Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series
Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data
Detecting higher regulated genes in the direction of our interest is still a challenging task. Here, we used a scalable computational approach to unbiasedly predict candidate genes for regulators of important transcriptional moments by searching the whole genome. We illustrated our alignment with the case of Foxp3, a master regulator of early regulatory T cells (Tregs) in individuals. Although the targets of Foxp3 were identified, the regulators in the higher direction were not yet identified. Following our method, we selected five more optimal volunteers and performed experiments to confirm the concept. Then, three of the five applicants showed a significant effect on Foxp3 MRNK expression in different donors. This revealed the idea of a regulatory mechanism that regulates the transcriptional expression of FOXP3 in Tregs. Overall, at the genome level, this increased the accuracy of prediction of top regulated genes for the main genes representing attention. Paper September 28, 2020
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Understanding the apparatus hidden in the database of gene expression has fundamental significance for cell research. With the development of large-scale experimental layout, the identification of target genes has become a relatively simple task for us1, 2. However, the detection of regulatory genes that control or regulate the expression levels of such important genes is typically performed in the biolaboratory by trial and error, which actually requires a huge amount of time and resources. To this end, we applied and discovered an unbiased computational scaffold to accelerate the detection of regulatory genes that are highly positioned toward the transcriptional moment of promoter binding, which is an important transcription factor in the early cells of an individual.Introduction
In recent years, hig h-performance all genome measurement experiments have made it possible to extract a genetic control net from a larg e-scale transcription dataset 3, 4. 5, 6, 7., Especially in the gene network. The decisive moment for the output is that data from the time series can be used 8. One of the typical drawbacks of these methods is that the calculation is poor, so small scale. It cannot be used not only on a network but also on a genome scale. In fact, these methods usually solve the difficult task of determining the control of the gene in the N-gene x N gene network. In the work provided, this is considered as no n-absorbed of modeling, with the withdrawal of the possibility of a higher control gene towards the first selected gene that indicates specific precautions of all genomic gene. It is regarded as a lightweight contradiction of the high interaction output N × 1. In this way, our network reasoning alignment solves solved difficult problems. < SPAN> In recent years, hig h-performance all genome measurement experiments have made it possible to extract a gene control net from a larg e-scale transcription dataset 3, 4. The decisive moment for the output of the genetic network is that data from the time series can be used 8. One of the typical drawbacks of these methods is that the expandability of the calculation is poor. Therefore, it cannot be used not only on a small network but also on a genome scale. In fact, these methods usually solve the difficult task of determining the control of the gene in the N-gene x N gene network. In the work provided, this is considered as no n-absorbed of modeling, with the withdrawal of the possibility of a higher control gene towards the first selected gene that indicates specific precautions of all genomic gene. It is regarded as a lightweight contradiction of the high interaction output N × 1. In this way, our network reasoning alignment solves solved difficult problems. In recent years, hig h-performance all genome measurement experiments have made it possible to extract a genetic control net from a larg e-scale transfer dataset 3, 4. 5, 6, 7. The decisive moment for the output is that data from the time series can be used 8. One of the typical drawbacks of these methods is that the calculation is poor, so small scale. It cannot be used not only on a network but also on a genome scale. In fact, these methods usually solve the difficult task of determining the control of the gene in the N-gene x N gene network. In the work provided, this is regarded as no n-absorption of modeling, with the withdrawal of the possibility of a higher control gene towards the first selected gene that indicates specific precautions of all genomic gene. It is regarded as a lightweight contradiction of the high interaction output N × 1. In this way, our network reasoning alignment solves solved difficult problems.
Previously, we have adopted a calculator method of circuit engineering, identifying 15. 17, which identified a safe causal control relationship from chronological order. This method is used to determine the control net of a small Sirinnazuna 16 circus hour and the Hordeum Vulgare (Hordeum Vulgare) 17, the same or the highest as a highly advanced method of a certain number that covers the favor of past tests. The performance has been proven. Here, in order to determine the possibility of the adjustment gene at a higher position depending on the direction of the transfer factor that interests you, this method was extended and distinct as follows: 1) No n-digit system; Genome transfer data on the set; 3) Dynamic system with delay. At the technical level, our method is suitable for the former delayed linear system, and is a specific gene controlled by a specific gene in a min i-series limited transcriptome information. Stimulate you. The challenge is to avoid unnecessary adaptation, scaling alignment to all genome, and constitutes the complexity of the model with the causal relationship, and eventually the details needed to establish control.
The purpose of the research provided is to indicate that the method created based on the previous research 18 in the initial T cells of the individual (other advanced methods (other advanced methods))). See in EXILE 18 for comparison. Here, this method is described using a hig h-frequency set of transcription data from genomic scale with 1 9-year series 19 to obtain an applicant who controls the transfer point, which is the key to immune cells. These data was obtained from the similarity of individual initial immune cells, which is popular as CD4+CD25+Foxp3+control T cell (TREGS) 20. 21. Our task is to detect the main control factor of TREGS, that is, foxp3. TREG is 23, 24, 25, which controls immunological sel f-restoration of effector cells, causes immunological sel f-restoration, and maintains home. Treg is involved in all diseases, including autoimmune disease 26, 27, 28, cancer 29, 30, 31, infection 32, 33, neurodegent disease 34, 35, and 36. < Span> Previously, we have adopted a calculato r-like method of circuit engineering, identifying 15. 17, which identified a safe and consequential control relationship from chronological order. This method is used to determine the control net of a small Sirinnazuna 16 circus hour and the Hordeum Vulgare (Hordeum Vulgare) 17, the same or the highest as a highly advanced method of a certain number that covers the favor of past tests. The performance has been proven. Here, in order to determine the possibility of the adjustment gene at a higher position depending on the direction of the transfer factor that interests you, this method was extended and distinct as follows: 1) No n-digit system; Genome transfer data on the set; 3) Dynamic system with delay. At the technical level, our method is suitable for the former delayed linear system, and is a specific gene controlled by a specific gene in a min i-series limited transcriptome information. Stimulate you. The challenge is to avoid unnecessary adaptation, scaling alignment to all genome, and constitutes the complexity of the model with the causal relationship, and eventually the details needed to establish control.
