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What is the subcellular location of synthesis of non-essential amino-acids?

What is the subcellular location of synthesis of non-essential amino-acids?



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What is location of non-essential amino acids synthesis in a cell? Is it some specific organelle? And what is the gene driver behind this?

I thought the whole point of DNA is coding for how to synthesize proteins and that it is the mechanism of genes expression. Triplets of nucleotides are codes of amino acids and that is the granularity that cell works with.

Now it turns out cells knows how to synthesize more than half of the 20 amino acids, in humans, and I do not see, in the web, good texts of how it is done, where, and how that is coded.

Thank you for helping to shed some light.


I think you are almost right. DNA (in the nucleus) is transcribed to mRNA, and protein synthesis occurs on ribosomes where the the sequence of bases in the mRNA is translated into protein. Ribosomes are located in the cytoplasm or attached to the endoplasmic reticulum. (The 'mRNA story' is well told in Who discovered mRNA?)

But there is also another type of RNA that is critical to protein synthesis, and that is transfer RNA (tRNA): each amino acid is attached to an 'adaptor' molecule (tRNA) and it is this form of the amino acid that is used by the ribosome. It is the adaptor molecule (a tRNA 'charged' with an amino acid) that contains the anti-codon, which base-pairs with the mRNA during protein biosynthesis.

In order to carry out protein biosynthesis, the cell needs a supply of amino acids, the 'building blocks' of proteins. It is here your thinking (maybe) is a little confused? The building blocks may come the digestion of protein in food, or they may be synthesized by the organism. If they can be synthesized 'de novo', perhaps starting from a glycolytic or Krebs cycle intermediate, they are (usually) considered non-essential. What amino acids are considered essential or non-essential depends on the organism. Humans, for example cannot synthesize 'de novo' the aromatic amino acids Phe, Tyr or Trp, but we can convert Phe to Tyr. E.coli, on the other hand, can make all three of these amino acids from simple building blocks, but cannot convert Phe to Tyr (see Miller & Simmonds). Thus Tyr is considered non-essential for humans (provided that an adequate supply of Phe is available).

The biochemical pathways leading to amino-acid biosynthesis are usually quite complex, with the involvement of many gene products (enzymes). The story of amino acid biosynthesis is well told in Umbarger(1978):Amino Acid Biosynthesis and its Regulation.


There are several enzymes involved in amino acid biosynthesis. These some of thse enzymes are encoded in the human genome. You can check out KEGG for a detailed pathway in different organisms.

The green arrows denote reactions (and enzymes) that are present in a given organism (Homo sapiens, in this case). If you click on the arrows you can know the details about the enzyme.

These enzymes are present in the cytoplasm as well as in mitochondria. Moreover, the reactants for synthesis of certain amino acids are produced in mitochondria (TCA cycle intermediates). See this article on the role of mitochondria in amino acid metabolism.

Overview of the amino acid metabolic network in human mitochondria. Shaded areas represent the cytoplasmic segments of the pathways.


Exam 2 Biochemistry First Round

Repression - slows down the transcription from DNA into RNA.

Protein kinase - they through a phosphate group on a target enzyme.

once you calucalte this, what do the numbers mean?

If delta G is less than zero, it proceeds forward.

If delta G is greater than zero, it is the reverse reaction.

If delta G equals zero, it is at equilibrium.

Consists of two flat _______________ blocks, joined by a hinge, with a _______ rod welded to the back of each block.

The metal blocks have much greater ___________ than the tissue to be sampled.

Tongs are brought to _______C in liquid ___.

Without interrupting the blood flow, an organ/tissue is ____________ between the plates, using considerable leverage of the long rods, compressing it to ______ mm thick.

The very low temperature, the high rate of heat transfer to the metal plates, and the thin sample result in freezing times of < 1 sec for a rate liver or heart. ______________ stops.

The products have a different _________ energy for products.

The first reaction ATP could

React with an enzyme or substrate, and use a phosphate, pyrophosphate or adenylyl transfer to either of these.

It can become a transfer group, or a leaving group from here.

Sum the energies of the two reactions to determine the delta G' of the overall reaction.

Tyrosine is essential when Phe isn't around - Tyrosine is made from Phe.

Contains pepsin, which cleaves after Phe, Leu, and Glu. The low pH will make is more isoelectric, which will help create like charges to repel is and separate the molecule - this helps pepsin get in there. Protein won't be able to fold again because it is no longer a complete protein.

There are neutral proteases - operate at neutral pH - chymotripsin (aromatic compounds) - Trypsin (lysin and arginine) - Carboxypeptidase - takes one AA at a time on the Carboxyl end. Elastase - cleaves elastin which is a highly elastic tissue found in connective tissue.


Introduction

One universal feature of eukaryotic DNA replication is that the genome is duplicated once and only once each time a cell divides. This is accomplished by inactivating the existing pre-replication complexes (pre-RCs) after S phase begins, yet concomitantly preventing assembly of new pre-RCs until mitosis is complete and a nuclear membrane is present. The importance of this principle is highlighted by the fact that endoreduplication (multiple rounds of DNA replication in the absence of an intervening mitosis) is a rare event. In mammals, it occurs only in the terminally differentiated trophoblast giant cells and in megakaryocytes.

Eukaryotic DNA replication begins with the binding of an origin recognition complex (ORC) to DNA, an event that is regulated by cell-cycle-dependent changes in one or more of the six ORC subunits (DePamphilis, 2005). In mammals, these changes appear to be specific for the largest ORC subunit, Orc1. The mammalian ORC consists of a stable core complex of ORC subunits Orc2, Orc3, Orc4 and Orc5 [referred to here as ORC(2-5)] that associate weakly with Orc1 and Orc6 (Dhar et al., 2001 Giordano-Coltart et al., 2005 Kneissl et al., 2003 Vashee et al., 2001). Interaction of Orc1 with the ORC core complex in the presence of ATP is essential for binding ORC to DNA and for assembly of pre-RCs in vitro (Giordano-Coltart et al., 2005). Selective inhibition of Orc1 synthesis in cultured cells confirms that it is also required for assembly of pre-RCs in vivo (Ohta et al., 2003), and the selective loss of Orc1 from chromatin during mitosis accounts for the fact that metaphase mammalian chromatin will not replicate in ORC-depleted Xenopus egg extracts (Li et al., 2000 Natale et al., 2000 Yu et al., 1998).

ORC activity in mammalian cells appears to be regulated by cell-cycle-dependent changes in the association of Orc1 with ORC-chromatin sites (DePamphilis, 2005). The so-called mammalian `ORC cycle' involves three specific changes in Orc1 (see diagram in Fig. 10). First, the affinity of Orc1 for chromatin is selectively decreased as mammalian cells enter S phase (Kreitz et al., 2001 Li and DePamphilis, 2002 Ohta et al., 2003), a change that is reflected in the fact that Orc1 can no longer be crosslinked to DNA in S phase cells (Abdurashidova et al., 2003 Ladenburger et al., 2002). By contrast, the ORC(2-5) core remains stably bound to chromatin throughout cell division (discussed in DePamphilis, 2005). Second, Orc1 is subject to ubiquitylation during S phase (Fujita et al., 2002 Li and DePamphilis, 2002 Mendez et al., 2002 Ritzi et al., 2003 Tatsumi et al., 2003). In transformed human cells, 70-90% of the HsOrc1 is selectively degraded during S phase by an ubiquitin-dependent mechanism (Fujita et al., 2002 Mendez et al., 2002 Ritzi et al., 2003 Tatsumi et al., 2003) (A.V., unpublished data). HsOrc1 is then resynthesized and bound to chromatin during the transition from M to G1 phase. Finally, Orc1 is hyperphosphorylated during G2-M phase, as a result of its association with CcnA-Cdk1 (Li et al., 2004 Tatsumi et al., 2000 Thome et al., 2000). Since inhibition of CDK activity in metaphase cells causes rapid binding of Orc1 (Li et al., 2004) and minichromosome maintenance (MCM) proteins (Ballabeni et al., 2004) to chromatin, and premature initiation of DNA replication (Itzhaki et al., 1997), hyperphosphorylation of Orc1 during the G2-M phase presumably prevents its stable association with ORC(2-5)-chromatin sites. This would account for the absence of functional ORC-chromatin sites on metaphase chromatin (Li et al., 2000 Natale et al., 2000 Yu et al., 1998). Orc1 reassociates tightly with chromatin during the transition from M to G1 phase (Li and DePamphilis, 2002 Mendez et al., 2002 Natale et al., 2000) followed closely by the appearance of pre-RCs at specific genomic sites (Li et al., 2000 Natale et al., 2000).

