Atlas Journal of Biology 2017, pp. 376–383 doi: 10.5147/ajb.2017.0153 A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) Additional Polymorphisms Linked to Soybean Cyst Nematode Resistance At The Rhg4 Locus Oleksandra Zatserklyana1,2, Khalid Meksem3, and David A. Lightfoot2,3,* 1 Department of Biochemistry and Molecular Biology, Southern Illinois University Carbondale, 1245 Lincoln Dr., Mail Code 4413, Carbondale, IL 62901; 2 Genomics Core Facility; Department of Plant Soil and Agricultural Systems, and Center for Excellence in Soybean Research, Teaching and Outreach, Southern Illinois University at Carbondale, Carbondale, IL 62901; 3 Department of Plant, Soil, and Agricultural Systems, Southern Illinois University Carbondale, 1205 Lincoln Dr., Mail Code 4415, Carbondale, IL 62901. Received: March 29, 2017 / Accepted: April 20, 2017 __________________________________________________ * Corresponding author: ga4082@siu.edu 376 Abstract Glycine max L. Merr., (soybean) is one of the major crops in the United States, south America and Asia. Yet, the seed yield of soybean is significantly reduced due to Heterodera glycines (Ichinohe), the soybean cyst nematode (SCN). SCN is one of the most destructive pests and pathogens of soybean because the main methods for control have proven difficult. Lineages of soybean that are resistant to SCN have been de- veloped but it is yet unknown what all the molecular causes of this resistance are. Rhg1 and Rhg4 loci are two of about 10 loci that underlie resistance. The Rhg4 locus, is required for resistance to SCN race 3 in ‘Peking’–type derived resistances. One gene in the locus has been isolated and a causative link shown, but the surrounding regions have not been fully ana- lyzed. Focusing on the Rhg4 locus, this study was aimed at uncovering other potential causes of resistance of soybean to SCN. In a bioinformatic analysis of the Rhg4 locus, alleles of nine genes were analyzed. The set of large intergenic re- gions have key regulatory elements in them. Since partial resistances are often multigeneic, some of these nine genes could be candidates for causing and regulating resistance. Keywords: Soybean; nematode; resistance; Rhg4. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecom- mons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the origi- nal work is properly cited. Introduction SCN or soybean cyst nematode (Heterodera glycines Ichino- he,) is one of the most destructive pests of Glycine max L. Merr. (soybean) worldwide (Matthews, 2004; Kurle et al., 2015; Yu Q. 2015). Due to the importance of G. max as an agricultural crop in the United States, the need for better management of SCN-infested fields is pronounced, as crop yield can be re- duced by up to 80% in the presence of SCN in a field (Smolik and Draper, 2007; Kazi et al., 2017). Two loci involved in resistance to SCN, are rhg1 and Rhg4 (Liu et al., 2012; Liu et al., 2011). Both rhg1 and Rhg4 combine to encode resistance to the same type of SCN race 3, HgType0 (Afzal et al., 2012; 2013; Liu et al., 2012). Rhg4 encodes resis- tance (Brucker et al., 2005; Liu et al., 2012) only in the ’Peking’ – type of resistance that is the chief subject of this study. Among the resistant lines that have been developed, none have been fully sequenced (Schmutz eet al., 2010). Among the partially sequenced resistant cultivars is ‘Forrest’ (unpublished) that was used extensively in this study. The cultivars used that were susceptible to H. glycines were ‘Williams 82’ [GenBank: EF623856] and ‘Asgrow 3244’ [GenBank: GP062386.1]. De- letions in these sequences can be seen (Zatserklyana, 2015; Tuteja and Vodkin 2008; Hauge et al., 2009a,b), introducing the problem of three sequences possibly not aligning correctly during genome mapping and recombination events. The origin of the Forrest cultivar was from the Peking lineage of soybeans (Hartwig and Epps 1973; Liu et al., 2009). Peking is a semi-wild, or semi-domesticated, black seeded G. max. The Forrest culti- var was derived from Peking lineage by introgression, of