The purpose of the research provided is to indicate that the method created based on the previous research 18 in the initial T cells of the individual (other advanced methods (other advanced methods))). See in EXILE 18 for comparison. Here, this method is described using a hig h-frequency set of transcription data from genomic scale with 1 9-year series 19 to obtain an applicant who controls the transfer point, which is the key to immune cells. These data was obtained from the similarity of individual initial immune cells, which is popular as CD4+CD25+Foxp3+control T cell (TREGS) 20. 21. Our task is to detect the main control factors of TREGS, that is, foxp3. TREG is 23, 24, 25, which controls immunological sel f-restoration of effector cells, causes immunological sel f-restoration, and maintains home. Treg is involved in all diseases, including autoimmune disease 26, 27, 28, cancer 29, 30, 31, infection 32, 33, neurodegent disease 34, 35, and 36. Previously, we have adopted a calculator method of circuit engineering, identifying 15. 17, which identified a safe causal control relationship from chronological order. This method is used to determine the control net of a small Sirinnazuna 16 circus hour and the Hordeum Vulgare (Hordeum Vulgare) 17, the same or the highest as a highly advanced method of a certain number that covers the favor of past tests. The performance has been proven. Here, in order to determine the possibility of the adjustment gene at a higher position depending on the direction of the transfer factor that interests you, this method was extended and distinct as follows: 1) No n-digit system; Genome transfer data on the set; 3) Dynamic system with delay. At the technical level, our method is suitable for the former delayed linear system, and is a specific gene controlled by a specific gene in a min i-series limited transcriptome information. Stimulate you. The challenge is to avoid unnecessary adaptation, scaling alignment to all genome, and constitutes the complexity of the model with the causal relationship, and eventually the details needed to establish control.
The purpose of the research provided is to indicate that the method created based on the previous research 18 in the initial T cells of the individual (other advanced methods (other advanced methods))). See in EXILE 18 for comparison. Here, this method is described using a hig h-frequency set of transcription data from genomic scale with 1 9-year series 19 to obtain an applicant who controls the transfer point, which is the key to immune cells. These data was obtained from the similarity of individual initial immune cells, which is popular as CD4+CD25+Foxp3+control T cell (TREGS) 20. 21. Our task is to detect the main control factor of TREGS, that is, foxp3. TREG is 23, 24, 25, which maintains immunological sel f-restoration, causing immunological sel f-restoration of effector cells. Treg is involved in all diseases, including autoimmune disease 26, 27, 28, cancer 29, 30, 31, infection 32, 33, neurodegent disease 34, 35, and 36.
Stressful research has led to the identification of FOXP3 targets 37, 38, 39 and the elucidation of genetic and epigenetic mechanisms 40, 42, 43, 44, 45 that regulate Foxp3 protein expression and strength. Most of the well-known upstream regulators of Foxp3 expression are believed to be co-regulated genes, for example, genes controlling interleukin (IL-2, IL-4, IL-6, etc.) and cell surface sensor (TGFB) signaling pathways 46. These genes generally regulate a vast number of targets far beyond FOXP3, which may actually cause significant unwanted effects beyond the target.
Identification of more specific regulators of Foxp3 head regulators may have crucial significance for the development of new immunotherapeutic drugs for autoimmune and other concurrent diseases. Targeting head regulators in other types of cells in other diseases, for example cancer, has also proven promising in 47, 48, 49 development projects. Foxp3 is a transcriptional moment of some Tregs, which plays a decreasing role in Treg development, for example, in their suppressive function. However, like other transcriptional sites, FOXP3 is intracellular and is not easy to affect by conventional methods. As a result, it is basically modified by genes higher than FOXP3, and it is necessary to examine whether it is possible to regulate FOXP3 expression by regulatory genes higher than FOXP3. Certain attention is being paid to identifying candidates for extracellular regulatory genes that may be applied as medicines for the development of promising treatments.
Identifying novel upstream FOXP3-regulated genes is challenging. A recent study based on human single-cell RNA-seq data with Tregs 50 only identified genes that may be co-expressed with FOXP3, but not upstream regulated genes. Indeed, with a very limited number of time points, it is difficult to establish causality, so simpler alternatives such as correlation, mutual information, and other basic statistics are often used 6, 51. These methods are primarily aimed at detecting gene co-expression, i. e. regulation, rather than causal interactions. Moreover, mathematical models of the dynamics of Tregs or other parts of the immune system are not useful for finding novel FOXP3-regulated genes. A recent study 61 addressed this problem experimentally by using CRISPR to test about 500 predefined nuclear factors to identify gene regulatory programs that promote or inhibit FOXP3 expression. Performing an unbiased experimental genome-wide screen for the presence of such regulatory genes in rare primary immune subsets, i. e. Tregs, is not yet possible with current experimental approaches and available resources 62. Therefore,
Results
In summary, this study shows that a simple dynamic model can efficiently (at the genomic level) and unbiasedly predict gene regulators upstream of a given target. Five genes were proposed that may regulate FOXP3 expression, three of which were subsequently experimentally confirmed. Our method showed high prediction accuracy at the genomic level and in human primary cells, which have a higher inter-individual heterogeneity compared to the mouse cell scenario.
The unbalanced approach to calculation screening used in this paper is based on a method of opening the roots into the technical field of dynamic system system identification. Here, an example using this tool is shown to predict the control gene of the Treg Foxp 3-master controller from the entire genome. This method can be applied to various types of tim e-series data from various organisms, including transcription, protro ohm, metabolom, and other data. Ideally, the time interval between the measurements should be constant for the time system in a row input data. Otherwise, for example, data may be interpolated.
In this paper, we used the published microchip time series of the transfer measurement of the isolated first human TREG19. The dataset consists of two different healthy donors TREGs stimulated by anti-CD3/-CD28/IL-2 at zero, measured at zero, and then sampled every 20 minutes. (Only transient 19th). Later, the expression of the transcription product was analyzed by the oligonucleotididiary of Affymetrix HG-U133 Plus 2. 0 (see link 19). The number of tim e-series points in 19 is already quite large, rarely in biological experiments, but is still limited from our engineering dynamic modeling.
As described in column 1 in Fig. 1A and described in the "Method" section, after the preliminary processing, correspond to the 7826 gene 13. The gene may be compatible with multiple transfer products, each with its own. Measured by a separate probe set. In the future, unless otherwise refused, it will be simple, so only the term "transfer product" will be used instead of "probe set".
Rice. 1: FOXP3-Hypothetical control gene is a causal modeling belt conveyor obtained upstream for subsequent experimental verification. < SPAN> The unbalanced approach of the calculation screening used in this paper is based on a method of opening the roots into the technical field of dynamic system identification. Here, an example using this tool is shown to predict the control gene of the Treg Foxp 3-master controller from the entire genome. This method can be applied to various types of tim e-series data from various organisms, including transcription, protro ohm, metabolom, and other data. Ideally, the time interval between the measurements should be constant for the time system in a row input data. Otherwise, for example, data may be interpolated.