Ectopic expression of CHO (Cg) Orc1 induced loss of cell adhesion under conditions where ectopic expression of CgOrc2 did not. (A) CHO cells were transfected with the amount of either pCgOrc1 (•) or pCgOrc2 (○) indicated and then incubated for 36 hours before determining the fraction of unattached cells. (B) CHO cells were transfected with 1 μg of either pCgOrc1 (•) or pCgOrc2 (○) and then incubated for the times indicated before determining the fraction of unattached cells. Data with pCI alone was subtracted from data for pCgOrc1 and pCgOrc2. Shaded areas indicate the conditions under which pCgOrc2 and pCI gave indistinguishable results. (C) CHO cells were transfected with 1 μg of either pFCgOrc1 (FOrc1) or pFCgOrc2 (FOrc2) for the times indicated in hours post-transfection (post-T). FOrc1 and FOrc2 were quantified in the total cell population (attached plus unattached) by immunoblotting using anti-FLAG antibody. A single band was detected that migrated as ∼100 kDa for FOrc1 and ∼65 kDa for FOrc2. (D) CHO cells, transfected as in panel A, were separated into those that lifted off the dish (unattached) and those that remained attached. Half of the unattached cells and 10 4 attached cells were assayed for either FOrc1 or FOrc2. (E) CHO cells, transfected as in panel B, were separated into attached and unattached cells before assaying for FOrc1 and FOrc2 for the times indicated. In each experiment, attached and unattached cell proteins were fractionated in the same gel, transferred to the same immunoblot, and detected with the same anti-FLAG antibody to ensure accurate comparison.

Ectopic expression of CHO (Cg) Orc1 induced loss of cell adhesion under conditions where ectopic expression of CgOrc2 did not. (A) CHO cells were transfected with the amount of either pCgOrc1 (•) or pCgOrc2 (○) indicated and then incubated for 36 hours before determining the fraction of unattached cells. (B) CHO cells were transfected with 1 μg of either pCgOrc1 (•) or pCgOrc2 (○) and then incubated for the times indicated before determining the fraction of unattached cells. Data with pCI alone was subtracted from data for pCgOrc1 and pCgOrc2. Shaded areas indicate the conditions under which pCgOrc2 and pCI gave indistinguishable results. (C) CHO cells were transfected with 1 μg of either pFCgOrc1 (FOrc1) or pFCgOrc2 (FOrc2) for the times indicated in hours post-transfection (post-T). FOrc1 and FOrc2 were quantified in the total cell population (attached plus unattached) by immunoblotting using anti-FLAG antibody. A single band was detected that migrated as ∼100 kDa for FOrc1 and ∼65 kDa for FOrc2. (D) CHO cells, transfected as in panel A, were separated into those that lifted off the dish (unattached) and those that remained attached. Half of the unattached cells and 10 4 attached cells were assayed for either FOrc1 or FOrc2. (E) CHO cells, transfected as in panel B, were separated into attached and unattached cells before assaying for FOrc1 and FOrc2 for the times indicated. In each experiment, attached and unattached cell proteins were fractionated in the same gel, transferred to the same immunoblot, and detected with the same anti-FLAG antibody to ensure accurate comparison.

Although these studies suggest that ORC activity in mammalian cells is regulated by cell-cycle-dependent changes in Orc1, other studies have suggested that this might not always be the case. In contrast to HeLa cells, the intracellular level of Orc1 in Chinese hamster ovary (CHO) cells remains constant throughout cell division (Li and DePamphilis, 2002 Li et al., 2004 Natale et al., 2000 Okuno et al., 2001), and this difference has been confirmed in parallel studies under identical conditions (S.G., unpublished data). Furthermore, although both hamster and human Orc1 can be ubiquitylated in vivo and in vitro (Li and DePamphilis, 2002 Mendez et al., 2002) (S.G., unpublished data), only a mono-ubiquitylated form of Orc1 has been detected during S phase in some studies (Li and DePamphilis, 2002), but not in others (Okuno et al., 2001). Moreover, there is no evidence that mono-ubiquitylation of Orc1 interferes with its function. Similarly, the effects of hyperphosphorylation on the ability of Orc1 to participate in DNA replication are implied from the inhibition of protein kinase activities in metaphase cells direct evidence is lacking.

To determine whether or not mono-ubiquitylation and hyperphosphorylation of Orc1 can, in fact, suppress Orc1 function, modified and unmodified ORC1 genes were transiently expressed in CHO and HeLa cells, and their effects compared with those of transiently expressed Orc2. The results demonstrated that the cell-cycle-dependent changes in Orc1 described above could, in fact, affect ORC activity activating it during the transition from M to G1 phase, and suppressing it during the transition from S to M phase. Unexpectedly, these experiments also revealed that unmodified Orc1 could induce apoptosis that was similar to apoptotic cells that had arisen spontaneously during cell proliferation. Moreover, the same modifications that prevented Orc1 from participating in DNA replication also neutralized its ability to induce apoptosis, suggesting that failure to regulate ORC activity during mammalian development could result in cell death.


RESULTS

Generation of mESCs lacking Y RNAs

We used the CRISPR-Cas9 system to construct mESC lines containing deletions of each of the two bona fide Y RNA genes: Rny1 and Rny3. We chose mouse cells because of the small number of distinct Y RNAs in these cells and reduced number of pseudogenes, compared with human cells. Specifically, while human cells contain >1000 Y RNA pseudogenes, mice contain fewer than 60 ( 16). Although most of these sequences lack RNA polymerase III promoters and contain mutations that prevent Ro60 binding ( 16, 48), at least some sequences are transcribed, possibly due to pervasive polymerase II transcription or as part of nascent pre-mRNAs ( 36, 49). Additionally, the use of mESCs could serve as a platform for future studies in mice.

We generated two independent cell lines lacking each of the two mouse Y RNAs. Using northern blotting, we confirmed that the expected RNA was undetectable when either Rny1 or Rny3 was deleted (Figure 1A). As observed previously ( 21), Y1 and Y3 RNAs are reduced by 9.5- and 4.5-fold, respectively, in Ro60 −/− mESCs (Figure 1A, lanes 1–2). Moreover, cells lacking either Y1 or Y3 RNA exhibited slightly increased levels of the other RNA (Figure 1A, lanes 5–8 and 1B) that may reflect increased Ro60-mediated stabilization of the remaining Y RNA. Since Y1 and Y3 RNAs could have overlapping or redundant functions, we also generated mESCs lacking both RNAs (Figure 1A, lanes 9–10).

mESCs lacking Y RNAs are viable and divide normally. (A) RNA from two Ro60 −/− mESC lines, wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*) and two independent Rny1 −/− , Rny3 −/− and Rny1 −/− Rny3 −/− mESC lines were subjected to northern blotting to detect Y1 and Y3 RNAs. 5S rRNA, loading control. (B) Quantitation of Y1 and Y3 RNA levels in each cell line relative to wild-type mESCs (lane 3 in A). Data represent three biological replicates and are shown as mean ± SEM. P values were calculated using one-way ANOVA. *, P < 0.05 ***, P < 0.001. (C) Cell cycle profiles of wild-type, Ro60 −/− and two independent Rny1 − / − Rny3 − / − mESCs. BrdU incorporation was measured by flow cytometry. The percentage of cells in G1, S and G2/M phases of the cell cycle are shown. Data represent mean ± SEM (n = 6).

mESCs lacking Y RNAs are viable and divide normally. (A) RNA from two Ro60 −/− mESC lines, wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*) and two independent Rny1 −/− , Rny3 −/− and Rny1 −/− Rny3 −/− mESC lines were subjected to northern blotting to detect Y1 and Y3 RNAs. 5S rRNA, loading control. (B) Quantitation of Y1 and Y3 RNA levels in each cell line relative to wild-type mESCs (lane 3 in A). Data represent three biological replicates and are shown as mean ± SEM. P values were calculated using one-way ANOVA. *, P < 0.05 ***, P < 0.001. (C) Cell cycle profiles of wild-type, Ro60 −/− and two independent Rny1 − / − Rny3 − / − mESCs. BrdU incorporation was measured by flow cytometry. The percentage of cells in G1, S and G2/M phases of the cell cycle are shown. Data represent mean ± SEM (n = 6).

Y RNAs are not required for DNA replication

Although Y RNAs are reported to be essential for DNA replication in vertebrate species ( 38–40, 50–52), our Rny1 −/− Rny3 −/− mESCs lacked obvious growth defects. We therefore compared the cell cycles of wild-type, Ro60 −/− and two independent Rny1 −/− Rny3 −/− cell lines. Labeling with bromodeoxyuridine (BrdU), followed by flow cytometry revealed similar cell cycles for all four cell lines (Figure 1C). We conclude that Y RNAs are not essential for DNA replication in mESCs.

Y RNAs are required for Ro60 to accumulate to wild-type levels

Interestingly, in mESCs lacking both Y1 and Y3 RNAs, Ro60 levels decreased >2-fold (Figure 2A). To confirm that the reduced Ro60 levels were due to the lack of Y RNAs, we generated stable Rny1 −/− Rny3 − /− cells in which Rny1 and/or Rny3 transgenes were integrated at ectopic loci (Figure 2B). Although Y1 RNA expression from the transgene was comparable to that of wild-type cells, Y3 RNA expression was only 45% that of wild-type cells (Figure 2B, compare lane 1 with lanes 5 and 6). Importantly, expression of Y1 restored Ro60 expression in Rny1 −/− Rny3 −/− cells to near wild-type levels and expression of Y3 in these cells increased Ro60 levels over cells carrying only the empty vector (Figure 2C). We conclude that Y RNAs are required for Ro60 to accumulate to wild-type levels.

