCA2 mutation, treatment with PARP inhibitors was considered but deferred as her current treatment regimen of carboplatin was working to treat her disease.

Comment
While individuals with serous epithelial ovarian cancers are more likely to harbor deleterious germline mutations, patients with other histologic subtypes warrant referral for genetic counseling and testing per national guidelines. In 2016, Norquist et al. further classified inherited deleterious mutations in ovarian carcinomas. Of the 1915 women evaluated, a total of 9 had transitional cell carcinomas. Of those 9 cases, one had an associated BRCA1 mutation, with no cases having a BRCA2 mutation. Although malignant Brenner tumors are substantially more rare, this mutation rate (11%) is similar to the mutation rate associated with the high-grade serous histology (Norquist et al., 2016). Relationships between the origins of fallopian tube epithelium and Brenner tumors, as well as between BRCA1/2 mutations and fallopian tube origins of serous carcinoma have been shown. An extensive English literature search was performed to evaluate any previously described connection between BRCA2 mutations and malignant Brenner tumors. PubMed search terms included BRCA, BRCA1, BRCA2, malignant Brenner tumor, and Brenner tumor, and a review of the references of relevant papers was performed. While one case of malignant Brenner tumor in an individual with a BRCA1 mutation has been reported, to our knowledge our case represents the first known case of a malignant Brenner tumor associated with a BRCA2 mutation. Interestingly, this patient also had a case of cutaneous melanoma in 1997, which can also be associated with familial BRCA2 mutations (Moran et al., 2012).
Despite current guidelines, genetic risk assessment for patients with epithelial ovarian cancer remains underutilized, with some providers still relying more on family history than histology (Febbraro et al., 2015;Lheureux et al., 2016). Patients with uncommon histology or patients who were diagnosed prior to the recommendation for universal testing are likely at higher risk of not having undergone genetic testing. In addition, it is important to note that not all gynecologic oncology patients are taken care of by gynecologic oncologists, and some, especially in rural areas, are cared for by general surgeons. This, in combination with patients having multiple providers can be barriers to guideline adherence. In a 2017 study, Hayden et al. demonstrated that financial concerns were a barrier to proceeding with genetic testing after undergoing genetic counseling. Over 15% of those with private insurance and 36% of those with government-sponsored insurance cited financial concerns as a reason for not obtaining genetic testing. Continued attention for widespread insurance coverage of genetic testing  for those at high risk for familial cancer remains paramount (Hayden et al., 2017). The testing and discovery of germline BRCA1/2 mutations for at risk patients is critical for medical decision-making for individual patients as well as their relatives. For the affected patients, the discovery of a harmful mutation may lead to more frequent cancer screening for other malignancies in addition to potential therapy options. Preventive therapies, such as mastectomy or tamoxifen for breast cancer and bilateral salpingo-oophorectomy or oral contraceptive pills for ovarian cancer, have been shown to decrease risk in high-risk populations (Iodice et al., 2010;Phillips et al., 2013;Domchek et al., 2010). For relatives, the knowledge of a deleterious germline mutation may offer the opportunity for enhanced surveillance or prophylactic surgery, which as mentioned above would substantially decrease their risk of developing a cancer in the future. This finding affirms the recommendation that we should be counseling and testing all patients with non-mucinous epithelial ovarian, fallopian tube, or primary peritoneal cancers for germline mutations regardless of how common or rare the histology. Our finding is supported by the Norquist et al. study demonstrating similar rates of BRCA1 mutations in high-grade serous carcinomas and malignant Brenner tumors (Norquist et al., 2016). Going forward, especially with the decrease in cost of genetic testing in recent years, a deliberate effort should be made to follow the established recommendation and refer all women with epithelial ovarian cancers for comprehensive genetic counseling and genetic testing.