In this paper, we used the published microchip time series of the transfer measurement of the isolated first human TREG19. The dataset consists of two different healthy donors TREGs stimulated by anti-CD3/-CD28/IL-2 at zero, measured at zero, and then sampled every 20 minutes. (Only transient 19th). Later, the expression of the transcription product was analyzed by the oligonucleotididiary of Affymetrix HG-U133 Plus 2. 0 (see link 19). The number of tim e-series points in 19 is already quite large, rarely in biological experiments, but is still limited from our engineering dynamic modeling.
As shown in column 1 in Fig. 1A and described in the "Method" section, after preliminary processing, responding to 7826 gene 13. The gene may be compatible with multiple transfer products, respectively. It is measured with a separate probe set. In the future, unless otherwise refused, it will be simple, so only the term "transfer product" will be used instead of "probe set".
Rice. 1: FOXP3-Hypothetical control gene is a causal modeling belt conveyor obtained upstream for subsequent experimental verification. The unbalanced approach to calculation screening used in this paper is based on a method of opening the roots into the technical field of dynamic system system identification. Here, an example using this tool is shown to predict the control gene of the Treg Foxp 3-master controller from the entire genome. This method can be applied to various types of tim e-series data from various organisms, including transcription, protros, metabolom, and other data. Ideally, the time interval between the measurements should be constant for the time system in a row input data. Otherwise, for example, data may be interpolated.Rice. 1: FOXP3-Hypothetical control gene is a causal modeling belt conveyor obtained upstream for subsequent experimental verification.
A Beginning with a live transcriptome data, an overview of all the milestones that can be calculated, which is the final ranking of the gene that seems to control the foxp3 expression in native human to legs. Abbreviation ACT means activator, and REP means repressor. The third row reddish signal A and B show the start input signal and the corresponding delay version. B The number of models that have achieved more than the predetermined conformity. View of one mode: A group of models that shows a sharp decrease in fitness evaluation, and a significant huge group that shows a almost straight reduction in fitness evaluation. The buil d-up on the upper right of the panel shows buil d-ups in a model with the first place in the ranking. c Outline of the predicted candidate genes for the upstream control of Foxp3 tested within the test tube.
Rice. 1: FOXP3-Hypothetical control gene is a causal modeling belt conveyor obtained upstream for subsequent experimental verification. The unbalanced approach to calculation screening used in this paper is based on a method of opening the roots into the technical field of dynamic system system identification. Here, an example using this tool is shown to predict the control gene of the Treg Foxp 3-master controller from the entire genome. This method can be applied to various types of tim e-series data from various organisms, including transcription, protros, metabolom, and other data. Ideally, the time interval between the measurements should be constant for the time system in a row input data. Otherwise, for example, data may be interpolated.& amp; amp; gt; & amp; gt; (t) & amp; gt; = acdot
& amp; amp; gt; & amp; amp; gt; \, (t)+bcdot transcript (T). $k.
The wrong leaf in the equation gives a differentiation time of Foxp3 MRNK's concentration (that is, change rate). The first member on the right side of the host corresponds to the decomposition. In the case of automatic control, there is still a possibility of reflecting the negative or positive revolutionary relevance of foxp3 itself. The second member outlines the formation of Foxp3 gene by another transfer product. Considering the simplicity of the model, the two parameters A and B suggest that they have the highest information correlation, thus they are the fastest, abstract, and no material meaning. 。 Based on this comparison, we found the characteristics A and B that respects the most. This procedure has been repeated for all 13. 601 transcripts. Regarding the role of activation factors / suppression factors, all transfer products that connect to compatible models among the two donors were excluded. These procedures are generalized in column 2 in Fig. 1A, and more detailed information is shown in the section of the "method".
If the candidate is considered a transfer moment, the c o-formation transfer is transmitted to the first code of the encoded protein, which is also necessary to be related to the Foxp3 promoter area. This provides a temporary delay in revitalizing the transfer product of the control factor candidate for the expression of Foxp3 MRNK. A fairly common upstream control factor candidate is not considered a transfer moment that requires a number of crusts / proteins to adjust the FOXP3 expression, and as a result. You will also request a delay. Time delayed τ was introduced in the input code to reflect the dynamics of the border and avoid significantly expanding the difficulty of the model.Frac
& amp; amp; gt; & amp; amp; gt; (t) & amp; gt; = $ cdot
& amp; amp; gt; & amp; amp; gt; (t)+b cdot transcript (T-tau).
In general, this model actually contains only three parameters, so when used in a small number of inexpensive times, the possibility of r e-scaling can be reduced. In addition, this general model has the ability to scale the entire genome. Due to this difference, other more difficult models, such as a highe r-dimensional system, no n-linear shape, or complex penalty, have higher risk of bankruptcy, and usually optimizes combinations. It leads to (not scalable). At least other models can lead to correct prophecy.
The ratio of information for each model obtained was observed in the support for evaluating a certain ratio in the second formula in column 2 Figure 1. Transcripts that control FOXP3 with a higher probability respond to the highest significance of fitness Ballal (fitness Ballal ranges from 0 to 100, and this 100 integral complies). For each model, we evaluated a certain probability of delay τ and selected the delay that leads to a greater fitness. After this, we ranked all the remaining 7030 models at that point by this metric. This is shown in the third column of Figure 1A and is explained in more detail in the "Methods" section.
Furthermore, we decided to focus on 2, 5% of the genes occupying the first space of the list, i. e. 176 transcripts out of 7030 (Table 1). Fitness traits decreased rapidly in models occupying the first space and then more slowly. However, since each truncation can be considered to some extent random, we decided to focus on genes located at the top of the list, which are more likely to control FOXP3 (additional information in the Methods). These 176 genes cover a wide range of cellular functions, allowing their biological functions and dynamics to be studied individually.Table 1. Top rankings of 176 transcripts aligned with our layout
Of the data obtained from the 176 models, 161 were individually assigned to genes. These genes were fit to a spectrum ranging from 46 (lowest) to 62 (highest). Among the remaining 161 genes occupying the 1st space in the ranking, FOXP3 is known to interact with the promoters of 59 genes 65, and 38 genes are differentially spoken of in human trumps to compare with CD4+CD25 effector T cells (Teffs) 65. For 15 genes, both statements were correct. As a test of the literature data, this indicates that the predicted genes occupying the 1-space are indeed involved in a manner related to the control of FoxP3, and indeed approves our disappearance relevance as a whole.
