Direct Reprogramming of Erythrocytes to Renal Nephron Cells Using Transcription Factor Combinations with the Implementation of Soluble Factors and Extracellular Matrix Proteins and Peptides Within the Respective Synthetic Scaffold

By SHIVAM AGARWAL and NABEEL QURYSHI, Pasadena, USA

The number of willing kidney donors is far lower than the number of patients in need of a kidney transplant, posing a major healthcare predicament. Studies to counter this dilemma have experimented with the process of somatic cell nuclear transfer, though with low consistency levels. Direct reprogramming can facilitate the transformation of various somatic and stem cells into a desired target cell type by forcing the experimental cell to use specific transcription factors. Several public bioinformatic databases such as Amazonia, NCBI gene, BioCarta, Pathway Commons and more were used for computational / microarray studies for the transformation of erythrocytes to renal nephron cells. Promising transcription factors, soluble factors (synthetic scaffold prerequisite), and ECM proteins and peptides (synthetic scaffold ingredient) were tested quantitatively to evaluate their known functions and individuality to the kidney. In order to prevent immune rejection of our renal cells, experimentation involving ECM peptides and genetic variations of ECM proteins was conducted to eliminate involvement of biological strains from different organisms (mice, rats, etc.). By compiling our data from three phases, we were able to generate an outlook of renal direct reprogramming efforts including specific combinations of biological modules.
 
1        Introduction

Figure 1: As a graphical aid, this graph [1] illustrates the prevalent biomedical roadblock of Kidney transplant failure and premature death on the waiting list. Since 1990, the number of people on the waiting list for a Kidney transplant has grown sharply, while the number of transplants has increased only slightly.
 
According to the National Kidney Foundation in 2015, there are currently 123,193 people in the US who are waiting for lifesaving organ transplants. Of these, 101,662 need kidney transplants. In 2014, 4270 patients died while waiting for a kidney transplant and another 3,617 people became too sick to receive a transplant kidney. The fact is that the demand for kidney transplants is high, however, inversely, human kidney donors are highly limited. Moreover, if the transplant is successful, in order to prevent immune rejection of the transplanted organ, the recipient must then consume immunosuppressant drugs that then cause negative side effects to the patient such as a higher risk for cancer, exposure to potential mutagens, osteoporosis, or hyperglycemia. One way to avoid these problems of a kidney transplant would be to bioengineer a kidney made from the recipient’s own adult somatic cells. Using this method, the grown kidney would be patient specific, have a 100% blood type match, identical lymphocytotoxic crossmatch (negative), and the exact number of HLA antigens based on tissue typing. With the implementation of an organ explicitly suited to the patient, it gets rid of the need for donors as well as immunosuppressant drugs.

            This paper considers researching the direct reprogramming of an erythrocyte into a kidney cell, (bypassing the induced pluripotent stem cell state) by electing a group of transcription factors that internally reside in nephron cells and then harvesting these newly differentiated cells using the correct synthetic scaffold/microenvironment with the implementation of ECM peptides within the extracellular matrix. As our main objective is to construct a full blueprint for the bioengineering of a nephron with a patient's own red blood cells, we focus on the the full cellular components of a nephron, the basic functional unit of the kidney.

Most kidney diseases directly attack the nephrons, causing them to lose their filtering capacity slowly tearing away at both kidney lobes. Every kidney contains approximately 0.5 million to 1 million nephrons; they filter waste from the blood, expel that waste in the form of urine, and return water and nutrients to the bloodstream. Every nephron is made up of two main parts, the renal corpuscle and the renal tubule, as shown in Figure [2]. The renal corpuscle receives unfiltered blood through the glomerulus and filters it through the Bowman's capsule, which sends the waste liquid to the renal tubule, from which it is sent on to be expelled eventually as urine. The role of these parts of the kidney in membranous nephropathy, IgA nephropathy, and focal segmental glomerulosclerosis add to the importance of nephrons.

            Transcription factors, also called sequence specific DNA binding factors, are proteins that control the rate of transcription of genetic information from DNA to messenger RNA(mRNA), which will then be used to create the amino acid sequences and, eventually, the proteins the cell needs. Because they have DNA binding domains, transcription factors can attach to the specific enhancer regions of DNA and can assist the transcription of the gene it needs to express, allowing transcription factors to extensively control the regulation of genes and creation of specific proteins in the organism.