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Genetic variants in SERPINA4 and SERPINA5, but not BCL2 and SIK3 are associated with acute kidney injury in critically ill patients with septic shock

Background Acute kidney injury (AKI) is a multifactorial syndrome, but knowledge about its pathophysiology and possible genetic background is limited. Recently the first hypothesis-free genetic association studies have been published to explore individual susceptibility to AKI. We aimed to replicate the previously identified associations between five candidate single nucleotide polymorphisms (SNP) in apoptosis-related genes BCL2, SERPINA4, SERPINA5, and SIK3 and the development of AKI, using a prospective cohort of critically ill patients with sepsis/septic shock, in Finland. Methods This is a prospective, observational multicenter study. Of 2567 patients without chronic kidney disease and with genetic samples included in the Finnish Acute Kidney Injury (FINNAKI) study, 837 patients had sepsis and 627 patients had septic shock. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, considering stages 2 and 3 affected (severe AKI), stage 0 unaffected, and stage 1 indecisive. Genotyping was done using iPLEXTM Assay (Agena Bioscience). The genotyped SNPs were rs8094315 and rs12457893 in the intron of the BCL2 gene, rs2093266 in the SERPINA4 gene, rs1955656 in the SERPINA5 gene and rs625145 in the SIK3 gene. Association analyses were performed using logistic regression with PLINK software. Results We found no significant associations between the SNPs and severe AKI in patients with sepsis/septic shock, even after adjustment for confounders. Among patients with septic shock (252 with severe AKI and 226 without AKI (149 with KDIGO stage 1 excluded)), the SNPs rs2093266 and rs1955656 were significantly (odds ratio 0.63, p = 0.04276) associated with stage 2–3 AKI after adjusting for clinical and demographic variables. Conclusions The SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 were associated with the development of severe AKI (KDIGO stage 2–3) in critically ill patients with septic shock. For the other SNPs, we did not confirm the previously reported associations. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1631-3) contains supplementary material, which is available to authorized users.

Background
In critically ill patients, the incidence of acute kidney injury (AKI) is high -39% in the recent prospective, observational, multicenter study, the Finnish Acute Kidney Injury (FINNAKI) study, which was conducted in Finnish intensive care units (ICUs) [1]. In other prospective studies in critically ill patients the incidence of AKI is reported as 24-66% [2,3]. Although the pathophysiology of AKI has been investigated, there is no single explanation for the condition due to the multifactorial nature of the syndrome. Sepsis is the most common factor predisposing to AKI in the critically ill. In patients with sepsis the incidence of AKI is 31-53% [4][5][6] and in patients with septic shock it is even higher, at 47-61% [7,8]. The previously identified risk factors for septic AKI, such as age, sex, and baseline comorbidities, have failed to reliably predict individual risk of septic AKI [9]. Thus, it is plausible that genetic variability between individuals may explain a significant part of the risk.
Genetic predisposition to AKI has been previously studied in candidate genes, by testing associations between single nucleotide polymorphisms (SNP) from candidate genes and a phenotype. Our recent systematic review confirmed that despite some positive associations there are no conclusive data [10]. A study by Frank et al. [11] provided one of the very first hypothesis-free approaches to septic AKI; it comprised 887 patients in septic shock defined according to American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) criteria [12], who were genotyped using a large-scale genotyping microarray. In this study, five SNPs were associated with AKI after validating the results in an additional sample. Interestingly, the associated SNPs are in the apoptosis pathway genes. The SNP rs8094315 and SNP rs12457893 are located in the intron of B-cell CLL/lymphoma 2 (BCL2)gene. The SNPs rs2093266 in the serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 4 (SERPINA4) gene and SNP rs1955656 in the serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 5 (SERPINA5) gene are in complete linkage disequilibrium (LD). There was also association between the SNP rs625145 in the salt-inducible kinase family 3 (SIK3) gene and AKI. In this study we aimed to replicate the aforementioned findings [9] in a prospective cohort of critically ill patients with sepsis.

Study population
This study is a predetermined genetic study of the prospective, observational, multicenter FINNAKI study, in which patients were recruited from 17 Finnish ICUs. Details of the FINNAKI study have been published elsewhere [1] and are presented in Additional file 1. The Ethics Committee of the Department of Surgery in Helsinki University Hospital approved the study. An informed separate written consent for genetic samples was obtained from the patient or next of kin at the initiation, with the option of deferred consent. The main study ended on 1 February 2012, and recruitment was extended until 30 April 2012 to achieve an adequate number of patients with sepsis. The study was conducted according to the Declaration of Helsinki.

Data collection
We collected the study-specific data items (approximately 80% of the data) on admission and daily until day 5 or ICU discharge using an electronic case report form (CRF). Data comprised previous and current health status, medication, risk factors for AKI, laboratory values and operations preceding ICU admission, sepsis and related organ dysfunctions, and focus of infection. Presence of AKI and/ or sepsis was screened until ICU discharge or day 5 at the latest if the patient was still in the ICU. Routine data were collected with the help of the Finnish Intensive Care Consortium, which consists of 25 ICUs nationwide.