Y RNAs are important for Ro60 accumulation. (A) The levels of Ro60 in two Ro60 −/− mESC lines, wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*) and two independent Rny1 −/− , Rny3 −/− and Rny1 −/− Rny3 −/− mESC lines were measured by western blotting (left panel). ActB is a loading control. Right panel, quantitation of Ro60 protein levels from three biological replicates. Data are mean ± SEM normalized to ActB. P values were calculated using one-way ANOVA. ***, P < 0.001. (B) Northern blotting was used to compare the levels of Y RNAs in wild-type, Rny1 −/− Rny3 −/− mESCs and Rny1 −/− Rny3 −/− mESCs expressing the empty pUB6/V5/His A vector or the same vector containing Rny1, Rny3 or both transgenes. 5S rRNA is a loading control. (C) Lysates from wild-type, Rny1 − / − Rny3 − / − and Rny1 − / − Rny3 − / − mESCs expressing empty vector or the same vector containing Rny1, Rny3 or both transgenes were subjected to immunoblotting to detect Ro60 (left panel). ActB, loading control. Right panel, quantitation of Ro60 levels in each cell line compared to wild-type mESCs. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using two-tailed unpaired t-test **, P < 0.01 ***, P < 0.001. (D) Potential secondary structures for Y1, Y3 and the truncated Y1 RNA, Y1-S, in which the large internal loop of Y1 was replaced with a CUUG tetraloop. The Ro60 binding site is boxed. (E) Northern blot showing expression of Y1-S in two independent Rny1 −/− Rny3 −/− mESC lines. (F) Western blotting was used to compare Ro60 levels in wild-type mESCs, Rny1 −/− Rny3 −/− mESCs and Rny1 −/− Rny3 −/− mESCs expressing the empty vector or the same vector containing the Y1-S transgene (left panel). ActB is a loading control. Right panel, quantitation of Ro60 protein in each cell line compared to wild-type cells. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using one-way ANOVA. **, P < 0.01 ***, P < 0.001. (G) RT-qPCR analyses of Ro60 mRNA levels in wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*), Rny1 −/− , Rny3 − / − and Rny1 −/− Rny3 −/− mESCs. Data are mean ± SEM (n = 3), normalized to β-actin and Gapdh mRNAs. One-way ANOVA was used for statistical analyses.

Y RNAs are important for Ro60 accumulation. (A) The levels of Ro60 in two Ro60 −/− mESC lines, wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*) and two independent Rny1 −/− , Rny3 −/− and Rny1 −/− Rny3 −/− mESC lines were measured by western blotting (left panel). ActB is a loading control. Right panel, quantitation of Ro60 protein levels from three biological replicates. Data are mean ± SEM normalized to ActB. P values were calculated using one-way ANOVA. ***, P < 0.001. (B) Northern blotting was used to compare the levels of Y RNAs in wild-type, Rny1 −/− Rny3 −/− mESCs and Rny1 −/− Rny3 −/− mESCs expressing the empty pUB6/V5/His A vector or the same vector containing Rny1, Rny3 or both transgenes. 5S rRNA is a loading control. (C) Lysates from wild-type, Rny1 − / − Rny3 − / − and Rny1 − / − Rny3 − / − mESCs expressing empty vector or the same vector containing Rny1, Rny3 or both transgenes were subjected to immunoblotting to detect Ro60 (left panel). ActB, loading control. Right panel, quantitation of Ro60 levels in each cell line compared to wild-type mESCs. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using two-tailed unpaired t-test **, P < 0.01 ***, P < 0.001. (D) Potential secondary structures for Y1, Y3 and the truncated Y1 RNA, Y1-S, in which the large internal loop of Y1 was replaced with a CUUG tetraloop. The Ro60 binding site is boxed. (E) Northern blot showing expression of Y1-S in two independent Rny1 −/− Rny3 −/− mESC lines. (F) Western blotting was used to compare Ro60 levels in wild-type mESCs, Rny1 −/− Rny3 −/− mESCs and Rny1 −/− Rny3 −/− mESCs expressing the empty vector or the same vector containing the Y1-S transgene (left panel). ActB is a loading control. Right panel, quantitation of Ro60 protein in each cell line compared to wild-type cells. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using one-way ANOVA. **, P < 0.01 ***, P < 0.001. (G) RT-qPCR analyses of Ro60 mRNA levels in wild-type mESCs, mESCs carrying the empty lentiCRISPR v2 vector (WT*), Rny1 −/− , Rny3 − / − and Rny1 −/− Rny3 −/− mESCs. Data are mean ± SEM (n = 3), normalized to β-actin and Gapdh mRNAs. One-way ANOVA was used for statistical analyses.

Since the high affinity binding site for Ro60 is located within the Y1 and Y3 RNA stems (boxed in Figure 2D), we tested whether expression of this stem in Rny1 −/− Rny3 −/− cells was sufficient to restore Ro60 levels to that of wild-type cells. We generated two independent mESC lines stably expressing a truncated Y1 RNA, Y1-S, in which the large internal loop of Y1 was replaced with a CUUG tetraloop (Figure 2D). We confirmed that Y1-S was expressed in Rny1 −/− Rny3 −/− mESCs using northern blotting (Figure 2E). Expression of Y1-S RNA restored Ro60 levels to that of wild-type cells (Figure 2F). Using RT-qPCR, we demonstrated that Ro60 mRNA levels were unchanged in mESCs lacking one or both Y RNAs (Figure 2G). Thus, Y RNAs increase Ro60 levels through a post-transcriptional mechanism.

Y RNAs influence the subcellular distribution of Ro60

Previous studies revealed that mouse Ro60/Y RNA complexes are largely cytoplasmic, in part because Y RNA-binding masks a nuclear localization sequence on the Ro60 surface ( 21, 41). To determine how loss of Y RNAs affects the subcellular distribution of Ro60, we performed immunofluorescence on wild-type and Y RNA-deleted mESCs. As described ( 21), Ro60 is largely cytoplasmic in mESCs (Figure 3). The distribution of Ro60 in Rny1 −/− and Rny3 −/− mESCs was similar to that of wild-type cells, indicating that deleting either Y RNA is not sufficient to alter Ro60 distribution. However, in Rny1 −/− Rny3 −/− mESCs, less Ro60 was detected in the cytoplasm (Figure 3A). Although the decreased cytoplasmic Ro60 could be due to the reduced Ro60 protein in these cells (Figure 2A), some Ro60 accumulated in nuclear foci that co-localized with the nucleolar marker fibrillarin (Figure 3A and B).

Y RNAs regulate the subcellular distribution of Ro60. (A) Immunofluorescence was performed to detect Ro60 (green) in Ro60 − / − , wild-type, Rny1 −/− , Rny3 − / − , Rny1 −/− Rny3 −/− and Rny1 −/− Rny3 −/− mESCs stably expressing either Y1 and Y3 RNAs or Y1-S. Fibrillarin (red) is a nucleolar marker. Nuclei (blue) were detected with DAPI scale bar, 20 μm. Enlarged images of colocalizing Ro60 and fibrillarin are included in bottom panels. Inserts represent 2.5× zoom of selected areas with colocalizing Ro60 and fibrillarin. (B) Quantitation of mean nucleolar fluorescence intensity of Ro60 from >35 nucleoli in each of the indicated cell lines. P values were calculated using one-way ANOVA ***, P < 0.001.

Y RNAs regulate the subcellular distribution of Ro60. (A) Immunofluorescence was performed to detect Ro60 (green) in Ro60 − / − , wild-type, Rny1 −/− , Rny3 − / − , Rny1 −/− Rny3 −/− and Rny1 −/− Rny3 −/− mESCs stably expressing either Y1 and Y3 RNAs or Y1-S. Fibrillarin (red) is a nucleolar marker. Nuclei (blue) were detected with DAPI scale bar, 20 μm. Enlarged images of colocalizing Ro60 and fibrillarin are included in bottom panels. Inserts represent 2.5× zoom of selected areas with colocalizing Ro60 and fibrillarin. (B) Quantitation of mean nucleolar fluorescence intensity of Ro60 from >35 nucleoli in each of the indicated cell lines. P values were calculated using one-way ANOVA ***, P < 0.001.

To confirm that the altered Ro60 distribution in Rny1 −/− Rny3 −/− mESCs was due to loss of Y RNAs, we determined whether the changes in Ro60 distribution could be complemented by expressing Y RNAs in the cells. In Rny1 −/− Rny3 −/− mESCs stably expressing the two Y RNAs, the distribution of Ro60 was similar to that of wild-type cells (Figure 3A). Interestingly, in Rny1 −/− Rny3 −/− mESCs expressing only the Y1 RNA stem (Y1-S), the Ro60 cytoplasmic fluorescence increased, but the accumulation of Ro60 in nucleoli was similar to that of Rny1 −/− Rny3 −/− mESCs (Figure 3A). Thus, while the Y1 RNA stem is sufficient to restore Ro60 protein levels (Figure 2F) and at least partly restores the accumulation of Ro60 in cytoplasm, the other Y RNA module is necessary to prevent Ro60 from accumulating in nucleoli. As biochemical and structural data support a model in which this module acts as a gate to sterically prevent other RNAs from accessing the Ro60 cavity ( 25, 30), one possibility is that in the absence of this module, some Ro60 binds nascent rRNA or other nucleolar RNAs.