Considering a certain example, in the list of gene, which occupies the first space, our method, as already shown, plays an important role in adjusting the expression of foxp3 and suppressing the function of TREG. The candidate gene is assembled. For example, as shown in Table 1, our method led IKZF4 as the 52nd candidate gene, which has the ability to control the expression of foxp3. Greatly, the selective IKZF4 loss in TREG has the loss of suppression and the onset of systemic aut o-immunity 66. ARG2 borrowed 55 space. ARG2 has actually increased TRE G-based abilities in Vitro and shows that in Vivo is selectively dominant in accumulation in inflammatory tissues. CD44 borrowed the 84th space. Three of the KD44 nokaut mice have a displaced adjustment function outside the living body in the worst direction, and the products of IL-10 and TGF-BET are decreasing on the cell surface 68. IRF1 was predicted in our way from the gene of the first space (107th) in the list. In fact, it has been reported that IRF1 controls TREG's differentiation by suppressing the expression of foxp3.
Discussion
The next step was to experimentally prove some of the 161 gene, which accounted for the ranking first place. The probability was incomplete, so only five were experimented. Since the evaluation of the 161 gene conformity, which accounted for the first place, was relatively close, I selected with appropriate additional examinations. First of all, it is impossible to modify which gene to create the resulting profile of the result (that is, cannot modify a clear candidate), so a set suitable for one or more gene is excluded. It was done. Second, the echosignal of the time axis was eliminated, and only the relatively smooth signals of the time axis were focused. Third, MRNA shifts from control transfer product to protein, binds to foxp3 promoters, and further controls the expression (2) appropriate delay τ of the formula (2). Priority of transcription products for 60 minutes was prioritized. Fourth, we made an inseparable choice based on biological meaning. The transfer control gene that encodes proteins in the nucleus is prioritized because the known Foxp3 control format occurs in the nucleus (for example, link 46).
Five candidates that were previously relevant were selected in line with these additional considerations for experimental audits: NCOA7, NRBF2, PDE4D, MAP1LC3B, RNF12 (also known as RLIM) (also known as RLIM). Figure 1C). The first three coded the protein in the nucleus. The details of this selection are shown in the "method" section.
As a result of the experiment, if SiRNA, which is specific to the corresponding gene, it would succeed in NOKDOWN MAP1LC3B, NCOA7, and NRBF2 compared to the control of the sorted raw wood. Showed (see the "Methods" section, additional diagram). The use of Qiagen HP ONSIGNA Design Transport has minimized the potential risk beyond the intended exposure of SiRNA used. Furthermore, SiRNA, which was used for any of these five cult regulations, had no universal impact on TREG's unrelated gene, for example, the CD4 MRNU level (additional Fig. 2). Also, in our project, transfection of this specific SiRNA did not have a significant impact on the cell survival rate (shown in Figure 5a). As we predicted by our method, the partial knock-down of MAP1LC3B, NCOA7, and NRBF2 has already significantly reduced the transcription of Foxp3 in TREG (Figure 2b-D). The foxp 3-t o-Foxp 3-t o-FOXP 3-developed fixes after SiRNA processing were slightly different in three candidates. The other two candidates, PDE4D and RNF12, succeeded in knocking down the expression of those MRNAs, but did not see any obvious effects in the Foxp3 MRNK expression (no data indicated).
Rice. 2: Experimental verification of predicted cartridge gene in the adjustment factor of the rise current. < SPAN> Five candidates that were previously relevant were selected according to these additional considerations for experimental audits: NCOA7, NRBF2, PDE4D, MAP1LC3B, RNF12 (also known as RLIM) (Fig. 1C). The first three coded the protein in the nucleus. The details of this selection are shown in the "method" section.
As a result of the experiment, if SiRNA, which is specific to the corresponding gene, it would succeed in NOKDOWN MAP1LC3B, NCOA7, and NRBF2 compared to the control of the sorted raw wood. Showed (see the "Methods" section, additional diagram). The use of Qiagen HP ONSIGNA Design Transport has minimized the potential risk beyond the intended exposure of SiRNA used. Furthermore, SiRNA, which was used for any of these five cult regulations, had no universal impact on TREG's unrelated gene, for example, the CD4 MRNU level (additional Fig. 2). Also, in our project, transfection of this specific SiRNA did not have a significant impact on the cell survival rate (shown in Figure 5a). As we predicted by our method, the partial knock-down of MAP1LC3B, NCOA7, and NRBF2 has already significantly reduced the transcription of Foxp3 in TREG (Figure 2b-D). The foxp 3-t o-Foxp 3-t o-FOXP 3-developed fixes after SiRNA processing were slightly different in three candidates. The other two candidates, PDE4D and RNF12, succeeded in knocking down the expression of those MRNAs, but did not see any obvious effects in the Foxp3 MRNK expression (no data indicated).
Rice. 2: Experimental verification of predicted cartridge gene in the adjustment factor of the rise current. Five candidates that were previously relevant were selected in line with these additional considerations for experimental audits: NCOA7, NRBF2, PDE4D, MAP1LC3B, RNF12 (also known as RLIM) (also known as RLIM). Figure 1C). The first three coded the protein in the nucleus. The details of this selection are shown in the "method" section.
As a result of the experiment, if SiRNA, which is specific to the corresponding gene, it would succeed in NOKDOWN MAP1LC3B, NCOA7, and NRBF2 compared to the control of the sorted raw wood. Showed (see the "Methods" section, additional diagram). The use of Qiagen HP ONSIGNA Design Transport has minimized the potential risk beyond the intended exposure of SiRNA used. Furthermore, SiRNA, which was used for any of these five cult regulations, had no universal impact on TREG's unrelated gene, for example, the CD4 MRNU level (additional Fig. 2). Also, in our project, transfection of this specific SiRNA did not have a significant impact on the cell survival rate (shown in Figure 5a). As we predicted by our method, the partial knock-down of MAP1LC3B, NCOA7, and NRBF2 has already significantly reduced the transcription of Foxp3 in TREG (Figure 2b-D). The foxp 3-t o-Foxp 3-t o-FOXP 3-developed fixes after SiRNA processing were slightly different in three candidates. The other two candidates, PDE4D and RNF12, succeeded in knocking down the expression of those MRNAs, but did not see any obvious effects in the Foxp3 MRNK expression (no data indicated).
Methods
Time-series data normalization and filtering
Rice. 2: Experimental verification of predicted cartridge gene in the adjustment factor of an up current.