Definitions
For staging of AKI, plasma creatinine was measured daily and urine output hourly. We defined and staged AKI according to the new Kidney Disease: Improving Global Outcomes (KDIGO) AKI criteria [13]. We sought to compare patients with a severe phenotype of AKI (KDIGO stage 2-3) to patients with no AKI. Thus, we excluded patients with KDIGO stage-1 AKI from the current analysis because their phenotype may be seen as indecisive. We defined sepsis and septic shock according to the ACCP/SCCM definitions [12], a definition also used in the previous report [11].

Blood sampling and DNA extraction
Whole blood was collected at enrollment and after separation of plasma, stored at -80°C for subsequent DNA extraction. DNA was isolated on a Chemagic 360 instrument (Perkin Elmer, Baesweiler, Germany), based on magnetic bead technology, using a Chemagic DNA Blood10k Kit according to the manufacturer's instructions. DNA concentrations were determined using UV light and PicoGreen methods, and samples were diluted into 10 ng/μl for genotyping.

Genotype analysis
Genotyping was performed at the Technology Centre of the Institute for Molecular Medicine Finland (FIMM), University of Helsinki. The genotyping was done using Agena MassARRAY® system and the iPLEX TM Gold Assay (Agena Bioscience TM , San Diego, CA, USA). This method has excellent success (>95%) and accuracy (100%) [14]. Genotyping reactions were performed on 20 ng of dried genomic DNA in 384-well plates according to the manufacturer's recommendations and using their reagents [15]. Both polymerase chain reaction (PCR) and extension primers were designed using Mas-sARRAY Assay Design software (Agena Bioscience TM ) (Additional file 2). The data were collected using the MassARRAY Compact System (Agena Bioscience TM ) and the genotypes were called using TyperAnalyzer software (Agena Bioscience TM ).
We examined genotyping quality by a detailed quality control procedure consisting of success rate check, duplicated samples, water controls, and Hardy-Weinberg equilibrium (HWE) testing. In addition, the genotype calls were checked manually and corrected when necessary. Genotyping personnel were blinded to the clinical status of the patients.

Statistical analysis
We compared the clinical and demographic variables to test for significant differences between groups using the Fisher exact test for categorical variables and the Mann-Whitney U test for continuous variables. We present data as medians and interquartile ranges, or absolute values and percentages. Statistical analyses of the demographic and clinical variables were performed using the SPSS Statistics version 22 (IBM Corp., Armonk, NY, USA).
Associations between AKI and SNP were adjusted for clinical characteristics that differed significantly between patients with and without AKI. In addition to the univariate analysis we performed multivariate analysis using logistic regression by entering variables, selecting characteristics with a p value <0.2 in univariate analysis. The percentage of missing data was 15.9%. The missing data were imputed on the assumption of being unaffected for categorical variables and on the median for continuous variables.
Genetic association tests were performed using logistic regression and the PLINK software [16]. As in the previous study an additive genetic model was assumed. However, confirmatory tests were performed using recessive and dominant genetic models. In the primary analysis patients with sepsis and AKI (KDIGO stage 2-3) were compared to patients with sepsis but no AKI. Secondary analyses were performed including cases and controls from the entire cohort, and was performed separately only in those with septic shock. As this was a replication study, we considered all p values <0.05 significant.

Power calculations
Power calculations were made using Genetic Power Calculator [17], assuming allele frequencies and odds ratios from the validation cohort from the study by Frank et al. [11], and with the assumption of 299 patients and 354 controls, a prevalence of AKI of 39,% and a type I error rate of 0.005. For detailed power calculations for each SNP see Additional file 3.

Patients
We prospectively enrolled 2968 ICU patients in the FIN-NAKI genetic study, as presented in the study flowchart ( Fig. 1 The baseline characteristics of the patients with sepsis who did and did not have AKI are presented in Table 1. The baseline characteristics of the patients with septic shock who did and did not have AKI are presented in Table 2. The baseline characteristics of the entire cohort are presented in Additional file 4.

