Figure 2

It is evident from the dendrogram that there is a grouping of the genes in accordance with the proteins encoded by them. From the ClustalW dendrogram, it is clear that the genes are grouped into 3 categories. Further for convenience of analysis, we grouped them into 7 clusters. As the different domains present in the sequence determine the functions of the proteins, a thorough analysis of the functional domains was performed to authenticate the dendrogram

Maximum number of genes falls into the first category which comprises of 4 clusters, organized into 2 branches. Clusters 1, 2 & 3, which are located on one branch, predominantly show the presence of Tumor Necrosis Factor (TNF) and related domains. TNF-alpha, a family of TNF can stimulate cell proliferation and induce cell differentiation under certain conditions. Death domains present along with TNFRSF have a role in apoptosis and various other domains present in genes MYETS1, MMSET, FAS etc. play important roles in gene transcription, translation, mRNA trafficking, cytoskeleton organization, epithelial development, cell adhesion, protein folding, chromatin remodeling and zinc sensing, to mention but a few.

Cluster 4 consists of 5 genes whose domains are in varied pattern without much uniformity. FASN has a number of fatty acid synthetase domains and there are records that cancer cells frequently exhibit a significant increase in over-expression and activity of fatty acid synthase (FASN). The transcription factor IRF4 is required for the generation of plasma cells. Available data suggest that IRF-4, by modulating the efficiency of the Fas-mediated death signal, is a novel participant in the regulation of lymphoid cell apoptosis.

II. In the second category, there are two clusters (Clusters 5 & 6), distinctly on two branches.

Genes of Cluster 5 mainly consist of domains like PAN_AP, KR domains, ZnF_C2HC domain and Ring finger and B-Box-type zinc finger with different binding functions. These domains are believed to play a role in binding mediators (e.g., membranes, other proteins or phospholipids). Zinc-binding motifs are stable structures, and they rarely undergo conformational changes upon binding their target. Znf-containing proteins function in gene transcription, translation, mRNA trafficking, cytoskeleton organization, cell adhesion, protein folding, chromatin remodeling etc. Besides, there are myc-n, Ig and BCL domains that influence cell proliferation and apoptosis.

Cluster 6 is a small cluster with 3 genes namely MYEOV, CD9, BCL2L14 all of which, when dys-regulated promote mitotic progression and prevent apoptosis.

III. In the third category (cluster 7), there is a predominance of IG domains in combination with Cyclin domains and tumor suppressor related domains.

IG domains: The Ig monomer is a “Y”-shaped molecule that consists of two identical heavy chains and two identical light chains connected by disulfide bonds. Each chain is composed of structural domains called Ig domains.

Under normal conditions the immune response usually activates many B cell clones resulting in heterogeneous antibodies. On the contrary B cell tumors provide source of homogenous antibodies. As such, in Multiple Myeloma, cancer of B cells outgrows other cells of the marrow and results in homogenous antibodies (monoclonal antibodies).

Cyclin domains: Cyclins are a family of proteins involved in the progression of cells through the cell cycle and regulation of cyclin dependent kinases (CDKs). When dysregulated expression of cyclin D1, D2, or D3 occurs in tumors, it may render the cells more susceptible to proliferative stimuli.

In addition to the Ig and cyclin domains, there are other domains in this gene cluster like TyrKcTyrosine kinase, catalytic domain and P53 domains which play a role in a multitude of cellular processes, including division, proliferation, apoptosis and differentiation.

Patterns exhibited by the protein sequences and their significance:

In the Myeloma genes analyzed in the present study for patterns, we could obtain interesting correlations. The patterns detected were present in many other diseases, especially cancers indicating that the presence of such patterns can serve as biomarkers for detecting cancers.