Identification of Y RNA-dependent Ro60-associated proteins

To determine the extent to which Y RNAs scaffold the interaction of Ro60 with other proteins, we identified the set of mESC proteins whose Ro60 association requires Y RNA. For this purpose, we established Ro60 −/− mESCs stably expressing a Ro60 cDNA with three copies of the FLAG epitope fused to the N-terminus (Ro60 −/− , FLAG3-Ro60) (Figure 4). By performing western blotting, we determined that the level of FLAG3-Ro60 in these cells, which was synthesized under control of the human ubiquitin C promoter, was approximately 2.5-fold higher than wild-type cells (Figure 4A, compare lanes 2 and 3). Notably, Y1 RNA levels also increased in these cells (Figure 4B, lane 3), consistent with a model in which this RNA is made in excess and stabilized by Ro60 binding. We used these mESCs as the parent line to generate Rny1 −/− , Rny3 −/− and Rny1 −/− Rny3 −/− derivatives. In support of a post-transcriptional mechanism for regulating Ro60 levels, the Ro60 in Ro60 −/− , FLAG3-Ro60 Rny1 −/− Rny3 −/− mESCs lacking both Y1 and Y3 decreased to 67% of the parent Ro60 −/− , FLAG3-Ro60 mESCs (Figure 4A, compare lanes 6 and 7 with lane 3). Whether the smaller decrease in Ro60 levels in these cells, compared to Rny1 −/− Rny3 −/− mESCs (Figure 2A) is due to overexpression of FLAG3-Ro60 or the presence of the N-terminal tag is not known. Proteins that co-purified with FLAG3-Ro60 from cells containing and lacking Y RNAs were identified using mass spectrometry (Figure 4C and Supplementary Table S2 ). Anti-FLAG eluates from untagged wild-type mESCs were negative controls. To increase the robustness of our mass spectrometry data, we performed three independent purifications. We defined potential interactors as those proteins that, in all three replicates, were both represented by at least two peptides and were at least 4-fold enriched in Ro60 −/− , FLAG3-Ro60 mESCs relative to untagged wild-type cells.

The association of Ro60 with partner proteins requires Y RNA (A) Ro60 and FLAG3-Ro60 levels in the indicated cell lines were compared by western blotting (left panel). Right panel, quantitation of three biological replicates. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using one-way ANOVA. **, P < 0.01. (B) The levels of Y RNAs in the indicated cell lines were compared by northern blotting. (C) Proteins identified by mass spectrometry in anti-FLAG eluates. Three replicates (R1, R2, R3) were performed. Those proteins for which the peptide spectrum matches were >4-fold higher in immunoprecipitates from Ro60 −/− , FLAG3-Ro60 mESCs than untagged wild-type cells in all three replicates are shown.

The association of Ro60 with partner proteins requires Y RNA (A) Ro60 and FLAG3-Ro60 levels in the indicated cell lines were compared by western blotting (left panel). Right panel, quantitation of three biological replicates. Data are mean ± SEM (n = 3) normalized to ActB. P values were calculated using one-way ANOVA. **, P < 0.01. (B) The levels of Y RNAs in the indicated cell lines were compared by northern blotting. (C) Proteins identified by mass spectrometry in anti-FLAG eluates. Three replicates (R1, R2, R3) were performed. Those proteins for which the peptide spectrum matches were >4-fold higher in immunoprecipitates from Ro60 −/− , FLAG3-Ro60 mESCs than untagged wild-type cells in all three replicates are shown.

Interestingly, of the nine proteins identified as likely specific components of our anti-FLAG eluates, Ro60 was the only protein that was present in immunoprecipitates from cells lacking both Y RNAs (Figure 4C and Supplementary Table S2 ). All other potential interactors, including the Mov10 5′ to 3′ RNA helicase ( 53), the mitochondrial and nuclear RNA-binding protein Lrpprc ( 54), Igf2bp1/Zbp1, Ssb/La, Ptbp1, the LINE-1 retrotransposon-encoded Orf1 and the Y-box proteins Ybx1 and Ybx3, were absent in immunoprecipitates from Rny1 −/− Rny3 −/− mESCs (Figure 4C). Although each of these proteins was described previously to co-purify with mouse or human Ro60 ( 32, 34, 55), our data demonstrate that the association of these proteins with Ro60 requires one or both Y RNAs.

Y1 and Y3 RNAs tether Ro60 to distinct binding partners

To determine which Y RNA was responsible for the association of Ro60 with each protein, we performed immunoprecipitations using our Ro60 −/− , FLAG3-Ro60 mESCs lacking one or both Y RNAs and detected the co-purifying proteins by western blotting. As expected, all examined proteins, including Lrpprc, Mov10, Igf2bp1/Zbp1 and Orf1 were present in anti-FLAG eluates from the parent Ro60 −/− , FLAG3-Ro60 mESCs (Figure 5A, lane 7) and absent in eluates from the same cells deleted for both Y1 and Y3 (lane 10). Notably, examination of Ro60 −/− , FLAG3-Ro60 mESCs lacking Y1 or Y3 revealed that these ncRNAs tether distinct sets of proteins to Ro60. Co-purification of Mov10, Lrpprc and Orf1 with Ro60 required Y1, while association of Ro60 with Igf2bp1/Zbp1 depended on Y3 RNA (Figure 5A and B). Due to the low levels of Ybx1 and Ybx3 that co-purified with Ro60 in mESCs, we were unable to determine whether their association with Ro60 required specific Y RNAs.

Y1 and Y3 tether distinct proteins to Ro60. (A and B) Lysates from untagged wild-type (lanes 1 and 6) and Ro60 −/− , FLAG3-Ro60 mESCs (lanes 2 and 7) were subjected to immunoprecipitation with anti-FLAG antibodies together with Ro60 −/− , FLAG3-Ro60 mESCs deleted for Y1, Y3 or both Y RNAs (lanes 3–5 and 8–10). Lysates (lanes 1–5) and proteins in immunoprecipitates (lanes 6–10) were analyzed by western blotting to detect the indicated proteins. ActB is a negative control. (C and D) Lysates from Ro60 −/− (lanes 1 and 6), untagged wild-type mESCs (lanes 2 and 7), and wild-type mESCs deleted for one or both Y RNAs (lanes 3–5 and 8–10) were subjected to immunoprecipitation with anti-Ro60 antibodies. Lysates (lanes 1to 5) and immunoprecipitated proteins (lanes 6 to 10) were subjected to western blotting to detect Lrpprc (C) and La (D). ActB is a negative control. (EH) Lysates from Ro60 −/− (lanes 1 and 6), wild-type mESCs (lanes 2 and 7), and wild-type mESCs deleted for one or both Y RNAs (lanes 3–5 and 8–10) were subjected to immunoprecipitation with antibodies against Ptbp1 (E), LINE-1 Orf1 (F), Mov10 (G) and Igf2bp1/Zbp1 (H). Lysates (lanes 1–5) and proteins in immunoprecipitates (lanes 6–10) were subjected to western blotting to detect the indicated proteins. In each blot, Ro60 was detected as a positive control and ActB (E) or Gapdh (F–H) was a negative control. *, nonspecific band.

Y1 and Y3 tether distinct proteins to Ro60. (A and B) Lysates from untagged wild-type (lanes 1 and 6) and Ro60 −/− , FLAG3-Ro60 mESCs (lanes 2 and 7) were subjected to immunoprecipitation with anti-FLAG antibodies together with Ro60 −/− , FLAG3-Ro60 mESCs deleted for Y1, Y3 or both Y RNAs (lanes 3–5 and 8–10). Lysates (lanes 1–5) and proteins in immunoprecipitates (lanes 6–10) were analyzed by western blotting to detect the indicated proteins. ActB is a negative control. (C and D) Lysates from Ro60 −/− (lanes 1 and 6), untagged wild-type mESCs (lanes 2 and 7), and wild-type mESCs deleted for one or both Y RNAs (lanes 3–5 and 8–10) were subjected to immunoprecipitation with anti-Ro60 antibodies. Lysates (lanes 1to 5) and immunoprecipitated proteins (lanes 6 to 10) were subjected to western blotting to detect Lrpprc (C) and La (D). ActB is a negative control. (EH) Lysates from Ro60 −/− (lanes 1 and 6), wild-type mESCs (lanes 2 and 7), and wild-type mESCs deleted for one or both Y RNAs (lanes 3–5 and 8–10) were subjected to immunoprecipitation with antibodies against Ptbp1 (E), LINE-1 Orf1 (F), Mov10 (G) and Igf2bp1/Zbp1 (H). Lysates (lanes 1–5) and proteins in immunoprecipitates (lanes 6–10) were subjected to western blotting to detect the indicated proteins. In each blot, Ro60 was detected as a positive control and ActB (E) or Gapdh (F–H) was a negative control. *, nonspecific band.

Since both Ro60 and Y1 RNA were overexpressed in Ro60 −/− , FLAG3-Ro60 mESCs (Figure 4), we confirmed the interactions of Ro60 with each of the identified proteins using wild-type mESCs. Using anti-Ro60 antibodies, we confirmed that the interaction of Lrpprc with Ro60 required Y1 RNA, while Ssb/La interacts with Ro60 through both Y1 and Y3 RNAs (Figure 5C and D), as expected from previous studies ( 23). Similarly, immunoprecipitations with antibodies against Ptbp1, Orf1, Mov10 and Igf2bp1/Zbp1 and Orf1 confirmed that their association with Ro60 depended on one or both Y RNAs (Figure 5E– H). Specifically, the association of Orf1 and Mov10 was largely dependent on Y1, the association of Igf2bp1/Zbp1 required Y3, while Ptbp1 interacted with both Ro60/Y RNA complexes (Figure 5E– H). Taken together, we conclude that one role of Y RNAs is to scaffold interactions of Ro60 with specific RNA-binding proteins to create specialized RNPs.