 Extracellular matrix peptides are short amino acid sequences in ECM proteins that direct cellular differentiation, cell adhesion, and intra-cell communication. These ECM proteins are located in the extracellular matrix, a complex mixture of carbohydrates and proteins that surrounds the cells of an organism. Because the ECM is made up of different degrees of elasticity, it has been shown that it has an influential role in the regulation of cell differentiation and gene expression. Mesenchymal stem cells that have been placed on more elastic, softer ECMs have been shown to differentiate into cells analogous to the target cells, with similar shape and transcription factor activity levels. Unfortunately, ECM proteins are one of the major causes of immune rejection in transplant organs. Although they are key in differentiating human induced pluripotent stem cells (hiPSCs), the proteins are usually isolated from other animals, such as mice and rats. Because our bodies' immune systems may reject foreign proteins, bioengineered kidneys with proteins from mice or rats are prone to be rejected after transplant. The short sequences of ECM peptides are potential alternatives having being synthesized in a laboratory. They have already been shown to have functional activity along with the plausibility of interacting with other cells similar to a full protein. Deplorably, very few existing studies regarding bioengineered renal membranes have explored ECM peptide usage in membranous bioactive nephron units.

Currently, the reprogramming of cells to a different cell type can only be done through either somatic cell nuclear transfer (SCNT), a method with narrow flexibility as it depends on certain breeds of donor cells, or by registering the construction of certain transcription factors. In regard to past similar experiments based on direct cell reprogramming, bioengineering researchers converted fibroblasts into muscle cells by employing transcription factors and embryonic stem cells in 1980. However, they later found that only certain cells were capable of converting into muscle cells, hindering the medical implications to waiting list patients. Other experiments up to 2008 have followed this train of thought by only using transcription factors, isolating neurons and pancreatic cells as their primary target iPSC. However, much of the experimentation ended with incompletely or faultily differentiated cells. Even though direct reprogramming of adult cells into other cell types is possible as shown, the process is still fairly alien to us and is yet to be perfected before it can be commonplace in regenerative medicine. Specifically, there is controversy over the implementation of cancerous transcription factors with reprogramming a cell into an iPSC state. Although our paper applies the usage transforming one cell type directly into another without the need of an iPSC middle man and therefore avoids the potential mutagenic element,  we still investigate  normal transcription factor functions. Hopefully, direct reprogramming could feasibly become a hub for creating patient specific, non rejected, fully differentiated cells.

            In response to the gaps present in both the process of direct reprogramming itself in addition to the direct implementation of ECM peptides into the extracellular matrix, therefore substituting ECM proteins, (an ersatz biological component from mice and rats), we decided to tackle the obstacles inhibiting the discharge of direct reprogramming in regenerative medicine by developing a bioinformatic method of selecting transcription factors and protein sequences for bioengineering the main cellular functional components of a nephron.

This process consisted of three stages. The first phase was researching and selecting the premier transcription factors needed for the transformation of a red blood cell into a renal tubule cell. After finding these transcription factors, we then found their normal cellular functions, other cell types that express the specific transcription factor, and potential relations to malignant elements. Next, we developed a synthetic scaffold in order to completely differentiate the transformed cell into a kidney cell. We accomplish this by selecting the appropriate soluble factors that are evident in our specific nephron microenvironment. Subsequently, we researched what ECM protein would assist the created synthetic scaffold best for the differentiation process. We then found the specific ECM peptide amino acid sequence that would have functional activity and theoretically replace the ECM protein.

2         Procedure and Methods          
The methodology of this project lies in three phases:
Phase 1: Transcription Factor Research and Selection:
During the process of achieving a method for direct programming, the first step was researching the best transcription factors to use in order to convert an erythrocyte into a general kidney cell. To begin with, we found which ones were the most important transcription factors of kidney cells using past scientific studies in which kidney transcription factors were the main topic of study. This data was then inputted into a simple data table we had set up. After finding these premier transcription factors, we created a separate table of 10 transcription factors with their specific abbreviations  that described the other cell types that express those specific transcription factors, their main cellular functions, and whether there were any relations to malignant cells like cancer. During the selection process, we had to make sure that the chosen transcription factor combination was unique and  specific to the kidney. Otherwise, if the chosen transcription factors was very common to another cell type, the conversion process could be led astray. When researching through Amazonia, the microarray database, we also had to make sure that the transcription factor was expressed to the highest amount by the kidney, or else the problem stated before might occur.  If a certain transcription factor was expressed by many tissue types, we didn’t consider that transcription factor in our process. To accomplish this, we first averaged all of the kidney signal samples of a graph of a selected transcription factor. After this, we took 90% of that average and compared it to the samples of the other tissues in the graphs given by the bioinformatics database. The tissues which were equal to or higher than the 90% of the average were written down to note the common tissues that make similar amounts of the selected transcription factor. After doing this for all of the graphs of each transcription factor, we were able to find the amounts of transcription factors produced by the tissues and which transcription factors were the most unique to the kidney.