CNVs, A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) SNPs, insertions and deletions at both the rhg1 and Rhg4 loci (Cook et al., 2012; Liu et al 2012; Srour et al., 2012). Forrest cultivar sequence derived from two BAC libraries us- ing BamHI and HindIII enzymes (Meksem et al., 2000). The con- struction of BAC libraries and the physical map helped put the fragmented genome sequence of Forrest together (Shultz et al., 2006). Although the Forrest cultivar library and the BAC libraries for the Asgrow 3244 and the Williams 82 susceptible cultivars were available, complete BAC libraries did not exist for either cultivar (Wu et al., 2004). Wu et al. (2004) reported a com- plete physical map from three BAC libraries derived from the Forrest cultivar. As the BAC libraries created complement each other, they expanded the number of clones used soybean ge- nome analysis. The Williams 82 susceptible cultivar, in contrast, arose largely through intra-cultivar genetic heterogeneity following or during introgression of phythophtora resistances (Haun et al., 2011). Not all the genes at or near or in the Rhg4 locus have had their functions discovered (Yi et al., 2010; Center for Integrative Ge- nomics 2017; Liu et al., 2017). Among those that have is CHS7, which is the seventh gene in the chalcone synthase family, a hy- droxyl-proline serine transferase and the I gene for seed coat color. The difficulty in fully mapping the Rhg4 locus and the molecu- lar markers associated with it arises out of HgType variability, genomic variability in the soybean and the very small distance between this locus and I locus of less than 0.01cM (Weisemann et al., 1992; Lewers et al., 2001; Wu et al., 2011). This rela- tively small genomic distance between the loci makes it difficult to map the Rhg4 locus relative to the I locus at high resolution (Weisemann et al., 1992). For rhg1 and Rhg4 loci, both map- ping and annotation are necessary to fully elucidate the full set of causes of resistance to all HGTypes of SCN. Genetic mapping alone may not be sufficient to show this (Lewers et al., 2002). The annotation to date has shown extensive duplications that have taken place in BAC 56G2, the BAC where the Williams 82 Rhg4 susceptible sequence is located (Lewers et al., 2002). Here, nine genes were found in a BAC derived from Forrest that aligned poorly with Williams 82 sequences, some of which could be candidates for causing and regulating resistance. Further, large variations in intergenic regions were present that could have key regulatory elements within them. Materials and Methods BAC sequences from Forrest, Asgrow 3244, and Williams 82 soybean cultivars, and BLASTn, tBLASTx, tBLASTn, BLASTp, ORF Finder, and GeneMark.hmm were used. Sequence from For- rest cultivar was provided by Dr. Lightfoot from a BAC sent to JCVI for sequencing. The other cultivar sequences were obtained through BLAST searches from NCBI (used for Asgrow 3244) and Phytozome (for Williams 82). The nucleotide BLAST program at NCBI was run to differentiate between the two files containing the Forrest sequence. The alignment of each of the files contain- ing the Forrest sequence separately gave the accession number of the BAC Williams 82 sequence. It was the accession number of the BAC Williams 82 sequence that was used in the alignments. BLAST and ClustalW were used to do multiple alignments. With the BLASTn program, the alignment was being shown as broken up into sections. After the large gaps were found, Needleman-Wunsch was used to complete the alignment. Com- pleting the alignment included trimming the Williams 82 56G2 BAC sequence to the length of the Forrest BAC (92 kbp) and doing alignments with the trimmed Williams 82 56G2 BAC se- quence. Pairwise alignments and ORF Finder were used to search for genes and for what could be genes in the Rhg4 region. The pair- wise alignments were done using BLASTn, tBLASTx, and tBLASTn (McGinnis and Madden, 2004; Ye et al., 2006; Johnson et al., 2008) using the Forrest B100B10 BAC DNA sequence and the nr database, limiting the search to G max. The