Genetic associations
All of the polymorphisms tested were in HWE. None of the five SNPs investigated, rs8094315 (odds ratio (OR) 1.10, p = 0.48), rs12457893 (OR 1.02, p = 0.87), rs2093266    Table 3). The SNPs rs2093266 and rs1955656 are in complete linkage disequilibrium (LD) and thus present identical results. In logistic regression analysis in patients with sepsis, higher body mass index (BMI), not having chronic obstructive pulmonary disease (COPD), use of non-steroidal anti-inflammatory drugs (NSAID) as daily medication, administration of contrast medium prior to ICU admission, simplified acute physiology score II (SAPS II) without renal or age components, and source of infection were significantly associated with KDIGO stage 2-3 AKI (Additional file 5). Adjustment for these clinical and demographic factors in the patients with sepsis did not change the results for genetic association, which were not statistically significant (Table 3).
In patients with septic shock, none of the investigated SNPs was significantly associated with AKI in univariate analysis (Table 4). After adjusting for clinical and demographic variables that remained significant in logistic regression (BMI, use of NSAID as daily medication, arteriosclerosis, COPD, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, SAPS II without age or renal components, operative admission, and source of infection (Additional file 6)) SNPs rs2093266 and rs1955656 were significantly associated with AKI (OR 0.63, 95% CI 0.40 to 0.98, p = 0.043, for each) ( Table 4, Fig. 2). The carriers of the minor alleles of these SNPs (A and A, respectively) had a decreased risk of developing AKI. This association was in the same direction as in the previous report. The minor allele frequencies of all investigated SNPs in patients with septic shock are presented in Fig. 2.
When the same analyses were performed in the entire genetic cohort, there was no evidence of association between any of the SNPs and AKI in univariate or multivariate models when adjusting for clinical and demographic variables that remained significant in logistic regression (Additional files 7 and 8).
Using recessive and dominant genetic models did not change the results: SNPs rs8094315, rs12457893 and rs625145 were not associated with AKI in any of the subgroups studied. The protective association with SNPs rs2093266 and rs1955656 prevailed in patients in septic shock in the dominant adjusted model (OR 0.59, p = 0.034, for each) (Additional file 9). The self-assessed quality score of the study was 9/10 [18] (Additional file 10).