In order to determine whether the selected transcription factor was associated with cancerous and other negative implications, we used a gene database (NCBI) to find whether the transcription factor had any diseases associated with it. In addition, we used other such databases to find out more about the signaling pathways of our selected transcription factors. During this, we examined the information for any potential, undesirable events that might be triggered if a cell does produce that transcription factor.

After going through many databases and selecting the best 10 transcription factors that succeeded our requirements, we went through each of the 10 transcription factors and looked at certain traits. The transcription factors that were already produced by red blood cells were looked upon favorably as they were the ones which wouldn’t need to be forced upon the cell. After going through another rigorous analysis of going through possible disease associations, its commonality to other cell types, and how much of it was being created by the kidney tissue, we chose a combination of two transcription factors that we found would work the best.

Phase 2: Synthetic scaffold / microenvironment involvement in conjunction with soluble factors:
In order to fully differentiate our newly reprogrammed cells, we turned to modeling a synthetic scaffold, implementing soluble factors to culture and compose our cell harvesting media. Previous studies have investigated the transformation of pluripotent stem cells to specific nephron cell types, but the techniques have been inefficient, resulting in up to 30% of new cells undifferentiated and unresponsive.

The initial stage of this phase was to explore key publications of research in outputting specific Nephron cell types (primarily employing ipsc’s as starting cells) by using specific soluble factors. For example, three groups (Kramer et al., 2005; Kobayashi et al., 2005; and Morizane et al., 2009) were part of our soluble factor study and instrumental in our renal tubule endeavors, having made the cells using one, two, or no soluble factors. In response to our publication research, we also have identified two additional points of interest. Together, we investigated the non use of soluble factors and the potential benefits soluble factors could make evident in the (Kramer et al., 2005) publication. The latter of the two, although not directly related to specific nephron cells, involved the research of soluble factors necessary for general in vivo embryonic Kidney development.

After stage one, we used four different bioinformatic databases to learn more about our amassed soluble factors (The NCBI gene database, Pathway Commons, Embryonic and induced pluripotent stem cells and Lineage-specific markers, Bio Carta). Our main focus of study was the function of the factors, components it either inhibits or promotes, and its connection with other transcription factors. For example, some of the questions we sought to answer were: does the factor regulate proliferation or apoptosis, does the factor regulate differentiation, what other factors our specific factor directly regulate, and which ones does it more distantly regulate?

The ultimate stage of phase two, was to look at how different and how similar the soluble factors are. If any two soluble factors present three of the same functions, we label them as similarly acting soluble factors. We then locate previously used combinations of soluble factors, and by using our data collected above, we form potential new combinations and substitutions. By using different combinations of soluble factors, we may improve upon the current nephron cell  differentiation protocol and possibly improve the creation of nephron cells and nephron precursors.
 
Stage 3: Implementing the Extracellular Matrix to drive Nephron Cell Differentiation
This portion of the work required us to research which extracellular matrix polypeptide sequence would work best to fully differentiate direct reprogrammed cells to specific nephron cells. To effectively discern the cells, we combined the appropriate soluble factors in their media and the substrate they are grown on. Differentiating cells on certain ECM proteins may produce nephron cells more efficiently, a potential benefit to a kidney synthetic scaffold.

In order to interact with the ECM proteins, the cells need integrins, specific cell receptors on the surface of their cells. The first endeavor of this phase was to explore the specific alpha and beta subunit combinations of the integrin heterodimers, determining what ECM proteins they can bind. We then used the Amazonia microarray database to focus on the expression of the integrin subunits in our specific nephron cells (Renal tubule, Renal Corpuscle, Bowman's capsule cells, podocytes in the Glomerulus). Using the data collected from our specific nephron cells, we are able to calculate the average “signal” from our samples and therefore reveal the average expression (of subunits) in our embryonic bioactive units.