The following is the list of pattern exhibited by genes and corresponding diseases

LTA- multiple aneoplasia type IIA (MEN2A) LIG4- immunodeficiency diseases PTEN- Fanconi anemia BCL5- breast cancer, Fanconi anemia IL6- systemic juvenile rheumatoid arthritis MMSET- Other tumor suppressor genes. FASN- Huntington disease, myelodysplastic syndrome (MDS), squamous cell carcinoma antigen TNFAIP2- childhood pre-B acute lymphoblastic leukemia, small cell lung carcinomas BCL7A- B-cell non-Hodgkin lymphoma, ovarian cancer, breast, lung, colon, pancreatic and ovarian cancers C-myc- acute leukemias, oncogenesis, a variety of hematopoietic tumors HGF- Overexpressed in tumor cells lines FGFR3- Bladder cancer, multiple Myeloma skeletal dysplasia, colorectal tumors. TP53 - mutated or inactivated in about 60% of cancers.

Protein-to-protein associations and functional partners:

Information about interactions that take place among the important disease genes and their so called functional partners improves the overall understanding of diseases and can provide a basis for new therapeutic approaches. These functional partners could very well be the targets of new therapies too, where therapies face short comings in targeting the actual disease proteins.

In the present investigation the functional partners of each gene were detected by special tools and the interactions that exist between them are studied in order to detect their roles in MM. For e.g. CCND1 gene encodes for cyclin proteins important for a temporal coordination of each mitotic event. These cyclins forms a complex with CDK4 or CDK6 whose activity is required for cell cycle G1/S transition. CCND1 protein is also shown to interact with tumor suppressor protein RB and is regulated positively by it. Mutations, amplification and overexpression of CCND1 which alters cell cycle progression may contribute to tumorigenesis.

Figure 3

Further, it is interesting to note that not only most of the Myeloma genes had their own unique functional partners but also they were interacting with other myeloma genes. For e.g., MYEOV gene, RB1, P15, MMSET and CDK4 have CCND1, as their functional partner. Similarly, MMSET also acts in association with FGFR3. However, for 6 genes in the present study, there were no partners recorded in the set parameters.

Detection of Tandem repeats

At the early stage of development of some cancers, microsatellites are believed to change their length. Such microsatellites are useful as markers for early cancer detection. Mutations in DNA-repair genes cause microsatellites to get longer or shorter, a phenomenon called microsatellite instability (MSI)12. MSI is associated with defective DNA mismatch repair in various human malignancies13. In view of their role in detection of diseases, a cluster-wise analysis of Simple Sequence Repeats of the myeloma genes was taken up.

Results reveal that all the genes except CD19 show the presence of tandem repeats as di-, tri-, tetra- and/or pentamers. Frequencies are represented in the form of graphs. It is evident from the graphs that dimers were more frequent in occurrence.

Figure 4

SSR effects in coding regions on phenotypes have been extensively studied only in human diseases, revealing abundant evidence in cancers and neuronal disorders 16.

Analysis of available DNA sequences of human, mouse, worm (Caenorhabditis elegans), and yeast genomes shows that the distribution functions of all possible dimeric SSRs are exponential in coding DNA6. The potential size expansion of di- or tetranucleotide SSRs at the 3' and 5' regions and at introns could lead to disruption of the original protein and/or formation of new genes by frame shift2, 11.

Dinucleotide repeated sequences are preferential sites for recombination because of their high affinity for recombination enzymes4. Some SSR sequences may influence recombination directly by their effects on DNA structure.

SSR can affect enzymes controlling cell cycles. For instance, the human CHK1 gene has a role in controlling cell cycle progression, and its coding region contains an (A)9 tract5 that is a potential site of mutations in tumors with SSR instability3. Alterations in the CHK1 gene in human colon and endometrial cancers were associated with the presence of a high degree of poly (A) tract instability.

About 15% of sporadic colorectal cancers, as well as cancers at several other sites, show SSR instability1. The progressive accumulation of SSR instability may also contribute to gastric cancer development10. It has been shown that 14 neurological disorders result from the expansion of unstable trinucleotide repeats.

The demonstrated widespread non randomness, selectivity, and other various patterns displayed by repeated elements, call for special attention at all stages of using this class of markers in all the fields, from designing of the experiments, to data analysis and interpretation15.

During the further analysis of the tandem repeats we have come up with interesting observations like the excessive presence of dimers in connection with the occurrence of recombination events that take place during the progress of multiple myeloma.