Discussion
In this study we confirmed the previously reported association between AKI and two polymorphisms, rs2093266 and rs1955656 in apoptosis-related genes SERPINA 4 and SERPINA5 in patients with septic shock. Both in our study and in the original study the minor alleles of these SNPs provided protection from AKI. The association signal was seen in both additive and dominant genetic models but not in the recessive model, suggesting that merely one protective allele is sufficient.
Our study was based on a previous report suggesting that polymorphisms within apoptosis-related genes may be associated with the development of AKI. In this study comprising 887 patients with septic shock, AKI was associated with five SNPs [11]. To our knowledge this is the first successful replication of two of these SNPs. There was no evidence in our cohort of significant association between AKI and the other three polymorphisms tested, rs8094315, rs12457893, and rs625145.
The SNP rs2093266 is located in an intronic region of SERPINA4 gene encoding kallistatin, a serine proteinase inhibitor that has multiple regulatory roles in biological processes [19,20]. In kallistatin there are two functional domains, an active site and a heparin-binding site, through which it regulates several signaling and biological   pathways. Along with its functions in relation to apoptosis, it has anti-inflammatory, antioxidant, vasodilator and angiogenesis inhibiting functions [21]. All these mechanisms are relevant in septic AKI and could thus explain the better outcome. In recent mouse model studies, Kallistatin has been associated with attenuated inflammation and organ injury and decreased mortality in established sepsis [22], and with improved survival in sepsis-related acute lung injury [23]. Of note, it may also have a renoprotective role against diabetic nephropathy [24]. rs1955656 in SERPINA5 is strongly correlated with rs2093266 and as suspected before, this association with AKI could be driven by rs2093266. Notably, the product of SERPINA5 is known to inhibit activated protein C. This protein C inhibitor (PCI) plays a role in tumor growth and metastasis through its effect on blood coagulation, but it has also been suggested to inhibit the anti-inflammatory activity of activated protein C [25,26]. This inflammatory function could possibly explain the protection against septic AKI.
Of the other three SNPs studied, two (rs8094315 and rs12457893) are located in the introns of the apoptosis-related gene BCL2 encoding an integral outer mitochondrial membrane protein BCL2 that blocks the apoptotic death of certain cells [27]. The SNP rs625145 is in SIK3, coding for a member of the AMP-activated protein kinase family that affects the regulation of several genes. This protein is found to suppress inflammatory molecule gene expression in macrophages stimulated with lipopolysaccharides (LPS) [28].
There is no definite consensus on the pathophysiology of AKI, and until recently the most common approach has been to test for association with known genetic variants. These variants have often come up in another phenotype, commonly chronic kidney disease, and, thus, may serve as poor markers of AKI [29,30]. So far, the most studied polymorphisms are within inflammatory mediator genes [10].
Apoptosis-related genes are good candidates for AKI because there is some evidence that apoptosis is an important mechanism in septic AKI. In a murine model of septic AKI the pathophysiology appears to differ from that of ischemia-reperfusion insult, lacking signs of tubular cell injury by necrosis but showing a substantial number of tubular cells undergoing apoptosis [31]. Lerolle et al. found that in post-mortem kidney biopsies  Patients/controls. b Adjusted for age, body mass index, diabetes mellitus, mechanical ventilation, minimum platelet count, use of non-steroidal anti-inflammatory drugs as daily medication, chronic pulmonary obstructive disease, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, simplified acute physiology score II without age or renal components, operative admission, and source of infection. SNP single nucleotide polymorphism, Chr chromosome of patients who died of septic shock (n = 19) there was a marked increase in apoptosis and capillary leukocytic infiltration in comparison with patients with trauma and ICU controls [32]. However, there were contradicting results in a separate sample of post-mortem kidney biopsies in patients who died of sepsis [33]. The nature of septic AKI alone appears to be diverse, with temporal and individual variation, and the role of apoptosis is inconclusive [34]. We also tested the association between the five SNPs and development of AKI in all patients with sepsis, but the results were negative. The phenotype of septic shock differs from that of sepsis without shock, reflecting a more severe form of illness and a greater risk of death. Thus, we can speculate that the associations between SNPs and AKI detected in these patients, although not generalizable to the septic cohort, can predict better survival for the carriers of the protective allele in terms of AKI.
The strength of our study is that it was a prospective, relatively large, multicenter study comprising consecutive patients. In addition we used the KDIGO criteria to provide a robust phenotype of AKI and critically ill patients without AKI as controls to increase the power of our study.
There are some important limitations in our study. First, 171 genetic samples (5.8%) could not be analyzed due to failure in the DNA isolation phase or to rejection of samples because of a low genotyping success rate. Second, we did not collect data ont ethnicity. However, 99.9% of Finnish-speaking inhabitants are Caucasian. Third, individual genetic susceptibility factors can be expected to increase the AKI risk with values just exceeding an OR of 1 [35]. In our power calculations we used the ORs reported in the original study, and, thus, our study might have been underpowered to replicate the results for BCL2 and SIK3. Fourth, we did not adjust the p values for multiple testing, in this replication of positive findings.
Finally, in contrast to the previous study [11] we excluded patients with chronic kidney disease. Furthermore, we aimed to strengthen the phenotype by comparing patients without AKI to patients with more severe AKI (KDIGO stage 2-3), thus excluding those with KDIGO stage 1. We reasoned that this group would include patients with only KDIGO stage-1 urine output  (not included in [11]) criteria, whose phenotype clearly differs from that of more severe acute kidney. These findings provide some interesting questions for future research. The functions of the SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 for the protein products are yet to be determined. If further studies can provide independent evidence supporting the role of these SNPs in AKI susceptibility, information about the genotype of either of these SNPs may add to the battery of risk-predicting tools. However, the effect size of the SNPs is too small for the genotype information to work as an independent biomarker in a clinical setting.

Conclusions
In this study we aimed to replicate the previous findings associating polymorphisms within apoptosis-related genes to AKI. We found that SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 were associated with KDIGO stage 2-3 AKI in critically ill patients in septic shock.