Amino acids as biosynthetic materials

Enhanced biosynthetic activities are an essential feature of metabolic reprogramming in cancer: they support cells to produce the macromolecules required for DNA replication, cell division, and subsequent tumor growth. Biosynthetic pathways or anabolic pathways convert simple metabolites (e.g., sugars and amino acids) to complex molecules through ATP-dependent processes. Amino acids are involved in synthesizing three primary macromolecules: proteins, lipids, and nucleic acids (Fig. 1). All 20 canonical amino acids are proteinogenic, but only a subset of amino acids is involved in nonessential amino acid (NEAA) synthesis (e.g., glutamine, glutamate, methionine, and phenylalanine). Among the amino acids involved in NEAA synthesis, glutamine supplies nitrogen for synthesizing the amide group of asparagine. It also contributes to the synthesis of several amino acids through its catabolism to glutamate. Glutamine is converted to glutamate, mainly by glutaminase (GLS) activity (Figs. 1, 4b) 42 . Glutamate can then be further converted to α-KG and other amino acids, such as alanine, aspartate, and phosphoserine, by aminotransferase reactions (Figs. 1, 4b) 43 .

Unlike the aforementioned glutamine-derived amino acids, which need glutamate as a nitrogen donor, asparagine requires glutamine for de novo synthesis (Figs. 1, 2e, 4b). Glutamine is a substrate for asparagine synthetase (ASNS), providing amide nitrogen to aspartate to produce asparagine. Arginine is another amino acid used to synthesize nonessential amino acids. It can serve as the precursor to proline or as an additional source of glutamate, both via the intermediacy of 1-pyrroline-5-carboxylate (P5C) (Fig. 1) 56 . 1-Pyrroline-5-carboxylate reductase (PYCR) converts P5C into proline, and 1-pyrroline-5-carboxylate dehydrogenase (P5CDH encoded by ALDH4A1) catalyzes the conversion of P5C into glutamate 57 . Serine and glycine are closely related and can be interconverted by serine hydroxymethyltransferase (SHMT1 and 2) (Fig. 1) 58 . SHMT is one of the key enzymes in folate-mediated one-carbon metabolism. One-carbon metabolism encompasses both the folate and methionine cycle and provides methyl groups for the one-carbon pools that are required for de novo nucleotide biosynthesis and DNA methylation 59 . Tetrahydrofolate (THF) serves as a universal one-carbon acceptor and can accept one-carbon from the conversion of serine to glycine (folate cycle), the oxidation of glycine to CO2 and NH3 by the glycine cleavage system (GCS) 6 or methionine to homocysteine conversion (methionine cycle) 59 . One-carbon bound THF exists in different oxidation states (5,10-methylene tetrahydrofolate (5,10-meTHF), 5-methyl-THF, formate (10-formyl THF)), and supports distinct biosynthetic functions, for example, 5,10-meTHF for pyrimidine biosynthesis and 5-methyl-THF for purine biosynthesis 59 .

a Reverse-transsulfuration pathway: Cysteine can be produced from methionine through the reverse-transsulfuration pathway. This pathway is a combination of the methionine cycle and transsulfuration pathway. Homocysteine, the intermediate of the first step in the transsulfuration pathway, is generated from the methionine cycle. Serine condenses with homocysteine, producing cystathionine. Cystathionine is then converted to cysteine and alpha-ketobutyrate by CGL. Key enzymes are in red circle. THF tetrahydrofolate, CBS cystathionine β-synthase, SAM S-adenosylmethionine, CGL cystathionine γ-lyase. b Polyamine synthesis: Polyamines (putrescine, spermine, and spermidine) are synthesized from the amino acid arginine, and are converted from one to another (in the order of putrescine to spermidine to spermine). SAM, as the precursor of dcSAM, is the major donor for constructing polyamine structures. Key enzymes are in red circle. ODC ornithine decarboxylase, AMD S-adenosylmethionine decarboxylase, SAM S-adenosylmethionine, dcSAM decarboxylated S-adenosylmethionine. c Nitrogen and carbon source for nucleic acids: Aspartate, glycine, and glutamine provide nitrogen, and glycine and one-carbon units from the folate cycle (as a form of formate) provide carbon for purines. Glycine is formate’s indirect precursor through one-carbon metabolism, providing formate for biochemical reactions in purine biosynthesis. Aspartate and glutamine are the main amino acids involved in pyrimidine synthesis. Carbon (C) is in yellow, and nitrogen (N) is in green. d GSH and NADPH as antioxidants: Reactive oxygen species (ROS) bind and damage cellular macromolecules. The oxidation of NADPH and GSH allows ROS to be reduced to an inactive state. GSH reduces hydrogen peroxide to water and becomes oxidized to GSSG by GPX. Oxidized glutathione (GSSG) is then reduced back to GSH by GR in the presence of NADPH. Enzymes are shown in red circles. GPX glutathione peroxidase, GR glutathione reductase, GSH reduced glutathione, GSSG oxidized glutathione, NADPH reduced nicotinamide adenine dinucleotide phosphate, NADP+ oxidized nicotinamide adenine dinucleotide phosphate. e Amidation reaction for asparagine synthesis: Asparagine is synthesized by an amidotransferase reaction, catalyzed by asparagine synthetase (ASNS). The conserved amide group nitrogen is in a red box, while the enzyme is in a red circle.

The sulfur containing amino acid methionine can provide cysteine by the reverse-transsulfuration pathway (Fig. 2a). Homocysteine generated from the methionine cycle is condensed with serine to become cystathionine by cystathionine β-synthase (CBS), and is then transformed to cysteine by cystathionine γ-lyase (CGL) (Fig. 2a). Methionine to cysteine conversion is connected to serine–glycine interconversion because the methionine cycle is part of one-carbon metabolism coupled with the folate cycle, where SHMT catalyzes serine–glycine synthesis (Fig. 1) 6 . While asparagine is not directly involved in NEAA biosynthesis, it acts as an amino acid exchange factor and promotes amino acid uptake, preferentially of serine/threonine and nonpolar amino acids 60 , and can potentially support further building block synthesis.

Amino acids provide both carbon and nitrogen for nucleic acid synthesis (Fig. 2c). Purine biosynthesis requires formate, bicarbonate, and three amino acids: aspartate, glycine, and glutamine. While glutamine and aspartate act as the nitrogen source for both nucleobases (N1 from aspartate and N3 and N9 from glutamine) and the amino group of purines (glutamine for adenine and aspartate for guanine), glycine can contribute to purine biosynthesis in two ways: by direct incorporation into the purine backbone (C4, C5, and N7) or by producing one-carbon units for biochemical reactions involved in purine biosynthesis (C2 and C8) (Fig. 2c) 5 . The critical carrier of one-carbon units in the latter process is 5,10-meTHF. 5,10-meTHF is further converted to formate (10-formyl THF), contributing C2 and C8 carbons to the nucleobase 4,7,61 . Pyrimidine biosynthesis is simpler than that of purine. In contrast to purines that are synthesized as ribonucleotides rather than as nucleobases, pyrimidines are synthesized first as nucleobases and then conjugated to phosphoribosyl pyrophosphate (PRPP) to yield the corresponding ribonucleotide. The pyrimidine ring is derived from glutamine, aspartate, and bicarbonate. For pyrimidine synthesis, aspartate acts as both carbon and nitrogen donors (N1, C4, C5, and C6), whereas glutamine contributes to N3 of the nucleobase and amino group of cytosine (Fig. 2c) 4 . The one-carbon unit derived from serine to glycine conversion is required for thymidylate synthesis. 5,10-meTHF serves as a one-carbon donor to transfer a methyl group to deoxyuridine monophosphate (dUMP) and produce deoxythymidine monophosphate (dTMP), a reaction catalyzed by thymidylate synthase (TS) 7,8 .

In addition to their primary role in the biosynthesis of nitrogenous metabolites, amino acids can supply carbon atoms for lipid biosynthesis. Under hypoxia, glutamine contributes to the acetyl-CoA pools needed for lipogenesis by being converted into pyruvate that reenters the TCA cycle 46,62 . BCAAs can also contribute to lipogenesis. In differentiated adipocytes, BCAA catabolic flux increases, and BCAA-derived acetyl-CoA accounts for approximately 30% of the lipogenic acetyl-CoA pools 3,63 . Essential amino acids (EAAs) act not only as carbon donors, but their ratio can also regulate lipogenesis by impacting lipogenic gene expression 64 . In bovine mammary epithelial cells, the “optimal” amino acid (AA) ratio (OPAA = Lys:Met 2.9:1 Thr:Phe 1.05:1 Lys:Thr 1.8:1 Lys:His 2.38:1 Lys:Val 1.23:1) upregulates lipogenic gene expression and alters the expression of key miRNAs involved in the control of lipogenic balance, implying a potentially important role of EAA ratios in lipid synthesis 64 . It would be interesting to see if this is conserved in other species as well as in cancer.