reason for this was to see what known genes, if any, could align with the Forrest B100B10 BAC DNA sequence. The ORF Finder at NCBI (Wheel- er, 2003) complemented these BLAST searches by showing what other genes may be encoded in the Forrest B100B10 BAC DNA sequence and where they may be located. GeneMark.hmm and BLASTp were used in annotating the Forrest B100B10 BAC sequence. GeneMark.hmm was used to predict the genes that are located on the Forrest B100B10 se- quence using the GFF mechanism of the program and having GeneMark.hmm give the protein sequences as part of the out- put. BLASTp was then used to look for the possible amino acid changes in the protein sequence. Results InDels and SNPs All the insertions and deletions were looked at in Forrest in comparison to both susceptible cultivars and to either of the sus- ceptible cultivars as was the case in the region of alignment where Williams 82 and Asgrow 3244 overlap (Supplementary Table 1). This was the region from 850 bp to 7,466 bp. Longer Insertions and Deletions The region of alignment between the Forrest and Williams 82 sequences showed a protein coding region (1,284 – 5,720 bp) in the deletions in Forrest (Supplementary Table 1). Out of the 1603 amino acids in this protein, nineteen amino acids were deleted from Forrest in the process of the Forrest cultivar becom- ing resistant to SCN. Sixteen of the nineteen deleted amino acids were nonpolar and hydrophobic, suggesting that this protein lies somewhere in one of the membranes in the cells. More than sixty percent of the deletions where codons have been deleted from the Forrest cultivar were more AT rich than GC rich (Table 1). Outside of the protein-coding region in the stretch of DNA where Forrest and Williams 82 align, this becomes a pattern. 99.8% of the deletions in this region are AT rich. It is possible these deletions result from Forrest cultivar undergoing mutation while being strongly selected for resistance to SCN during back- crossing. Table 2 shows the deletions and how AT rich they are over the entire Forrest-Williams 82 region. 377 A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) 378 SNPs The distribution of SNPs forms a more or less normal distri- bution through the Forrest-Williams 82 and the Forrest-Asgrow 3244 aligned DNA. In the same region (through the 92.5% of the region of DNA where the Williams 82 sequence and the Asgrow 3244 sequence align), the number of the insertions and deletions in SNPs differ by whether a nucleotide is a purine or a pyrimidine. A few SNPs that are insertions are found in pyrimidine nucleotides, the opposite is seen with the SNPs that are deletions (Table 3). However, when the alignment of Forrest B100B10 BAC DNA sequence and Williams 82 56G2 BAC DNA sequence was analyzed, there were 508 SNPs that are result of a single nucleotide substitution. There is great diversity between the Forrest B100B10 BAC DNA sequence and the Williams 82 56G2 BAC DNA sequence. Graphically, in the alignment between the Forrest B100B10 BAC DNA sequence and the Williams 82 56G2 BAC DNA sequence the two sequence appear to align well in small regions (Figure 1-2). Analysis of this alignment, by BLAST, showed the alignment as sections with gaps. Hence the need for Needleman-Wunsch alignments. Table 1. Here are listed the deletions in the Forrest-Williams 82 pro- tein-coding region and the percentage of AT or GC that is present in each deletion. The deletions, which are indicated in column 1 by num- ber, all come from the Forrest sequence compared to the Williams 82. Columns 2 and 3 show the starting and ending base position of each deletions and while the numbers in columns 2 and 3 correspond to the base positions in Asgrow, they are of use as this sequence overlaps with Williams 82 in this region. Columns 4 and 5 show the percentage of AT and GC, respectively, and the last column shows codon. Number Start End Length % AT Codon 1 96623 96625 3 33 TGG 2 96634 96636 3 66.7 GAA 3 96749 96751 3 66.7 ATG start 4 96877 96879 3 33 GGT 5 96985 96987 3 67 TTG 6 97123 97125 3 67 CTA 7 97168 