An experimental schedule for verifying the concept. The predicted candidate gene is knocked out by the SiRNA Transfiction Law by the initial trembling of a person in a 24-hour direction, and the stimulation of the anti-CD3/ CD28/ recombinant IL-2 is added in the direction of everyone's type. It was. The result of the rea l-time quantitative PCR (QPCR) on the MAP1LC3B knockdown in the initial tremor and the expression of foxp3 corresponding to it. The contrast of the encoded abnormal knockdown (si_ns) is shown in red in black, and the species knoc k-down (SI_MAP1LC3B). C NCOA7 NOKDOWN The result of QPCR of Foxp3 corresponding. The control knockdown (si_ns) is black, and the see d-down (si_ncoa7) is shown red. D NRBF2 Knockdown and the corresponding expression foxp3 QPCR result. The control knockdown (si_ns) is black, and the seed knockdown (si_nrbf2) is red. Go on your feet to show that NCOA7 and NRBF2 knoc k-down experiments were performed in combination with control standards from the same donor as the first. The statistical significance (P value) was determined by the support of a T-test-koester without a multiple comparison correction-koester. MUJI is not significant. The data is a normal value of 4 technical replication (S. D.). Experimental schedule for verifying the < Span> concept. The predicted candidate gene is knocked out by the SiRNA Transfiction Law by the initial trembling of a person in a 24-hour direction, and the stimulation of the anti-CD3/ CD28/ recombinant IL-2 is added in the direction of everyone's type. It was. The result of the rea l-time quantitative PCR (QPCR) on the MAP1LC3B knockdown in the initial tremor and the expression of foxp3 corresponding to it. The contrast of the encoded abnormal knockdown (si_ns) is shown in red in black, and the species knoc k-down (SI_MAP1LC3B). C NCOA7 NOKDOWN The result of QPCR of Foxp3 corresponding. The control knockdown (si_ns) is black, and the see d-down (si_ncoa7) is shown red. D NRBF2 Knockdown and the corresponding expression foxp3 QPCR result. The control knockdown (si_ns) is black, and the seed knockdown (si_nrbf2) is red. Go on your feet to show that NCOA7 and NRBF2 knoc k-down experiments were performed in combination with control standards from the same donor as the first. The statistical significance (P value) was determined by the support of a T-test-koester without a multiple comparison correction-koester. MUJI is not significant. The data is a normal value of 4 technical replication (S. D.). An experimental schedule for verifying the concept. The predicted candidate gene is knocked out by the SiRNA Transfiction Law by the initial trembling of a person in a 24-hour direction, and the stimulation of the anti-CD3/ CD28/ recombinant IL-2 is added in the direction of everyone's type. It was. The result of the rea l-time quantitative PCR (QPCR) on the MAP1LC3B knockdown in the initial tremor and the expression of foxp3 corresponding to it. The contrast of the encoded abnormal knockdown (si_ns) is shown in red in black, and the species knoc k-down (SI_MAP1LC3B). C NCOA7 NOKDOWN The result of QPCR of Foxp3 corresponding. The control knockdown (si_ns) is black, and the see d-down (si_ncoa7) is shown red. D NRBF2 Knockdown and the corresponding expression foxp3 QPCR result. The control knockdown (si_ns) is black, and the seed knockdown (si_nrbf2) is red. Go on your feet to show that NCOA7 and NRBF2 knoc k-down experiments were performed in combination with control standards from the same donor as the first. The statistical significance (P value) was determined by the support of a T-test-koester without a multiple comparison correction-koester. MUJI is not significant. The data is a normal value of 4 technical replication (S. D.).
One-2-one method
As a result, it was found that it is likely to affect the expression amount of foxp3 proteins, but the importance of proteins has hardly been built with no model or disappearance (all models are built only in the MRNK database, The prophecy of protein expression has not been built). Two types of protein analysis, Imnobrot and Running Citometry (additional figure 3-5). Since the reliable antibodies for MAP1LC3B are limited, Western blotting (WB) was performed to examine the effects of knocking down only NCOA7 and NRBF2. As a result, the value of the NRBF2 protein decreased, which led to the decrease in the expression of NRBF 2-sirna (see supplementary note), and some foxp 3-support expression (additional Fig. 3). In the WB, a large protein sample can only obtain average results, so we also used running citrometry based on individual cells. However, in other donors, even if the citrometry was executed, the decrease in the expression of foxp3 proteins between living cells was not clear, but in the same donor, TREG, which was thoroughed with NRBF2 NRBF2 NRBF2, was stimulated, foxp3 transferring. A significant decrease in the product was found, which was actually determined by The Quantitative PCR in Real Time (QPCR) (QPCR) (Supplementary Figure). Another main trigger gene CTLA4 protein expression was actually increased in the two groups (supplementary diagram)
These experiments have been trembling from the peripheral blood of eight healthy adult donors. The number of analyzed donors depends on both proteins, MRNA, or both. Almost two of the tested donors were not observed, but this is likely to be related to human individual differences. < SPAN> As a result, it has been found that it is highly likely that it will affect the amount of foxp3 proteins, but the importance of proteins has hardly been built (all models are MRNK databases only (all models are MRNK database. It has been built and has not been built for protein protein expression). Two types of protein analysis, Imnobrot and Running Citometry (additional figure 3-5). Since the reliable antibodies for MAP1LC3B are limited, Western blotting (WB) was performed to examine the effects of knocking down only NCOA7 and NRBF2. As a result, the value of the NRBF2 protein decreased, which led to the decrease in the expression of NRBF 2-sirna (see supplementary note), and some foxp 3-support expression (additional Fig. 3). In the WB, a large protein sample can only obtain average results, so we also used running citrometry based on individual cells. However, in other donors, even if the citrometry was executed, the decrease in the expression of foxp3 proteins between living cells was not clear, but in the same donor, TREG, which was thoroughed with NRBF2 NRBF2 NRBF2, was stimulated, foxp3 transferring. A significant decrease in the product was found, which was actually determined by The Quantitative PCR in Real Time (QPCR) (QPCR) (Supplementary Figure). Another main trigger gene CTLA4 protein expression was actually increased in the two groups (supplementary diagram)These experiments have been trembling from the peripheral blood of eight healthy adult donors. The number of analyzed donors depends on both proteins, MRNA, or both. Almost two of the tested donors were not observed, but this is likely to be related to human individual differences.
The research challenge provided is to demonstrate that the systematic identification and use of engineering-based computational methods allows for the extraordinary and efficient transfer by the applicant of regulators of genes found to be highly directional. The accuracy of the prediction was shown in the support of the analysis of the candidate genes occupying the first place in the ranking during the experiment to confirm the concept. We showed the method of transcriptional data from a temporal Treg-line to qualify the likely regulator genes of Foxp3 master regulator from the whole genome. Subsequently, five genes occupying one space were selected and experimented to verify this concept. As a result, three candidates proved to be safe.