Discussion

In this paper, we present the first constraint-based model reconstruction and analysis of energy metabolism in Leishmania infantum, the causal organism of a vital neglected tropical disease, visceral leishmaniasis. From our simple model-based study, we explore the different facets of the Leishmania infantum energy metabolism and reveal novel features of parasite metabolism under varying environmental conditions. The model accounts for central metabolic processes that include ATP synthesis and production of key intermediates that are essential for biomass production/growth. Furthermore, the model consists of reactions characterized into 5 different compartments𠅏our cellular—the glycosome (characteristic compartment of the Trypanosomatids), the mitochondrion, the mitochondrial intermembrane space and the cytosol and an extracellular for exchange and transport of metabolites from the cell to the environment. The main point to note here is that none of the previous constraint-based models have considered the mitochondrial intermembrane space compartment [15, 17, 51]. The inclusion of this compartment was imperative, since the proton gradient established by oxidative phosphorylation is coupled to ATP synthesis between the mitochondrion and the intermembrane space, and not with the cytosol. Based on relevant proof from literature and appropriate sequence analysis, reactions have been assigned to specific subcellular locations in the model (giving a high confidence to those reactions having strong literature proof). As a result, around 54 reactions could be assigned to their appropriate locations with high confidence.

The iAS142 metabolic network was analyzed using flux balance analysis with respect to a novel metabolic demand/biomass reaction developed using 13C isotopic enrichment data acquired from L.mexicana promastigotes grown in medium containing 13C labeled glucose [19]. None of the previous FBA constraint-based models [15�] have used 13C isotope data for developing the biomass reaction. From the model analysis and validations, the strength of the iAS142 biomass in accurately predicting the intricate details of L. infantum metabolism could be fairly demonstrated. Further, the iAS142 biomass was compared with the biomass objective obtained from a previously published Trypanosoma cruzi iSR215 model to enlighten the influence of biomass objective on reaction flux distribution [15]. The objective behind this analysis was to highlight the importance of the biomass reaction and the variation in reaction fluxes on considering a different biomass function. This also brings forward the amount of scrutiny which should be maintained while performing modeling studies using FBA.

A two-tier validation of the model was performed to assess its importance in predicting the actual metabolic aspects as perceived from various experimental studies. The first stage of model validation was performed through prediction of growth phenotypes using in silico reaction knockout analysis and comparison with experimentally known phenotypes. With respect to available knockout phenotype information in Leishmania, the model could predict the corresponding known growth phenotypes associated with 7 reaction knockouts with an absolute accuracy of 100%. Apart from validation purposes, the reaction knockout study gave a distinct biological insight into the crucial role played by every reaction in L. infantum energy metabolism. Even though it is inappropriate to compare the model predictions with Trypanosoma species that show stage specific differences in metabolism and reside in different environments than Leishmania, the model predictions were compared with available non-stage specific Trypanosoma knockout phenotype data only to further stress on the fact that the model was indeed Leishmania specific giving biological reasoning behind every single gene deletion phenotype predicted from the model. A total of 61 lethal reactions were identified through single reaction deletions in the network and 55 non trivial lethal reaction pairs were proposed to be essential through double reaction deletions. Each one of these deletions constitutes a promising drug target and an experimentally testable hypothesis, which we considered further to show the chemotherapeutic intervention scenarios for 9 reactions predicted to be essential for parasite growth from the model reaction knockout analysis.

Even though the model predictions are usually validated through reaction knockouts, there are three important shortcomings in validating a constraint-based model through reaction knockout studies [15]. First, due to unavailability of full knockout data specific to L. infantum, many knockout validations were not made from L. infantum data alone, but also considering data obtained from other Trypanosomatid species. Second, as the model considered here is not a genome-scale model, there might be other essential pathways that might be connected to core energy metabolism but not considered in the model. Hence, it is possible that the model might predict a reaction to be non-lethal, even though in the whole genome scale context it might be lethal or vice versa. Third, the experimental knockout data for validation purposes were collected from heterogeneous studies that might have considered different experimental conditions or media to generate knockouts, which are not explicitly implemented in our model. Hence, we prefer to use the model reaction knockout analysis as a predictor of essential genes required for parasite growth rather than a basis for validating our model.

In order to overcome the limitations of validation through reaction knockout studies, by performing a robustness analysis with respect to changes in oxygen uptake in our model, we attempted to validate the model by identifying model conditions to discern the secretion of overflow metabolites. Leishmania is known to exhibit an overflow metabolism during which it secretes substantial amount of succinate, acetate, pyruvate, CO2 and small amounts of lactate [57, 58]. This secretion is largely controlled by differences in the oxygen and glucose concentrations in the medium. With variation in both glucose and oxygen uptake, we were able to observe secretion of all the aforementioned overflow metabolites. More specifically, model robustness analysis discovered the secretion of lactate via the D-lactate dehydrogenase reaction which was one of the most important aspects that verify the model indeed to be Leishmania specific. Sequence analysis and experiments indicate absence of a functional D-lactate dehydrogenase in Trypanosoma species [6, 72, 74]. Although, Trypanosoma species have been shown to demonstrate L-lactate secretion instead of D-lactate, the pathway for this mechanism is still unknown [6]. Thus, with respect to production and secretion of Leishmania specific overflow metabolites, the model could be appropriately validated.

Promastigote and amastigote specific metabolic scenarios were created from the model so as to observe the stage specific differences in the energy metabolism of L. infantum. 13C isotope resolved metabolomics in L. mexicana developmental stages revealed a reduction of glucose uptake, glutamate uptake and secretion of overflow metabolites in axenic amastigotes as compared to promastigotes [19]. Similarly, after incorporating the amastigote scenario in our model, we could capture the reduced rate of glucose and glutamate uptake and reduced exchange of overflow metabolites with the environment experimentally observed in amastigotes. With respect to other carbon sources, uptake of other non-essential amino acids like aspartate, proline and alanine also reduced. As a result, a large reduction in glycolysis, TCA cycle, ATP synthesis, and amino acid metabolism reaction fluxes could be observed in the amastigote scenario. Also, a distinctive reduction in extracellular oxygen intake and hydrogen ion release was observed probably signifying the parasite’s adaption to the hypoxic and acidic environment of the human macrophage. Despite the considerable reduction in reaction fluxes, flux through transketolase reactions in the amastigote scenario was high when compared to the promastigote scenario suggesting the hyperactivation of pentose phosphate shunt in fulfilling the demand for glucose-6-phosphate under glucose-deficient conditions. Also, in both the promastigote and amastigote metabolic scenarios, closure of glucose uptake even in abundance of other amino acids led to abolishment of parasite growth signifying that glucose is the most important and essential carbon source preferred by both the developmental stages of the parasite though they exist in completely different environments.

The reaction knockout studies could predict the enzymes of succinate fermentation as essential for the organism’s growth suggesting the role of succinate fermentation pathway in maintaining a redox balance within the glycosome by regenerating the NAD molecules consumed by the enzymes of upper glycolytic pathway [6, 18]. Furthermore, for model simulations both with only glucose and glucose supplemented with amino acids, the results exhibited the replenishment of the TCA cycle through C4 dicarboxylic acid intermediates like malate, fumarate and succinate produced via succinate fermentation pathway, proving the importance of glycosomal succinate fermentation in TCA anaplerosis. By tracing the flux distribution through the model reactions, we could hypothesize that the major reason for activation of succinate fermentation was the demand for synthesis of glutamate through the glutamate dehydrogenase and aspartate aminotransferase reactions. The same was observed through 13C resolved energy metabolism in L. mexicana promastigotes [18]. It is important to note here that it is the network structure of the L. infantum energy metabolism as implemented in the iAS142 model that governs this metabolic behavior. Also, it is through the same metabolic route that the cellular energy maintenance of the parasite is met. This particular route was also, preferred in both the promastigote and amastigote scenarios of energy metabolism that were recreated in the model. Fascinatingly, model reaction knockout studies also predicted cytosolic NADPH�pendent glutamate dehydrogenase to be crucial for Leishmania infantum. All this information point towards the role played by succinate fermentation and glutamate biosynthesis in driving energy metabolism further establishing the fact, that the enzymes of succinate fermentation and glutamate biosynthesis could possibly be novel therapeutic targets for the L. infantum parasite.

As a part of rational drug design in Leishmania species, identification of novel drug targets in an early discovery phase has become increasingly important so as to design new small molecule inhibitors that can serve as potential drug candidates against the parasite [22, 24]. The amastigote developmental stage is the most sought after for drug discovery as it is the stage that is the cause of infection in humans. Gene essentiality studies in metabolic networks identify probable chemotherapeutic targets, by qualitatively assessing the role of a particular protein played in growth of the organism [17, 75�]. To further demonstrate the applicability of FBA-based model analysis in quantitatively predicting and categorizing essential reactions to be good drug targets, we incorporated in silico chemotherapeutic intervention scenarios within our model-presumed amastigote stage for 9 essential reactions predicted by in silico reaction knockout analysis carried out in the iAS142 model. By partially reducing the optimal flux value of each of these individual reactions in the amastigote scenario, a minimum inhibition threshold a chemotherapeutic needs to exert on each reaction to achieve zero parasite growth was identified through this simulation. Accordingly, the predictions indicated that for glutamate dehydrogenase and phosphoglucomutase reactions in comparison to the other 7 reactions, a minimal reduction in enzyme activity/reaction flux brought about an immediate decline in the parasite growth suggesting their choice as an important drug target.