97170 3 67 ATT 8 97274 97276 3 67 GTA 9 97725 97727 3 66.7 not a codon 10 98135 98137 3 66.7 TAC 11 98409 98411 3 66.7 CAT 12 98554 98556 3 33.3 CAC 13 98680 98682 3 33.3 CAG 14 99114 99116 3 33.3 AGC 15 99127 99129 3 100 TTT 16 99379 99381 3 33.3 GTG 17 100089 100091 3 33.3 GGA 18 100655 1000657 3 33.3 GGA 19 101159 101161 3 66.7 TTC 20 101256 101258 3 33 GAG 21 101322 101324 3 100 ATT 22 101337 101339 3 66.7 GTA 23 101436 101438 3 66.7 CTT 24 101556 101558 3 100 TAA stop Table 2. Here is listed the deletions in the entire Forrest-Williams 82 region and the percentage of AT or GC that is present in each deletion. The deletions, which are indicated in column 1 by number, all come from the Forrest sequence in comparison to the Williams 82. Columns 2 and 3 show the starting and ending base position of each deletions and while the numbers in columns 2 and 3 correspond to the base positions in Asgrow, they are of use as this sequence overlaps with Williams 82 in this region. Column 4 shows the length of each deletion and was the basis of sorting the values of the table for the graph. Column 5 shows how AT rich each of the deletions is while the percentage of how GC richness each deletion is shown in column 6. Number Start End Length % AT 1 96623 96625 3 33 2 96634 96636 3 66.7 3 96749 96751 3 66.7 4 96877 96879 3 33 5 96985 96987 3 67 6 97123 97125 3 67 7 97168 97170 3 67 8 97274 97276 3 67 9 97725 97727 3 66.7 10 98135 98137 3 66.7 11 98409 98411 3 66.7 12 98554 98556 3 33.3 13 98680 98682 3 33.3 14 99114 99116 3 33.3 15 99127 99129 3 100 16 99379 99381 3 33.3 17 100089 100091 3 33.3 18 100655 100657 3 33.3 19 101159 101161 3 66.7 20 101256 101258 3 33 21 101322 101324 3 100 22 101337 101339 3 66.7 23 101436 101438 3 66.7 24 101556 101558 3 100 25 101999 102001 3 100 26 102046 102048 3 66.7 27 102253 102255 3 100 28 102727 102729 3 67 29 103201 103203 3 66.7 30 101890 101893 4 50 31 96436 96439 4 25 32 96792 96795 4 50 33 97640 97644 4 60 34 97903 97906 4 75 35 98216 98219 4 25 36 98424 98427 4 25 37 98647 98650 4 50 38 98662 98665 4 75 39 98938 98941 4 50 40 99183 99186 4 60 41 99800 99804 4 40 42 100034 100037 4 75 43 101092 101095 4 50 44 101105 101108 4 50 45 101293 101296 4 100 46 101924 101927 4 50 47 101946 101949 4 50 48 102124 102127 4 50 49 102231 102234 4 50 50 103126 103129 4 50 A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) 379 A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) Needleman-Wunsch With the subrange for the Query and Subject sequences set to 10 kbp for the first nine sub-regions, and then to 1336 bp the tenth sub region, the Needleman-Wunsch algorithm was able to make the alignment. Between 1 and 200 bp, 201 and 400 bp, 1 and 1000 bp, 90,001 and 94,336bp there were many gaps in Table 2. Continued. Number Start End Length % AT 51 96314 96318 5 40 52 96447 96451 5 100 53 97284 97288 5 60 54 97315 97319 5 60 55 97411 97415 5 80 56 97864 97868 5 80 57 97965 97969 5 60 58 98330 98334 5 0 59 98704 98708 5 60 60 98752 98756 5 80 61 98881 98885 5 60 62 100834 100838 5 40 63 101391 101395 5 60 64 101527 101531 5 60 65 101690 101694 5 60 66 102389 102393 5 80 67 102423 102427 5 80 68 102990 102994 5 100 69 97196 97201 6 83.3 70 97970 97975 6 83.3 71 97985 97990 6 66.7 72 98715 98720 6 66.7 73 103174 103179 6 66.7 74 97253 97259 7 85.7 75 97336 97342 7 57.1 76 97813 97819 7 57.1 77 101303 101309 7 100 78 101580 101586 7 100 79 96378 96385 8 75 80 96465 96472 8 75 81 97385 97392 8 75 82 97710 97717 8 50 83 98381 98388 8 25 84 100609 100616 8 50 85 101673 101680 8 87.5 86 101982 101989 8 75 87 96402 96410 9 22.2 88 96981 96989 9 87.5 89 98121 98129 9 55.6 90 98617 98625 9 27.8 91 100780 100788 9 66.6 92 101265 101273 9 88.9 93 101281 101289 9 66.7 94 97268 97277 10 60 95 100710 100719 10 90 96 101444 101453 10 60 97 97678 97688 11 81.2 98 101535 101545 11 81.8 99 102951 102962 12 75 100 96330 96343 14 46.2 101 102885 102898 14 14.3 102 97474 97488 16 50 103 96850 96866 17 68.8 104 98584 98600 17 68.8 105 99412 99429 18 94.1 Figure 1. The alignment between the Forrest B100B10 BAC DNA se- quence (y axis) and the Williams 82 56G2 BAC DNA sequence (x axis) that had been trimmed to the length of the Forrest B100B10 BAC DNA sequence of 93.7bp. Figure 2. The alignment of the Rhg4 gene between the Forrest B100B10 BAC DNA sequence (x axis) and the Williams 82 56G2 BAC DNA se- quence (y axis) from 1-200 bp. Figure 3. The alignment of the Rhg4 gene between the Forrest B100B10 BAC DNA sequence (x axis) and the Williams 82 56G2 BAC DNA se- quence (y axis) from 201-400 bp. A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) 380 Gene # Gene Name Strand Start End SNPs Amino Acid changes 1 Serine hydroxymethyltransferase - 1441 2757 1593 A to T GNIYRIGSLPSGFDLL QMSINLTCSLCDCFA 2 Adenosylhomocysteinase + 10044 12138 no substitutions V238F, M239D, S240N, F242Y, P243G, CRHSL, L24G6, V247L, SFLC 3 Predicted heat stress transcription factor A-5-like isoform X2 - 20983 21828 no substitutions no amino acid changes 4 Unknown Glycine max gene + 25127 25432 no substitutions R10F, K11P, T12Q, Y13T, V14I, G15D, A19S, A22T, K24E, V25I, L28F, R32G, E33K, K34E, E35R, A36D, S37G, G40S, R41D, P43G, H44D, E46K, V49T, G50A, G51T, R54T, N55D, V57R, V58I, G59S, G60V, G61A, I62V, V63F, L66I, G68T, L69F, E70F, K71Q, R75H, T7R6, T77N, G79T, L80R, W81T, R82V, T83L, H85R 5 Predicted putative pentatricopeptide repeat-containing protein At1g12700 mitochondrial isoform X2 - 35243 36895 no substitutions no amino acid changes 6 Predicted microfibrillar-associated protein 1 + 37980 39182 38375 G to A, 38436 G to C, 38459 G to A no amino acid changes 7 Receptor-like kinase, Leucine-rich repeat receptor-like kinase - 73853 74395 73866 G to A no changes 8 Leucine-rich receptor-like kinase, Rhg4-like receptor kinase, receptor-like kinase - 75072 77252 76596 A to T, 77124 C to A G722A 9 Predicted anthocyanidin 3-O-glucoside 5-O-glucosyltransferase 1-like - 83975 85411 84209 T to C, 84232 C to T, 84239 G to C, 84242 C to T, 83245 C to A, 84251 G to T, 84257 C to T, 84260 A to G, 84265 T to A, 84279 G to A, 84287 T to G, 84290 A to C, 84302 A to C, 84308 T to C, 84326 C to A, 84335 T to C, 84337 T to C, 84340 A to C, 84386 C to A, 84395 T to C, 84397 T to A, 84416 T to C, 84504 C to G, 84520 G to T, 84534 A to G, 84552 T to A, 84563 A to C, 84574 C to T, 84584 C to G, 84589 T to C, 84590 C to G, 84611 T to C, 84621 T to C no amino acid changes Table 3. SNPs that result from single nucleotides substitutions and the changes in amino acids they cause, along with the genes these SNPs are expected to be located in. Red letters indicate insertion and blue letters indicate deletion. (The information in this table is up to date as of August 14, 2015). A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) 381 the alignment (Figures 3-5). Alignments inferred the exact (already known) location of Rhg4 gene (Liu et al., 2012). Using the trimmed and untrimmed version of the Williams 82 BAC sequence, the Query Coverage percentages were compared. For the alignment of Forrest as the Query Sequence and the trimmed Williams 82 BAC sequence as the Subject Sequence, the Query Coverage was 97% while for the alignment of Forrest as the Query Sequence and the untrimmed Williams 82 BAC sequence (starting from the point in the Williams 82 BAC sequence where Forrest sequence would end) as the Subject Sequence it was only 3%. ORF Finder An unknown was how many of the deleted or inserted codons (Table 1) were in the coding region of proteins. In total, in the Forrest B100B10 BAC sequence the ORF Finder found 327 ORFs (open reading frames), six of which are greater than 1,000 bp in length (Figure 6). One open reading frame was similar to serine hydroxyl-methyl serine transferase mRNA (1,317 bp for the open reading frame and 1,416 bp for the mRNA) but was incomplete on the BAC. Annotation Another unknown was which SNPs, caused changes in the ami- no acid sequences. GeneMark.hmm predicted ten genes within the Forrest B100B10 BAC DNA sequence, but manual annotation inferred there were nine genes in the Forrest B100B10 BAC DNA sequence, as genes 2 and 3 may be one gene. In contrast to