Additional files
Additional file 1: FINNAKI study enrollment and inclusion of patients. Report of the enrollment period and inclusion and exclusion criteria. Additional file 5: Logistic regression (the "enter" method) in patients with sepsis (n = 653) in differing demographic variables, missing data imputed. Number (N) of imputed values and percentages of the cohort are given. Results of logistic regression in patients with sepsis showing that higher BMI, not having COPD, use of NSAID as daily medication, administration of contrast medium prior to ICU admission, SAPS II score without renal or age components, and source of infection were significantly associated with KDIGO stage 2-3 AKI. (DOC 54 kb) Additional file 6: Logistic regression ("enter" method) in patients with septic shock (n = 478) in differing demographic variables, missing data imputed. Number (N) of imputed values and percentages of the cohort are given. Results of logistic regression in patients with septic shock showing that BMI, use of NSAID as daily medication, arteriosclerosis, COPD, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, SAPS II without age or renal components, operative admission, and source of infection were significantly associated with KDIGO stage 2-3 AKI. (DOC 53 kb) Additional file 7: Association between acute kidney injury and the polymorphisms studied in all genotyped patients (n = 2146) (additive genetic model). Association between acute kidney injury and the studied polymorphisms in all genotyped patients was tested in univariate and multivariate models. No significant associations are reported. (DOC 35 kb) Additional file 8: Logistic regression ("enter" method) in all genotyped patients (n = 2146) in differing demographic variables, missing data imputed. Number (N) of imputed values and the percentages of the cohort are given. Logistic regression in all genotyped patients showed that BMI, chronic liver disease, use of NSAID as daily medication, use of warfarin as daily medication, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, administration of albumin prior to ICU admission, maximum white blood cell count, minimum platelet count, SAPS II without age or renal components, operative admission, and source of infection were significantly associated with KDIGO stage 2-3 AKI. (DOC 72 kb) Additional file 9: Association between acute kidney injury and the studied polymorphisms, recessive and dominant genetic models. Association between acute kidney injury and the polymorphisms studied in septic patients, patients with septic shock and all genotyped patients, recessive and dominant genetic models. Univariate and multivariate associations are reported. In dominant genetic model in patients with septic shock the SNPs rs2093266 and rs1955656 were significantly associated with KDIGO stage 2-3 AKI after adjustment.


extfieldsofstudy: Mathematics Medicine

text:
Evaluation of the external and internal workload in female futsal players

Match analysis technology has been extensively used in football, but there is limited literature on its use in futsal. Despite its increased popularity, the female futsal game model has never been quantified. The aim of this study was to quantify locomotor and mechanical activities performed during a non-competitive female futsal match, measuring the differences between the first and second half. Sixteen female futsal players of the Italian 2nd division were enrolled (age 27±5 years, height 1.65±0.09 m, body weight 56.9±7.7 kg, BMI 20.9±1.9, fat mass 21.5±2.9%). Locomotor and mechanical activities were recorded by means of the 10 Hz GPS StatSports system. Games were performed on a 38x18 m synthetic grass outdoor pitch. Significant differences were found between the first and second half in total distance (1424±114 and 1313±113 m, p<0.05), relative velocity (70±6 and 64±6 m min-1, p<0.05), high speed running (28±16 and 22±19 m, p<0.05) and high metabolic distance (80 ± 29 and 69 ± 28 m, p<0.05). The match analysis of female futsal matches provides useful information about its external load demands. Female futsal players decreased the workload in the second half compared to the first one during this non-competitive match. It was found that fatigue impairs the performance in the second part of the game. Coaches and physical trainers can obtain useful information to design training programmes taking into account the quantification of locomotor and mechanical activities performed in this study.