Looking back at our collected “signal” data, we set an expression level threshold of 100 that results in a list of several highly expressed integrin subunits as well as marking the integrin heterodimers that are highly expressed in both their alpha and beta integrin subunits. Applying the integrin data, we were able to inquire 3 main questions regarding ECM proteins. Which ECM proteins are bound by the highly expressed integrins? Which ECM proteins are bound by multiple highly expressed integrins? Are the marked ECM proteins also highly expressed in the kidney during development?

Following the integrin study, we repeated the Amazonia database undertaking with ECM proteins. The multiple subunits of the matrix framework and highly expressed ECM proteins were conjointly implicated.
As stated in the introduction, ECM proteins are usually isolated from other animals, such as mice and rats and are prone to be rejected after transplant. Our next step was to conduct multiple literature searches regarding specific ECM peptide sequences (an alternative to ECM proteins, having been synthesized in a laboratory and have shown functional activity) basing our examination on our flagged, highly expressed ECM proteins. This stage of phase 3 allowed us to find what ECM peptides as well as clinical genetic variations that may work well for developing a synthetic scaffold with the future goal of bioengineering a kidney. Aside from literature searches, our experimentation included bioinformatics endeavors using the NCBI protein database and the Harvard University Pep Bank. Our extracellular matrix endeavor involved looking at ECM peptides specific to an ECM protein or common among many.

3         Results:

Results from Phase 1: After reviewing the 10 dominant transcription factors exceedingly involved in the direct reprogramming of red blood cells to general kidney cells, we tailored the list down to four transcription factors. Our parameters of judging included any potential harmful implications the transcription factor had, and its overall kidney specific microarray signal. According to the statistical probe and chip compilations of Amazonia, the microarray result database we were able to generate the following combination: PPARA (Peroxisome proliferator-activated receptor alpha), HNF1(HNF1 homeobox A), SREBP (LIM domain and actin binding 1), and HNF4A (Hepatocyte nuclear factor 4, alpha). Out of 4 available histograms, 3 indicated a high level of individuality towards the kidney in HNF1 testing. HNF1 is associated with insulin signaling pathways. After exploring the microarray results of PPARA, 5 out of 10 histograms indicated the specificity towards the kidney. PPARA affects the expression of target genes involved in cell proliferation, cell differentiation and in immune and inflammatory responses. Although SREBP indicated a one out of 3 histogram specificity match, its main function of Sterol regulatory element binding proteins and enzyme regulation persuaded us to involve it in renal direct reprogramming efforts. For HNF4A, 3 out of 9 histograms matched the cell type distinction test. HNF4A regulates the expression of several hepatic genes and is a major player in kidney development.

Results from Phase 2: The second step of our experiment was to model a synthetic scaffold by implementing soluble factors to culture and compose our cell harvesting media. After going through many published research papers about the study of soluble factors, we were able to find a group of soluble factors that could be used to completely differentiate a stem cell. From these published research papers, we found that the combination of the Transforming Growth factors alpha(TGFA), transforming growth factor beta(TGFB), insulin like growth factor(IGF1), and the platelet derived growth factor(PDGFA) are the best soluble factors for general kidney development. This part of the research we were able to derive from the publications we stated above. After we found this group, we put these soluble factors through a rigorous search using four microarray bioinformatics databases known as the NCBI gene database, R & D systems, and Pathway Commons. After going through each soluble factor individually, we determined the best soluble factor combination for the differentiation of Renal Tubule Cells to be Retinoid Acid (RARA) and Hepatocyte Growth Factor (HGF). We determined this by examining whether that soluble factor regulated proliferation, differentiation, cell development, and its other key functions. Retinoic Acid highly regulated differentiation, proliferation, and development. In addition, it is very similar to Activin A, which has been used in the past to differentiate cells successfully. Hepatocyte Growth Factor was chosen because though it doesn’t regulate development, it highly regulated proliferation of cells and development, as well as stimulating cell motility and tissue regeneration. HGF has been shown to help jumpstart the cell creation process in past research and was shown to be a suitable choice for successful cell proliferation and development. This part of the research was mostly qualitative.
 