Although spontaneous autoimmune phenotypes do not appear in NRBF2 mice, NRBF2 actively controls the process of autophagy 70. It is within the range of 72 million than such an integrated meta-analysis. In fact, NRBF2 still controls the energy of VPS34 70. Moreover, T cell depletion of VPS34 is important for the maintenance and suppressive function of Tregs 73. Our results, together with this submitted paper, indicate that NRBF2 may in fact be a promising ceiling regulatory genome that regulates the expression of FOXP3. Subsequent studies introducing NRBF2 into whole-body or tremor-specific knockout mice consisted of in vivo trigeminal nerves to realize the physical and pathological effects of NRBF2. However, in homeostatic criteria, NRBF2-deficient mice do not develop spontaneous autoimmune phenotypes 70. Therefore, ancillary efforts to study the impact in animal models and/or criteria of inflammatory, infectious or autoimmune diseases induced in the course of 74 are not developed in future experiments. In the case of SPLASH, our work builds a pathway to identify new likely applicants to regulators for regulating Foxp3 expression in trees and various challenging diseases where 3 plays a central role.
The model set by the provided RV matched the transfer data. In other words, this model can create reliable prophecy only at the MRNA level. In fact, the degree of protein does not match the dynamics of MRNA every time because the transcription and translation of all kinds of proteins (eg, foxp3, other research 75. 76) are solid and after translating. 。 Apart from this, NRBF2 selective NRBF2 NOCDAUN has the opportunity to reduce the expression of foxp3 transfer products, because there is a high possibility that there is a compensatory parallel pathway that controls the expression of foxp3 proteins. It is not an array to reduce. Note that the SiRNA actually used is made in the field of SIRNA Qiagen HP ONGARD DESIGN SYSTEM, which sells various difficult ways to minimize of f-target risks. There is a high possibility that different SIRNA will be tested by conflicting genetic candidates. Apart from this, since the three candidate gene (for example, MAP1LC3B, NCOA7, NRBF2) is not included in the popular transfer moment, all of these three candidate genes are all measured or the same control. For example, the model set by the < Span> RV provided, which is absolutely possible to control the interaction between proteins and white, matched the transcription data. In other words, this model can create reliable prophecy only at the MRNA level. In fact, the degree of protein does not match the dynamics of MRNA every time because the transcription and translation of all kinds of proteins (eg, foxp3, other research 75. 76) are solid and after translating. 。 Apart from this, NRBF2 selective NRBF2 NOCDAUN has the opportunity to reduce the expression of foxp3 transfer products, because there is a high possibility that there is a compensatory parallel pathway that controls the expression of foxp3 proteins. It is not an array to reduce. Note that the SiRNA actually used is made in the field of SIRNA Qiagen HP ONGARD DESIGN SYSTEM, which sells various difficult ways to minimize of f-target risks. There is a high possibility that different SIRNA will be tested by conflicting genetic candidates. Apart from this, since the three candidate gene (for example, MAP1LC3B, NCOA7, NRBF2) is not included in the popular transfer moment, all of these three candidate genes are all measured or the same control. For example, the model set by the provided RV, which is absolutely possible to control by the interaction between proteins and white, matches the transcription data. In other words, this model can create reliable prophecy only at the MRNA level. In fact, the degree of protein does not match the dynamics of MRNA every time because the transcription and translation of all kinds of proteins (eg, foxp3, other research 75. 76) are solid and after translating. 。 Apart from this, NRBF2 selective NRBF2 NOCDAUN has the opportunity to reduce the expression of foxp3 transfer products, because there is a high possibility that there is a compensatory parallel pathway that controls the expression of foxp3 proteins. It is not an array to reduce. Note that the SiRNA actually used is made in the field of SIRNA Qiagen HP ONGARD DESIGN SYSTEM, which sells various difficult ways to minimize of f-target risks. There is a high possibility that different SIRNA will be tested by conflicting genetic candidates. Apart from this, since the three candidate gene (for example, MAP1LC3B, NCOA7, NRBF2) is not included in the popular transfer moment, all of these three candidate genes are all measured or the same control. For example, there is a possibility that it may be controlled by the interaction between proteins and white.
From the viewpoint of the calculation, one of the limits is that in our situation based on linear dynamics, it may overlook difficult nonlinear interactions. Furthermore, since our method is once inspected one transfer product of FOXP3, which is likely to be a control gene, it requires the coordination of different transfer moments, coquet vetters, or coosplessers at the same time. There is a possibility that you will miss the action. What is the adjustment of different adjustment factors at once, and which genes are considered to be really important at the biological level. Our situation has the possibility that it can be easily expanded to consider, for example, nonlinearity, adjustment of numerous adjustment factors at once, and what the motivated genes are. 。 No n-linear models and models with multiple inputs and one output may be integrated with the support of adjustment methods to enhance rarity. However, this will lead to the fastest way to disclose the difficult output method in terms of calculation, which will limit the applicability of the method, the minimum number of gene. Apart from this, the possibility of data from a temporary system is limited, so the higher the complexity of the model, the higher the adaptation of the model, and the number of incorrect work increases. be.