INTRODUCTION
Futsal is commonly named five-a-side indoor football format. It is played on a field of 40x20 m (range between 38x18 and 42x22 m) with goals of 3x2 m between 2 teams of 5 players (four are outfield players and one is the goalkeeper). Futsal's popularity has improved in recent years, and more than 12 million players in over 100 countries are involved [1]. Despite its popularity, its scientific background in male and especially in female futsal players is limited, compared to the traditional game format in male and female soccer players [2].
Futsal can be defined as a multiple-sprint sport with an intermittent high intensity activity [3]. Male futsal players cover a mean of 4000 m (large range of 601-8040 m due to substitution policies), at mean relative velocity (RV) of 120 m • min -1 (range 102. 7-145.4 m) during official matches [4]. Moreover, Barbero-Alvarez et al. [4] reported that futsal players covered a distance (during official competitions) by walking of 397 m, jogging 1762 m, medium intensity running 1232 m, high intensity running 571 m and at maximum speed 349 m. Understanding the futsal game model and quantifying external load variables as distance covered, relative veloc-ity (RV), number of accelerations, decelerations, and change of directions may be a useful task for the design of suitable training programmes [3,5,6].
Metabolic power is an ecological approach useful to quantify workload during matches and training sessions [7]. The estimation of the metabolic power used the rationale that accelerated running on a flat terrain is energetically analogous to uphill running at constant speed [8]. The metabolic power approach better represents a soccer match model than a speed-based approach [7,8]. Indeed, especially on small pitches, traditional speed categorization may heavily underrate (over 100%) the quantification of high-intensity distance performed by players [9]. Thus, metabolic power variables might be included in external load analysis in order to better understand the actual futsal demands.
Studies that described futsal match analysis referred to male players only [5,4]. To the author's knowledge, no study has investigated a female futsal match using a match analysis approach. Moreover, there is no evidence of external training load quantification by effect of fatigue. Games were performed on a 38x18 m synthetic outdoor pitch. An outdoor pitch was used to enable the analysis of the GPS technology, as currently GPS technology does not gain a signal indoors to provide all the relevant variables. Total distance covered (TD) measured in metres, high speed running over 14.4 km • h -1 (HSR), number of accelerations and decelerations performed (>2 m • s -2 ), relative velocity calculated as the ratio between TD and the total time, average metabolic power (MP) and high intensity metabolic power distance over 20 W kg -1 (HMD) were measured and analysed [8]. The indirect estimation of the metabolic power used the rationale that accelerated running on a flat terrain is energetically analogous to uphill running at constant speed: Where EC is the energy cost of accelerated running on grass, EM is the equivalent mass and ES is the equivalent slope [8,10]. This study cannot explain in detail the rationale of this algorithm; thus we invite researchers and sport scientists to read Osgnach 2010 [8].
Further analysis was conducted measuring dynamic body load (DBL) with a 100-Hz triaxial accelerometer. The integration of triaxial accelerometers into GPS devices offers additional information about athletes' physical loads. The triaxial accelerometer summates accelerations in the 3 movement axes (X, Y, and Z planes) to measure a composite magnitude vector (expressed as a Gforce) [11]. This external parameter may be a specific new indicator of mechanical stress. It has demonstrated a good relationship with external (TD, r=0.70) and internal (rating of perceived exertion, r=0.74) load variables [11]. Body load evaluates every instantaneous variation in training activity, and based on a logical rationale relative to the football and futsal game model it could be a good indicator of players' workload.

Statistical analysis
Statistical analyses were performed using SPSS software version 20 for Windows 7, Chicago, USA. The assumption of normality was verified by the Shapiro-Wilk test. Paired t tests were performed to analyse the differences between the first and second half. Significance was set at p<0.05. Data are presented as mean ± SD. Cohen's d (ES) was calculated to determine the magnitude of effect by standardizing the coefficients according to the appropriate betweensubjects standard deviation and was assessed using the following criteria: small > 0.2, medium > 0.5, large > 0.8 [20].

RESULTS
Players covered a distance of 920±164 m during IR1, and they reported an HRmax of 194±7 bpm. Several significant differences between GPS parameters for the first and second half were identified, as reported in Table 1. Significant decreases (p<0.05) between the first (time 20 minutes 25 s) and second half (20 min 26 s) were a metabolic power approach in this sport. Therefore, the aim of this study was to quantify the workload in a female non-competitive futsal game, investigating the differences between the first and second half, and taking into account metabolic power as a new parameter in futsal.

Procedures
Body fat estimation was determined using a skinfold-based method (skinfold calibre, Gima S.p.A., MI, Italy). Body density was calculated using the Jackson and Pollock equation [21]. We also recorded both body weight (BW), height by stadiometer (Seca, Italy) and BMI. The measures (skinfold, body weight and height) were obtained three times using the average value for the analysis. Each player's HRmax was determined by completion of a Yo-Yo intermittent recovery test level 1 (IR1). Heart rate was recorded during IR1 and during matches using Polar RS400D heart rate monitor watches (Polar, Oulu, Finland), and the data were analysed with ProTrainer 5 software at the end of each test [5]. Before every session players performed a traditional warm-up for 12 minutes. In the first phase players did exercises without the ball and in the second part they performed specific exercises with the ball (players were supported by their coach during warm-up activation). Training load parameters were recorded during two friendly matches (composed of two halves of 20 minutes) by means of the 10 Hz GPS system and 100-Hz triaxial accelerometer (STATSports, Ireland). Validity of these GPS units was previously reported [22].