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Supplementary Figure 1 Subcellular fractions are enriched for distinct protein and RNA markers.

a, Widefield imaging of intact mES cells (left), mES nuclei after cytosol extraction, but excluding nonidet P-40 (NP-40) detergent in buffer (middle, control), and mES nuclei after cytosol extraction and ER removal (right). Nuclear (DAPI) stain shown in blue, and ER tracker dye in red. b, Western blots of protein markers for different subcellular compartments for HEK293 cells. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) for the cytoplasmic fraction (cy), small nuclear ribonucleoprotein U1 subunit 70 (SNRP70) for the nucleoplasmic fraction (np), and histone H3 for the chromatin fraction (ch). Protein from the whole cell (wc) is shown for comparison. c, RT-qPCR of RNA markers of different cellular compartments. For GAPDH and U1, the RT (reverse transcription) primers target spliced (mature) mRNAs, whereas for ACTIN mRNA, an intron is targeted. d-e, Western blots of protein markers for mES cells with (d) DMSO treatment and (e) NAI-N3 treatment. ACTIN for the cytoplasmic and nucleoplasmic fractions (cy and np), small nuclear ribonucleoprotein U1 subunit 70 (SNRP70) for the nucleoplasmic fraction (np), and histone H3 for the chromatin fraction (ch).

Supplementary Figure 2 Reverse transcription stops in the icSHAPE experiments are highly correlated in replicates.

Cumulative distribution plots of Pearson correlation coefficient of reverse transcription stops in replicates of RNA fractions in chromatin (ch), nucleoplasm (np), and cytoplasm (cy). Data from DMSO replicates are shown in red, in vitro NAI-N3 replicates in green, and in vivo NAI-N3 replicates in blue.

Supplementary Figure 3 Quality control of icSHAPE reactivity across replicates, fractions and different abundance of RNAs.

a,b, Scatter plot and Pearson correlation coefficients of icSHAPE reactivities between each pair of replicates and fractions in mouse (a) and human (b). c, Cumulative density plot of Pearson correlation coefficient of the number of RT stops between replicates at different RNA expression level thresholds. d, Scatter plots of NAI reads number versus RNA-seq RPKM * Length (nt).

Supplementary Figure 4 Chromatin fractions are enriched for lncRNA and pre-mRNA (intron) structural information.

a-b, Structural models of (a) Ribonuclease P RNA, and (b) Signal recognition particle (SRP) RNA. Models are generated from the ViennaRNA web service based on data from the RNASTRAND database. Nucleotides are colored with icSHAPE scores from the nucleoplasm fraction. c, Histograms of ratios of lncRNAs versus all transcripts in different cellular compartments for human and mouse. d, Histograms of ratios of the sequencing reads mapped to introns versus all transcripts in different cellular compartments for human and mouse. e-g, Violin plot of Gini index of icSHAPE data in exon versus in intron in (e) mRNAs from mES cells, (f) mRNAs from HEK293 cells depleted of RBP-binding sites (20nt) and (g) mRNAs from HEK293 cells depleted of RBP-binding sites (100nt).

Supplementary Figure 5 RNA structure plays a central role in connecting transcription, translation and RNA degradation.

a-i, Kernel density estimation (KDE) plots (a,c,e,g), scatter plots (b,d,f,h), and scatter plots with a higher read depth cutoff (read depth =200) (i) of transcription rate versus 5’UTR RNA structure in chromatin, translational efficiency versus 5’UTR RNA structure in cytoplasm, RNA half-life versus full-length-transcript RNA structure in nucleoplasm, and RNA half-life versus RNA structure in cytoplasm. The two-tailed p-value was calculated by python package function scipy.stats.pearsonr. j-l, Scatter plots of transcription rate versus 5’UTR RNA structure in chromatin. Transcription rates are from published datasets: Min, I. M. et al. Genes & development. 25, 742–754, 2011 (j) and Tastemel, M. et al. Stem Cell Res. 25, 250–255, 2017 (k). translational efficiency versus 5’UTR RNA structure in cytoplasm (l). Translational efficiency data are from a published report (Yoshikawa, H. et al. eLife. 7, 2018).

Supplementary Figure 6 Inverse correlations of RNA half-lives with RNA structures in different transcript regions and positive correlations of RNA translation efficiency with transcription rate.

a, Density plot of Gini index with half-life in 5’UTR, CDS and 3’UTR regions in cytoplasm and nucleoplasm. b, Density plot of translation efficiency versus transcription rate in mES cells. Figure is generated using published data (Ingolia, N. T. et al. Cell 147, 789–802, 2011, and Jonkers, I. et al. eLife. 3, 2014). The two-sided p-value was calculated by python package function scipy.stats.pearsonr.

Supplementary Figure 7 RNA is less structured in chromatin than in cytoplasm.

Heatmaps of average icSHAPE scores of different RNA types in various RNA fractions in human (including and excluding all known RNA binding protein (RBP) binding sites) and mouse. Dashed lines represent insufficient data.

Supplementary Figure 8 M 6 A, pseudouridylation (Ψ) and HNRNPC binding sites are more single-stranded.

a-c, Metagene profiles of icSHAPE scores at (a) m 6 A sites versus control (unmodified) sites with the m 6 A motif, (b) pseudouridylation (Ψ) sites versus random U sites, and (c) HNRNPC binding sites versus control sites with the polyU motif. P-values were calculated by single-sided Mann-Whitney U test, red stars mean p-values are less than 0.01.

Supplementary Figure 9 Many RBP-binding sites are enriched in RNA structurally changes regions.

The number of RBP-binding sites in the identified changes regions (red triangle) versus shuffled changes regions (blue dots).

Supplementary Figure 10 The binding of LIN28A and IGF2BP3 to their target RNAs is influenced by m 6 A modification.

a,b, RNA pull-down assays and RBP Western blots using RNA probes that contain unmodified A, m 6 A, and U mutation respectively from (a) transcripts IGF2BP3 binds to, and (b) LIN28A binds to m 6 A sites are marked with red ‘m’. Histograms show RNA pull down with three replicates. Western blots are done with (a) anti-IGF2BP3 antibody or anti-LIN28A antibody, after RNA pull down. The error bars represent standard deviation of replicates. c, Overlap of Lin28a binding sites (including both in Mettl3 knock-out (KO) and in wild-type (WT) mES cells) and m 6 A sites. For those overlapped sites, Lin28a binds more strongly at 45 out of 68 sites in Mettl3 KO, versus 23 out of 68 sites in WT mES cells.


<p>This section provides any useful information about the protein, mostly biological knowledge.<p><a href='/help/function_section' target='_top'>More. </a></p> Function i

Electroneutral transporter of the plasma membrane mediating the cellular uptake of the divalent metal cations zinc, manganese and iron that are important for tissue homeostasis, metabolism, development and immunity (PubMed:15642354, PubMed:27231142, PubMed:29621230).

Functions as an energy-dependent symporter, transporting through the membranes an electroneutral complex composed of a divalent metal cation and two bicarbonate anions (By similarity).

Beside these endogenous cellular substrates, can also import cadmium a non-essential metal which is cytotoxic and carcinogenic (By similarity).

Controls the cellular uptake by the intestinal epithelium of systemic zinc, which is in turn required to maintain tight junctions and the intestinal permeability (By similarity).

Modifies the activity of zinc-dependent phosphodiesterases, thereby indirectly regulating G protein-coupled receptor signaling pathways important for gluconeogenesis and chondrocyte differentiation (By similarity).

Regulates insulin receptor signaling, glucose uptake, glycogen synthesis and gluconeogenesis in hepatocytes through the zinc-dependent intracellular catabolism of insulin (PubMed:27703010).

Through zinc cellular uptake also plays a role in the adaptation of cells to endoplasmic reticulum stress (By similarity).

Major manganese transporter of the basolateral membrane of intestinal epithelial cells, it plays a central role in manganese systemic homeostasis through intestinal manganese uptake (PubMed:31028174).

Also involved in manganese extracellular uptake by cells of the blood-brain barrier (PubMed:31699897).

May also play a role in manganese and zinc homeostasis participating in their elimination from the blood through the hepatobiliary excretion (By similarity).

Also functions in the extracellular uptake of free iron. May also function intracellularly and mediate the transport from endosomes to cytosol of iron endocytosed by transferrin (PubMed:20682781).

Plays a role in innate immunity by regulating the expression of cytokines by activated macrophages (PubMed:23052185).