Results from Phase 3: The next step was to distinguish the correct integrin heterodimer subunits to determine the most effective extracellular matrix proteins involved in maintaining a synthetic scaffold. After compiling a list of 8 specific combinations of integrin subunits, we were able to distinguish 8 subunits using a statistical threshold of 100 microarray signal. The derived subunits were as follows: ɑ2, 𝛃1, ɑ3, ɑ8, ɑ10, ɑV, 𝛃5. Next we decided which integrin heterodimers were expressed in both subunits. The following details this step: ɑ2 + 𝛃1, ɑ3 + 𝛃1, ɑ10 + 𝛃1. Subsequently we analyzed the corresponding ECM protein bound by the highly expressed integrin heterodimers. Our work is as follows:
1.     A2 + B1: Laminin -111, Laminin -211, Collagen 1, Collagen 4 (IV)
2.     A3 + B1: Laminin 332, Laminin 511, Laminin 521, Collagen 4(IV), Fibronectin
3.     A10 + B1: Collagen 1, Collagen 4 (IV)
 
The red proteins indicate ECM proteins bound two or more integrins. Next, using Collagen 1, and Collagen 4 as our candidates we dug deeper into the inner workings of the protein, exposing the specificity of the kidney in each subunit. Collagen 1 contained 2 subunits, its average microarray expression over 100 (statistical threshold). Collagen 4 contained 6 individualized subunits its average renal microarray expression also over 100.
The next milestone of phase 3 was conducting the ECM peptide experimentation. In order to conduct our clinical Homo sapien genetic sequence variations we consulted the NCBI protein database. For the ECM peptide component we sought counsel from the Massachusetts general hospital, Harvard University Pep (peptide) Bank. Our first round of experimentation involved Collagen 4, a protein made up of alpha subunits. Our first step was to query the clinical variations for Collagen 4 using NCBI. Upon search we found a match, fitting our parameters of homo sapien, and alpha subunits: Collagen type IV alpha 5 transcript variant 2 [Homo sapiens]. By finding the specific clinical variation we were able to view the sequence in FASTA mode. The sequence is as follows:
>gi|703556897|gb|AIW39922.1| collagen type IV alpha 5 transcript variant 2 [Homo sapiens]
MKLRGVSLAAGLFLLALSLWGQPAEAAACYGCSPGSKCDCSGIKGEKGERGFPGLEGHPGLPGFPGPEGPPGPRGQKGDDGIPGPPGPKGIRGPPGLPGFPGTPGLPGMPGHDGAPGPQGIPGCNGTKGERGFPGSPGFPGLQGPPGPPGIPGMKGEPGSIIMSSLPGPKGNPGYPGPPGIQGLPGPTGIPGPIGPPGPPGLMGPPGPPGLPGPKGNMGLNFQGPKGEKGEQGLQGPPGPPGQISEQKRPIDVEFQKGDQGLPGDRGPPGPPGIRGPPGPPGGEKGEKGEQGEPGKRGKPGKDGENGQPGIPGLPGDPGYPGEPGRDGEKGQKGDTGPPGPPGLVIPRPGTGITIGEKGNIGLPGLPGEKGERGFPGIQGPPGLPGPPGAAVMGPPGPPGFPGERGQKGDEGPPGISIPGPPGLDGQPGAPGLPGPPGPAGPHIPPSDEICEPGPPGPPGSPGDKGLQGEQGVKGDKGDTCFNCIGTGISGPPGQPGLPGLPGPPGSLGFPGQKGEKGQAGATGPKGLPGIPGAPGAPGFPGSKGEPGDILTFPGMKGDKGELGSPGAPGLPGLPGTPGQDGLPGLPGPKGEPGGITFKGERGPPGNPGLPGLPGNIGPMGPPGFGPPGPVGEKGIQGVAGNPGQPGIPGPKGDPGQTITQPGKPGLPGNPGRDGDVGLPGDPGLPGQPGLPGIPGSKGE
PGIPGIGLPGPPGPKGFPGIPGPPGAPGTPGRIGLEGPPGPPGFPGPKGEPGFALPGPPGPPGLPGFKGALGPKGDRGFPGPPGPPGRTGLDGLPGPKGDVGPNGQPGPMGPPGLPGIGVQGPPGPPGIPGPIGQPGLHGIPGEKGDPGPPGLDVPGPPGERGSPGIPGAPGPIGPPGSPGLPGKAGASGFPGTKGEMGMMGPPGPPGPLGIPGRSGVPGLKGDDGLQGQPGLPGPTGEKGSKGEPGLPGPPGPMDPNLLGSKGEKGEPGLPGIPGVSGPKGYQGLPGDPGQPGLSGQPGLPGPPGPKGNPGLPGQPGLIGPPGLKGTIGDMGFPGPQGVEGPPGPSGVPGQPGSPGLPGQKGDKGDPGISSIGLPGLPGPKGEPGLPGYPGNPGIKGSVGDPGLPGLPGTPGAKGQPGLPGFPGTPGPPGPKGISGPPGNPGLPGEPGPVGGGGHPGQPGPPGEKGKPGQDGIPGPAGQKGEPGQPGFGNPGPPGLPGLSGQKGDGGLPGIPGNPGLPGPKGEPGFHGFPGVQGPALEGPKGNPGPQGPPGRPGPTGFQGLPGPEGPPGLPGNGGIKGEKGNPGQPGLPGLPGLKGDQGPPGLQGNPGRPGLNGMKGDPGLPGVPGFPGMKGPSGVPGSAGPEGEPGLIGPPGPPGLPGPSGQSIIIKGDAGPPGIPGQPGLKGLPGPQGPQGLPGPTG
PPGDPGRNGLPGFDGAGGRKGDPGLPGQPGTRGLDGPPGPDGLQGPPGPPGTSSVAHGFLITRHSQTTDAPQCPQGTLQVYEGFSLLYVQGNKRAHGQDLGTAGSCLRRFSTMPFMFNINNVCNFASRNDYSYWLSTPEPMPMSMQPLKGQSIQPFISRCAVCEAPAVVIAVHSQTIQIPHCPQGWDSLWIGYSFMMHTSAGAEGSGQALASPGSCLEEFRSAPFIECHGRGTCNYYNSYSFWLATVDVSDMFSKPQSETLKAGDLRTRISRCQVCMKRT
Subsequently we explored the BLAST component of NCBI to find the differences/similarities between our variant sequence and the original. In order to accomplish this we used the protein-protein BLAST algorithm. A comprehensive text document detailing our findings is available upon request.
Next we queried Collagen IV using Harvard University’s Pep (peptide) Bank. This specific database classifies its peptides on a 0-1 scale (0=having negative implications, 1 no negative
Figure 2: This graphical aide [4] is an illustration of the Query search results for Collagen IV (4) using the Massachusetts General Hospital, Harvard University Pep (peptide) Bank.
 