DISCUSSION
To the best of our knowledge, this study is the first to analyse a friendly futsal match in female players. A decrease in many physical parameters in the second half was identified (reported in Table 1). Due to the lack of research in this area, no available comparisons can be made for recorded MP, HMD, DBL, number of accelerations and number of decelerations.
It is generally accepted that changes of speed, accelerations and decelerations are important parameters in team sports [5]. It is known that 'stop and go' actions or high intensity intermittent bouts have higher energy demands than running at constant speed [6]. Significant decreases in physical load parameters were shown between the first and second half, especially in TD and HMD. However, DBL, number of accelerations and number of decelerations did not show a significant difference between halves. The decrease in physical workload that was found is in agreement with previous reports on male futsal players [4,12]. Barbero-Alvarez et al. [4] reported a significant decrease in high intensity running and RV from 118.4 m • min -1 to 110.5 m • min -1 between the two halves of the match, while De Oliveira et al. [12] found a decrease in RV from 136.6 m • min -1 to 129.2 m • min -1 . Physical activity decrease is a phenomenon potentially associated with fatigue [13]. The current study results show that fatigue may affect performance in female futsal players as reported in male players [3]. In this study, players performed the matches without substitutions; thus the onset of fatigue was inevitable during the match and it caused a decrease in performance (external and internal load parameters); this type of match protocol underlines that some substitution strategies (e.g. replace a couple of players every 2-3 minutes) are necessary in order to decrease the negative effect of fatigue. The physiological motivation of fatigue was previously explored in soccer [13]. It was found that after the game about half of the type I and type IIA fibres were almost or completely depleted of muscle glycogen [13]. The current data are in line with previous reports that showed a general performance decrease between the two halves of football of a longer duration (i.e. 45 min each) [13].
As previously stated, the current data cannot be compared to studies that included female samples, although it is possible to perform a brief comparison to male futsal players. On the basis of previous literature, large differences exist between female and male futsal players in match-load parameters, where male professional players performed greater TD (4000 vs. 2737 m) and relative velocity (117 vs. 67 m·min -1 ) compared to females [4]. These large differences are likely due to the differences in physical conditions between genders (male vs. female) and also league level (professional vs. semi-professional) [14]. Previous analysis of activity profiles of outfield players showed that the TD covered by elite players was knowledge on this topic, as well as on female futsal players generally. In this study players performed matches on a synthetic outdoor field; this was necessary in order to record the data by GPS devices that cannot work indoors. Official futsal matches are played on indoor pitches. In this case, it may be suggested that the limitation is not associated with the pitch location (indoor or outdoor), but rather with the field surface. Synthetic grass was used in the current study, and it has different characteristics to wood or other official surfaces typically used in futsal. However, a large number of teams, especially semi-professional or amateurs (and frequently even profes- kicking performance during shooting [19]. Future analyses are necessary in order to better understand the futsal game model in female players, and to investigate external load parameters suitable for this sport, as metabolic power and DBL may provide a better understanding.

Conflict of interest:
The authors declared no conflict of interests regarding the publication of this manuscript.
This study is the first to our knowledge that compares traditional external loads to metabolic parameters in an outdoor futsal pitch.  [7,9]. Thus, it is recommended to disregard the speed categories in a futsal game and training analysis.
To date, it is advised to quantify external workload by TD, number of accelerations and number of decelerations [3]. In the current study, analysis of internal (HR) and external load parameters (metabolic power and DBL) underlined the necessity to integrate both the parameters in order to better support training and match load analysis [7]. However, before the integration of these methods in routine evaluations, further analysis in future research has to be conducted.
Metabolic power has the potential to differentiate training within futsal, but this approach still has its limitations [16]. In this study, the utilization of DBL during futsal match analysis is reported for the first time. It is a relatively new parameter that records every instantaneous variation of activity, and it can add to the investigation of specific indicators of mechanical stress. Future research may analyse better its use in futsal.
This study is not without its limitations. The futsal matches studies were conducted in friendly matches, and it is possible to argue that an official futsal match could have a greater physiological impact and higher external load than reported in this study. In a futsal match, a team comprises five players, four on-court players and the goalkeeper, with an unlimited number of substitutions. This study did not allow substitutions, and we have to consider it as a limitation.
In addition, a larger sample size might be necessary to increase the Biology of Sport, Vol. 34 No3, 2017 231