Furthermore, there are as follows: (1) experimental tests on animal models on in vivo of MAP1LC3B, NCOA7, and NRBF2, for example, constant and pathological conditions. Introducing appropriate or specific things in T cells from symbolic nur mouse; Determine the control gene of other important gene (eg CTLA4) for the Combination of targets by control interaction 80, 81, 82, 83; and (5) When new type data data is seen in all kinds of experimental standards, it applies more difficult models that reflect the details of mechanics. From the viewpoint of the calculation of the unit cell RN A-segment data 84. 85. < SPAN> Calculation, in our situation based on linear dynamics, it is possible to overlook difficult nonlinear interactions. It is. Furthermore, since our method is once inspected one transfer product of FOXP3, which is likely to be a control gene, it requires the coordination of different transfer moments, coquet vetters, or coosplessers at the same time. There is a possibility that you will miss the action. What is the adjustment of different adjustment factors at once, and which genes are considered to be really important at the biological level. Our situation has the possibility that it can be easily expanded to consider, for example, nonlinearity, adjustment of numerous adjustment factors at once, and what the motivated genes are. 。 No n-linear models and models with multiple inputs and one output may be integrated with the support of adjustment methods to enhance rarity. However, this will lead to the fastest way to disclose the difficult output method in terms of calculation, which will limit the applicability of the method, the minimum number of gene. Apart from this, the possibility of data from a temporary system is limited, so the higher the complexity of the model, the higher the adaptation of the model, and the number of incorrect work increases. be.<\sqrt<\mathop<\sum >Furthermore, there are as follows: (1) experimental tests on animal models on in vivo of MAP1LC3B, NCOA7, and NRBF2, for example, constant and pathological conditions. Introducing appropriate or specific things in T cells from symbolic nur mouse; Determine the control gene of other important gene (eg CTLA4) for the Combination of targets by control interaction 80, 81, 82, 83; and (5) When new type data data is seen in all kinds of experimental standards, it applies more difficult models that reflect the details of mechanics. 84. 85. Related to the unit cell RN A-segment data 84. 85. From the viewpoint of the calculation, it is one of the limits that in our situation based on linear dynamics, it is possible to overlook difficult nonlinear interactions. 。 Furthermore, since our method is once inspected one transfer product of FOXP3, which is likely to be a control gene, it requires the coordination of different transfer moments, coquet vetters, or coosplessers at the same time. There is a possibility that you will miss the action. What is the adjustment of different adjustment factors at once, and which genes are considered to be really important at the biological level. Our situation has the possibility that it can be easily expanded to consider, for example, nonlinearity, adjustment of numerous adjustment factors at once, and what the motivated genes are. 。 No n-linear models and models with multiple inputs and one output may be integrated with the support of adjustment methods to enhance rarity. However, this will lead to the fastest way to disclose the difficult output method in terms of calculation, which will limit the applicability of the method, the minimum number of gene. Apart from this, the possibility of data from a temporary system is limited, so the higher the complexity of the model, the higher the adaptation of the model, and the number of incorrect work increases. be.<(As a whole, the results we have gained are also to regulate the expression of foxp 3-head control in Kiver, which supports foxp 3-control net, and in fact, in Kiburo, which supports low molecular compounds or biological products. It has revealed the ability to develop fresh immunotherapy agents, and has the potential to have an important significance for a series of difficult diseases. However, we tested only a certain amount of an intracellular cartridge of the adjustment factor, and in the list that occupies the first space, there are actually cel l-level external applicants, and all kinds of subsequent types. It was relatively easy to be targeted to succeed in verification. In fact, our results were obtained by the largest broadcast of the initial T cells of individuals, which indicate their broadcasting potential. From the viewpoint of calculation, our research shows the ability of dynamic modeling that performs objective inference on the transfer control gene in initial cells by screening all genome. This method may simplify the experimental process that requires a long time and resources to identify a highe r-order control gene. The provided results show that our layout will be used to execute the applicants for the regulatory factors of our interests throughout the time axis. In particular, by introducing time delays, we were able to find more difficult dynamics, and supported the only first auxiliary parameters with this scalability to protect the scalability of the method. This method was shown in all genome rankings for one of the more possible foxp3 control factors in native human treg cells. In order to prove this evaluation, three of the five candidate gene gene were proved in a transcription level experiment, indicating consistency between different donors. Drowning on either of the three genes-map1LC3B, NCOA7, and NRBF2- on the right reduced the transcription of Foxp3.As a whole, the results we have gained are also to regulate the expression of foxp 3-head control in Kiver, which supports foxp 3-control net, and in fact, in Kiburo, which supports low molecular compounds or biological products. It has revealed the ability to develop fresh immunotherapy agents, and has the potential to have an important significance for a series of difficult diseases. However, we tested only a certain amount of an intracellular cartridge of the adjustment factor, and in the list that occupies the first space, there are actually cel l-level external applicants, and all kinds of subsequent types. It was relatively easy to be targeted to succeed in verification. In fact, our results were obtained by the largest broadcast of the initial T cells of individuals, which indicate their broadcasting potential. From the viewpoint of calculation, our research shows the ability of dynamic modeling that performs objective inference on the transfer control gene in initial cells by screening all genome. This method may simplify the experimental process that requires a long time and resources to identify a highe r-order control gene.As shown in Fig. 1, as described in the notebook, initial data was obtained from the early human treg. 19. The measurement on this microchip was performed with anti-CD3/ CD28/ human RIL-2 stimulus for TREG of two donors (here is called donor-1, donor-2). Later, I went every 20 minutes for 6 hours. These data contain a 54676 transfer / sampling set for each donor, and various transfer options are formed for almost all genes in all genome. For simplicity, only the term "transcription product" is used, and the term "set probe" is omitted, but they actually exist in the relationship of "on e-o n-one" and, so they can be exchanged. Please remember. However, RNA sequencing is 86 exceeding microchips in detecting lo w-tuning transfer products. We can boldly apply this microchip data set because we are not targeting low tuning transfer products.<_>Before using a method of identifying the system, data from the time axis must be processed first. This links the support of the GCRMA 88 method, which is a general bioginformatics tool for removing the maximum amount of noise and displacement sold in MATLAB, links data. All other computational quality of this work and the work provided used was Matlab version R2016A, R2016B, and R2017A. After that, normalized with the support of the GCRMA method was performed, the data was converted to 2x (X gives normalized data), and the unconditional natural value is returned to an unconditional natural value used later. The natural data converted in this way was filtered. First, the flag filter of Affymetrix was removed to remove the default in the executed dimensions at any time. Conversely, in one measurement performed at any time, all the transferred substances pointed out that it is not very prominent or existed is protected 2. 2 Filters are average voltage (MRNA-expression, this is the above. As shown in Fig. 1, the position changes depending on the normalization used in, as shown in Fig. 1, the initial data was obtained from the early human Treg. 19. The measurement on this microchip was performed with anti-CD3/ CD28/ human RIL-2 stimulus for TREG of two donors (here is called donor-1, donor-2). Later, I went every 20 minutes for 6 hours. These data contain a 54676 transfer / sampling set for each donor, and various transfer options are formed for almost all genes in all genome. For simplicity, only the term "transcription product" is used, and the term "set probe" is omitted, but they actually exist in the relationship of "on e-o n-one" and, so they can be exchanged. Please remember. However, RNA sequencing is 86 exceeding microchips in detecting lo w-tuning transfer products. We can boldly apply this microchip data set because we are not targeting low tuning transfer products.
Before using a method of identifying the system, data from the time axis must be processed first. This links the support of the GCRMA 88 method, which is a general bioginformatics tool for removing the maximum amount of noise and displacement sold in MATLAB, links data. All other computational quality of this work and the work provided used was Matlab version R2016A, R2016B, and R2017A. After that, normalized with the support of the GCRMA method was performed, the data was converted to 2x (X gives normalized data), and the unconditional natural value is returned to an unconditional natural value used later. The natural data converted in this way was filtered. First, the flag filter of Affymetrix was removed to remove the default in the executed dimensions at any time. Conversely, in one measurement performed at any time, all the transferred substances pointed out that it is not very prominent or existed is protected 2. 2 Filters are average voltage (MRNA-expression, this is the above. As shown in Fig. 1, the position changes depending on the normalization used in, as shown in the notebook, the initial data was obtained from the early human treg. 19. The measurement on this microchip was performed with anti-CD3/ CD28/ human RIL-2 stimulus for TREG of two donors (here is called donor-1, donor-2). Later, I went every 20 minutes for 6 hours. These data contain a 54676 transfer / sampling set for each donor, and various transfer options are formed for almost all genes in all genome. For simplicity, only the term "transcription product" is used, and the term "set probe" is omitted, but they actually exist in the relationship of "on e-o n-one" and, so they can be exchanged. Please remember. However, RNA sequencing is 86 exceeding microchips in detecting lo w-tuning transfer products. We can boldly apply this microchip data set because we are not targeting low tuning transfer products.