the large number of SNPs that were found in the Forrest B100B10 BAC DNA sequence that are the result of single nucleotide sub- stitutions, the number of amino acid changes and the number of SNPs in the nine coding regions were 56 and 40, respectively. The results of the GeneMark.hmm prediction of genes in the For- rest B100B10 BAC DNA sequence and BLASTp, along with the SNPs and amino acid changes were shown in Table 3. Discussion Forrest cultivar sequence resistant to H. glycines and the sus- ceptible Asgrow 3244 and Williams 82 cultivar sequences were used in this project. Deletions in these sequences can be seen (Tuteja and Vodkin 2008; Hauge et al., 2009a,b; Zatserklyana, 2015) which can caused the cultivar sequences to not align cor- rectly, partly because the cultivars retain heterologous regions (Haun et al., 2011) and partly because BACs are single alleles cloned from within that variation. Multiple sequence alignments and Needleman-Wunsch Global Alignments were the key tools in the project. A deleted peptide was noted and the size of insertions and/ or deletions was notably larger compared to the alignment around Rhg1 (Hemmati et al., 2017). Transposons often cause indels and may be responsible for altering the degree and type of SCN resistance. In the Rhg4 region are predicted to be nine genes, four of which are genes with predicted functions (heat stress transcription factor A-5-like isoform X2, putative pentatri- copeptide repeat-containing protein At1g12700 mitochondrial isoform X2, microfibrillar-associated protein 1, and anthocy- anidin 3-O-glucoside 5-O-glucosyltransferase 1-like; Figure 6). Figure 4. The alignment of the Rhg4 gene between the Forrest B100B10 BAC DNA sequence (x axis) and the Williams 82 56G2 BAC DNA se- quence (y axis) from to 1-1000 bp. Figure 5. The alignment of the Rhg4 gene between the Forrest B100B10 BAC DNA sequence (x axis) and the Williams 82 56G2 BAC DNA se- quence (y axis) from 89 kbp to 91 kbp. Figure 6. The overview of the Rhg4 region showing the seven genes, regulatory elements and extent of the Rhg4 region in relation to Forrest B100B10 BAC DNA sequence. If we look at these genes as though they would be located on one strand, if transcription would occur, we would see AHC, Unknown G. max gene, and MAP 1 genes being transcribed to the right and SHTM (Rhg4 gene) , HSTF A-5-like isoform X2, PPRCP At1g12700 ML isoform X2, RLK, LRR-RLK, and AC 3-O-G 5-O-G 1-like genes being transcribed to the left. (The information in this figure is up to date as of August 14, 2015). A tla s Jo ur na l o f Bi ol og y - IS SN 2 15 8- 91 51 . P ub lis he d By A tla s Pu bl ish in g, L P (w w w .a tla s- pu bl ish in g. or g) 382 The major gene at the Rhg4 region was at the end of the Forrest BAC sequence, a serine hydroxymethyltransferase. It is a gene proven to be involved in the resistance of soybean to SCN (Liu et al., 2012; Lakhssassi et al., 2012; 2017; Liu et al., 2017). This study confirmed those earlier findings and showed the complex- ity of the introgressed region. Strong selection pressures on wild and semi-domesticated soybeans to develop resistance to SCN were inferred (Han et al 2016). Authors’ Contributions DAL and OZ did the analysis, with OZ actively carrying out the bioinformtics (DAL generated the GeneMark.hmm file) and DAL guided the work. KM isolated the BAC and wasCo-PI on the grant that sequenced it. The text of this article was based on the MS thesis written by OZ that DAL helped edit. Acknowledgements We thank Dr. Khalid Meksem for creating the clone. He per- sisted, away from home, at TAMU, to pick the 100th plate of the second library. We thank Dr. Hogbin Zhang for hosting Khalid and teaching the BAC library creation methods. We thank Dr. Naghmeh Hemmati for her help with downloading the ‘Williams 82’ sequence and submitting ‘Forrest’ sequence to GenBank as HQ008938.1. The Sanger sequencing was paid for by the United Soybean Board. Dr. Christopher Town of JCVI is thanked for supervising the sequencing and assembly. 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