implications). Entries are automatically classified by category (related to Cancer, CVD, DM, APO, ANG, MI, BD(binding data)) Our experimentation with Collagen 4 generated an ECM peptide named RGDS of unit 0.49. The data received from this database is more extensive and is available upon request.
Unlike Collagen 4, Collagen 1 was separated into its beta and alpha subunits for the NCBI phase. The two subunit experimental results are as follows:
COL1A1: COL1A1, Partial [Homo Sapiens]
>gi|514252354|gb|AGO43920.1| COL1A1, partial [Homo sapiens]
GEAGPQGPRGSEGPQGVRGEPGPPGPASAAGPA
COL1A2: COL1A2, Partial [Homo Sapiens]
>gi|180889|gb|AAB59384.1| COL1A2, partial [Homo sapiens]
EIDNPG
Both subunits also have their respective BLAST analysis screenings and are available upon request. After query with the Center for Molecular Imaging Pep Bank (Harvard), we found a match. The peptide name is VWTLP. More comprehensive data is available upon request (phage display, laboratory experimentation etc.)

4         Analysis

 Going into this project, we had expected to simply create a single phase analyzation process in which we could reconstruct any feasibly attainable cell into a different somatic cell type with the sole use of transcription factors as the differentiating medium.. However, after browsing through the many publications that had been previously concluded in this expansive area of regenerative medicine, we realized that this sole process had already been looked into and researched thoroughly. However, the process that these many publications had researched about were not completely consistent and were left with inconclusive and inefficient results. Based off this, we continued our research on transcription factor differentiation, but tried to look into a more consistent experimental procedure that would thoroughly differentiate a blood cell into specific nephron cells.