Before using a method of identifying the system, data from the time axis must be processed first. This links the support of the GCRMA 88 method, which is a general bioginformatics tool for removing the maximum amount of noise and displacement sold in MATLAB, links data. All other computational quality of this work and the work provided used was Matlab version R2016A, R2016B, and R2017A. After that, it was normalized with the support of the GCRMA method, converted the data to 2 x (X gives normalized data), and returned to unconditional natural values on the linear scale used later. The natural data converted in this way was filtered. First, the flag filter of Affymetrix was removed to remove the default in the executed dimensions at any time. Conversely, in one measurement performed at any time, all the transferred substances pointed out that it is not very prominent or existed is protected 2. 2 Filters are average voltage (MRNA-expression, this is the above. The position changes depending on the normalization used in)
Here, we exploited the methodology introduced in EXILE. 16 to detect candidate genes that regulate FoxP3. In this modeling strategy, we use a linear invariant (LTI) model to reflect the dynamics describing the composition rate of a selected transcript relative to another input transcript. The 1-2-1 model refers to a model with one input and one output. The linear modeling paradigm contains good qualities in the criterion of data scarcity. In particular, linear models do not distinguish the detailed structure of the entire network and are ready to detect regulatory interactions with reliable accuracy (see below). A general LTI model is expressed by the following equation:
BEGIN<\mu >_<_>)$$\ frac (t) = ax (t)+bu (t) $ y (t) = cx (t).<\mu >This model studies whether the gene expression rate of a particular transcript y (T) depends on the expression of a gene of another transcript U (T). Specifically, U (T) and Y (T) gave the short-term series of expression over time of genes that are likely to be Foxp3 and Foxp3 regulators, respectively. The variables X (T) give internal dynamics (transduction, transcription, etc.), which interact with the simulation output and were important for the observed behavior, but were not explicitly integrated in the model. The dimension of the vector x (t) determines the order of the model. In the general case, this can be a one-dimensional vector (direct regulation, or relatively unhurried dynamics to compare with the internal dynamics), or a multidimensional vector (regulation occurs through a lining step that introduces delays and cannot be ignored).<\mu >Evaluation of the model implies the intelligence A, B, C to distinguish vectors y (t) that are very close to the actual expression information. On the one hand, the difficult nonlinear models can represent the dynamic coupling between genes with the highest accuracy. On the other hand, the most complex models can lead to unnecessary fitting (fitting noise at the expense of dynamics) without the necessary number of data or detailed knowledge (such as the topology of the network and the type of nonlinear interactions). In the provided case, we limited the models to the first order, which is usually estimated to be 16, i. e. A, B, C are scalars. System identification was performed with the support of the ‘Pem’ function implemented in MATLAB to minimize the miss 89 .
The linear model has the ability to close several nonlinear systems and the linearized equilibrium. It indicates how a relatively small input configuration causes a small change to comparative output with a equilibrium. However, the position of the equilibrium is incomprehensible and depends on the removal of background noise by the GCRMA method. As a result, a permanent member was added to model each pair of gene interactions, and was observed at the same time as other model parameters. At the technical level, this is sold by the appearance of two linear models with two states, the second position is regarded as no change (that is, dynamics), and the contribution is multiplied by time parameters. As a result, the first part of the model can focus on more important dynamic information between gene, while the constant is cooled without the change.
Applying the one-2-one to our data: all-2-one method
To minimize the noise displacement, using the normal methodology known as the predictive error method (PEM): monitoring is θ (T, right first) = y left first ( T-right first) = Y, as the left most (T | T-1; the first right), this method recognized the parameters of the model θ and had the minimum dispersion. In other sentences, PEM minimizes the mismatch between monitoring and observation data generated by the current parameters.
Each model was characterized by the performance index that reflects the ability of a model that overlooks the relationship between input and output. For this purpose, I used fitness:
Fitness = 100* Link (1-Frac
Selection of genes for wet-lab experiments
\ Fitness = 100* Link (1-Frac $ NOLIMITS_^
) & amp; amp; gt;^& amp; amp; amp; _) & amp; amp; gt;^& amp; gt;
\ amp;^& amp; amp; gt;.
(1- Frac $ nolimits_^ & amp ;; gt; _) & amp; amp; gt;^ & amp; gt;
Here
K.
Regulatory T cell isolation and culture
Given data (output), nolimit s-average data, nolimits.
\ Provisional output. Matlab C O M PA R E You can calculate the compatibility of the model using a function. Compatibility 100%is equivalent to complete identification. The highest significance means that the huge percentage of dynamics was captured.2Next, in order to investigate what seems to be a control gene of foxp3, the collection of the first transcription product, which was similar to foxp3, was evaluated. In each case, the traits were observed in this way, so that the sobou cup provided the best ratio of FOXP3-one-time stroke. This step looked like a way:
Flow cytometry for Treg characterization
Here, N is the number of applicants. Each model was characterized by spectrum fitness matricks from 0%to 100%, reflecting the legal abilities that outlook the initial adjustment system between gene. In this way, the gene was regarded as a hypothetical Foxp3 control factor if the model obtained by the first introduction of the transfer product could be reproduced in the exact ascending foxp3 expression profile.
Treg siRNA knockdown and stimulation
Furthermore, we derived the formula of the dynamics of Foxp3, clearly linked the delay in modeling, and gave a future prospect:
RNA extraction
Frac = a [foxp3] (T)+b_ (T-)
cDNA synthesis
Here, Frac (
Quantitative real-time PCR (qPCR)
In a 2 0-minute step, the toe selected between 0 and 100 minutes ... Options __ (
Western blotting
Gt; = 0 ‡-Draw a model in a personal case that is considered higher.
Flow cytometry analysis in NRBF2 knockdown experiments
The systematic comparison of the advanced method with such a modeled data and our method is the number of applicants here. Each model was characterized by spectrum fitness matricks from 0%to 100%, reflecting the legal abilities that outlook the initial adjustment system between gene. In this way, the gene was regarded as a hypothetical Foxp3 control factor if the model obtained by the first introduction of the transfer product could be reproduced in the exact ascending foxp3 expression profile.
While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates. Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data. 100-zombies.lol We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been.
accepted