            In the first phase, the focus was primarily on researching and then selecting the premier transcription factors to start out the differentiation process to create the target cell type. By analyzing data points through the Amazonia database and examining the numerical microarray probe expressions of each transcription factor, we were able to find this premier combination and apply it to our experiment. Many of the publications we researched about used this strategy as well. However, the variance between them and our study is the use of  transcription factor differentiation as a prerequisite step in differentiating and proliferating the target cell type. Because of their lack of differentiation variability, there is a direct relationship between it and the lack of consistent  biological outputs.   
   
Following this, in the second phase, we evaluated soluble factor differentiation to try and create the best overall conditions to create renal tubule and real nephron cells. This is important because we used both soluble factor combination as well as transcription factor combinations to fully differentiate the cell. No other experiments have used both strategies to fully differentiate from one cell to another. Because we use both types of differentiation methods, our theoretical consistency of cell differentiation and proliferation will be much higher.

            Lastly, in the third phase, we first found the best ECM protein to mimic the most similar microenvironment for renal tubule cells. After finding this, we found the best ECM peptide sequences correspondent of the protein. Because we found peptide sequences (which show functional activity), which are much smaller than proteins, there is less of a likelihood for immune rejection of the transplanted cells. We also found laboratory derived human genetic variations of the ECM proteins. Because we were able to find the human genetic variations of the ECM peptides and proteins, we were able to fully conclude that the ECM protein and peptides we found were right and could be used to create the best microenvironment to mimic a kidney cell’s surroundings. The impact of this last step is to complete the cell proliferation process as the first two steps addressed differentiation while the last step completed the development process and will help synthesize a cell that will most similarly simulate a renal tubule cell. By using all three methods of transcription factors, soluble factors, and ECM peptides, we are ensured more consistent result of fully differentiated cells than that have been generated by experiments in the past.

5          Conclusion

Our work accomplished the three goals that were set out. We found the most beneficial transcription factors, the most stimulating soluble factors, and discovered ECM peptides and clinical variations to their respective ECM proteins and integrin heterodimers. As a result of this work, the concept of direct reprogramming especially in renal efforts will be more effective, ultimately setting the stage for the bioengineering of a full nephron. Moreover, because this work is broken down into specific phases, future somatic cell reprogramming experimentations will be able to follow a complete bioinformatic experimental procedure.
Our conclusions are supported by the basis of bioinformatics. As we primarily focused on querying data, and not the process of laboratory procedure itself, the work of experimenters before us is validation of our quantitative data. The bulk of our judging of transcription factors, soluble factors, and ECM proteins relied solely on microarray signals derived from previous laboratory research. For example, all data collected during the phase one transcription factor research relied on the prior data collected from different probes and chips that we viewed by looking at the histogram(s).

Moreover, as a major part of our procedure was on the basis of past experimentation efforts and research, our work is supported in conjunction with prior literature rather than relying on the report alone.
However, our methods are certainly not infallible. A majority of our experimental procedure involved us discerning a specific combination of biological components (i.e. transcription factors, soluble factors, and ECM proteins) by their known functions. Although we used deductive reasoning through accredited bioinformatics databases to distinguish our concluded combinations, the process was partially qualitative and therefore not validated by a numerical  value.

Experiments that would have to be performed to refine the calculations would necessitate the experimental testing of our theoretical results. As our paper details our bioinformatics endeavors, taking our experimental outline and applying it to laboratory techniques would support or refute our theoretical conclusions. By culturing directly reprogrammed cells using transcription factors in a synthetic scaffold with the involvement of ECM peptides/genetic variations of ECM proteins, one would be able to test our specific combination of biological elements against the outcome. The theoretical results in this paper could then be checked with the experimental results and statistical measures could potentially ensure significance. Likewise, in the future a computational model to fit/test proposed biological combinations could be created and referenced with the current theoretical results to check for accuracy.

This research aimed to be more valid than any other currently published bioinformatic results. Our future for this work is to research the validity of small molecule / chemical compound direct reprogramming. By comparing the process of transcription factors and small molecule approaches to somatic cell direct reprogramming, we found that the future of genetic bioengineering is promising. Through this paper, we attempted to patch up many of the holes that inhibit accurate and efficient direct reprogramming experimentations. This prompts and stimulates the eventual bioengineering of first a nephron cell and ultimately an entire functional kidney.