<p>Manually curated information which has been propagated from a related experimentally characterized protein.</p> <p><a href="/manual/evidences#ECO:0000250">More. </a></p> Manual assertion inferred from sequence similarity to i

<p>Manually curated information for which there is published experimental evidence.</p> <p><a href="/manual/evidences#ECO:0000269">More. </a></p> Manual assertion based on experiment in i


System metabolic engineering strategies for cell factories construction

Maofang Teng , . Guoqiang Zhang , in Systems and Synthetic Metabolic Engineering , 2020

6.3 Design and construction of novel biological parts/systems

Synthetic biology is an emerging area of research that can be described as the design and construction of novel artificial biological pathways, which include (1) the design and construction of new biological parts, devices and systems, and (2) the redesign of existing natural biological systems for useful purposes. Synthetic biology, integrated with engineering thought, is a cross-disciplinary research field developed on the basis of modern biology and systems science. It involves molecular biology, cell biology, evolutionary systematics, biochemistry, informatics, mathematics, computer technology and engineering, and other multidisciplinary disciplines. In other words, synthetic biology is the engineering of biology: the synthesis of complex, biologically-based (or inspired) systems, which display functions that do not exist in the nature. This engineering perspective can be applied into all levels of the hierarchy of biological structures-from individual molecules to whole cells, tissues and organisms. In essence, synthetic biology will implement the design of biological systems in a rational and systematic way. Science biological systems are a complex and unpredictable whole, the core molecular components are continuously optimized, designed and assembled through synthetic biology techniques and strategies to enhance the biological functions of existing systems, simulate and construct biological compositions, which contribute to create new biological functions and systems [13] . By artificially designing and constructing biological systems, people are provided with more powerful theories, tools and broadening intellectual vistas in solving energy, materials, environmental protection, and health issues [14] .

In 1978, the ideology of synthetic biology was first proposed by a Polish scientist pointing out that the discovery of restriction endonuclease not only provided people with tools for DNA recombination, but also could make use of existing genes to research and create new genes. Thereby people would be led into a new field, namely synthetic biology [15] . In the past dozens of years, synthetic biology has developed rapidly. Kolisnychenko et al. reduced the genome size by deleting some genes of Escherichia coli without affecting its basic life function, which paved the way for the genome modification of microbial strains [16] . Keasling et al. [17] obtained artemisinic acid by synthetic biological methods in Saccharomyces cerevisiae, which is the precursor of antimalarial drug. In addition, DNA synthesis is one of the technologies that promote the development of synthetic biology. Later, Gibson et al. synthesized and assembled a small bacterial genome that was transferred to another bacterial cell without DNA, creating a novel autonomous replicable microbial cell [18] . At present, based on genomic technology, synthetic biology research utilizing engineering strategies, standardized biological components, and constructing universal biological modules, facilitates the design and assembly of artificial biological systems with specific new functions.

In recent years, the tools and strategies of synthetic biology have been continuously expanded and improved. Through the construction and modulation of biological parts, the design and construction of new metabolic pathways, it is helpful to develop strains displaying better performance and improve the yield of biological products beneficial to humans. Here, the major research progress, application and achievements of synthetic biology tools and strategies in recent years will be introduced from the aspects of protein engineering and biological parts used for fine-controlled gene expression ( Fig. 6.1 ).

Figure 6.1 . Representative strategies of synthetic biology and metabolic engineering for cell factories construction.

Synthetic biology tools and strategies include promoter/RBS library, RNA based gene downregulation/ gene overexpression, CRISPR based gene editing/downregulation, synthetic scaffold, and so on.

6.3.1 Protein engineering improving element properties

Synthetic biology generally requires engineering cellular metabolic pathways. The common way to design and modify the metabolic pathways is to introduce heterogenous protein components into existing cellular pathways, and even knock existing proteins out [19] . So far, most of the components used in synthetic biology have come from natural organisms. Since synthetic biology is aimed at constructing new biological systems, protein modules that are relatively independent and assembly are required. However, the enzymes extracted from nature are limited by the substrate and environment, and cannot fully satisfy the desired characteristics and activities. Therefore, it is necessary to develop modular and standardized protein modules such as improving enzyme properties by introducing new protein catalytic functions to construct new synthetic pathways and optimize metabolic pathway efficiency, altering regulatory proteins to manipulate metabolic pathways and establishing protein scaffolds for metabolite channeling [20] .

Protein engineering have the potential to create novel functional proteins. Computational protein design is a structure-based approach for protein modification. According to specific studies such as the characteristic of reaction and substrate structure, the design of novel protein sequences, structures and functions depended on structures and atomistic computational simulations are performed to realize directed evolution of catalytic components and construct intelligent libraries [21] . Computational protein design tools can be exploited to identify the core domain of the protein structure, provided target sites for engineering and even allowed de novo design of enzymes. This can greatly reduce the cost of material, time, and labor. For example, in order to achieve new substrate specificity, Liu et al. [22] redesigned existing protein-ligand interactions based on catalytic mechanisms to alter the substrate specificity. They designed the lipoic acid ligase (lpal) by increasing the volume of the enzyme binding pocket, and finally obtained a fluorophore ligase with activity on resorufin. Siegel et al. [6] obtained an enzyme by de novo design that catalyzed the Diels-Alder reaction with the high stereoselectivity and substrate specificity.

The construction and modification of protein modules can also be used to increase the flux in biosynthetic metabolic pathways. It has been known that synthetic protein scaffolds or fusion proteins could spatially co-localize related enzymes to increase local enzyme concentration and improve the efficiency of product synthesis. Dueber et al. [23,24] expressed scaffolds with a modular protein-protein interaction domain to optimize the flux of engineered metabolic pathways in vivo. The scaffolds in pathways increase efficiency and produce higher product titers even at relatively low enzyme concentrations, which can effectively reduce the diffusion of intermediates, potentially increasing the rate of reaction by altering the interaction of scaffold proteins. This strategy has also been successfully applied to increase the efficiency of the mevalonate biosynthetic pathway. Lewicka et al. [25] expressed the fusion of pyrylate decarboxylase (Pdc) and alcohol dehydrogenase (AdhB) in E. coli, whose ethanol productivity was higher than that of cells coexpressing Pdc and AdhB.

In addition, spatial organization of proteins from cellular components can effectively limit toxic intermediates, increase local concentrations of enzymes and intermediates, and prevent unnecessary side reactions. Compartmentalization can be achieved by utilizing the natural organelles in eukaryotic cells. Prokaryotic cells can build bacterial microcompartment (BMC) similar to organelles in eukaryotic cells. The BMC is consisted of a protein shell and encapsulates the enzymes related to biosynthetic pathway [26] . For example, the introduction of a heterologous acetoin pathway in the mitochondria of Candida glabrata increased the concentration of enzymes and intermediates. After coupling with the mitochondrial pyruvate carrier (MPC), the production of acetoin was increased by 59.8% comparing to the cytoplasmic pathway [27] . The bacteriophage lysis protein E was encapsulated in recombinant BMCs, which could protect the cells from the toxicity [28] .

6.3.2 Design and engineering of biological parts

With the development of molecular biology, gene sequences are further classified as different functional modules to better understand the process of transcription and translation, such as promoters, repressors, activators, transcription units, protein coding regions, ribosome binding sites and terminator. However, a large number of known functional biological parts have not been standardized and have showed incompatibility, resulting in inconsistent work among components, greatly reduced efficiency, or inability to exert functions. New biological parts should be designed and synthesized by modifying existing or creating entirely new biological components to build a desired biological system.

With the advancement of DNA sequencing and DNA synthesis technology, the cost of gene synthesis and assembly has been decreased, which makes synthetic DNA possible. DNA synthesis involves redesigning target gene sequences and regulatory sequences. DNA sequences with the desired functions in different pathways can be selectively synthesized to avoid the effects of metabolic regulation in vivo.

To achieve rapid construction of biological parts and pathways, researchers have developed a series of new DNA assembly techniques, laying the foundation for the construction of highly efficient synthetic biology systems. Golden Gate assembly uses special type IIS restriction enzyme to cleave DNA recognition site to create a variable sticky end, then uses DNA ligase to achieve seamless assembly of multiple DNA fragments [29] . Gibson et al. [30] developed the Gibson assembly splicing multi-segment, which is an isothermal, scarless, one-step method. Vector with DNA fragment-overlapping region and several DNA fragments are cleaved when reacting with exonuclease, and subjected to seamless assembly under the synergistic action of DNA ligase and DNA polymerase. Ligase Cycling Assembly (LCA) is an approach to assembling shorter oligonucleotides or double-stranded DNA fragments into larger DNA structures [31] . In addition, there are other gene assembly technologies such as Polymerase Cycling Assembly (PCA) [32] , BioBrick assembly [33] , and Emulsion PCA [34] . In recent years, Wang et al. [35] developed a multiplexed automated genome engineering (MAGE) technology, in which gene combination diversity is achieved by simultaneous mutation of multiple sites in E. coli chromosome based on single-stranded DNA (ssDNA). This method was applied into S. cerevisiae and evolved yeast oligo-mediated genome engineering (YOGE) [36] . In addition, genomic engineering techniques based on transcriptional activator-like effector nucleases (TALENs) and Clustered Regularly Interspaced Short Palindromic Repeat Sequences (CRISPR) that are engineered can introduce incisions or break double-strand DNA down at specific sites and are capable to edit genes [37] , which have been widely used in synthetic biology. The development of gene assembly and gene editing technology allows for transcriptional control of natural and synthetic genomes as well as directed evolution of gene clusters in vivo or in vitro, facilitating remodeling of biosynthetic pathways and thereby accelerating the optimization of engineered microbial cells.

Due to the large biosynthetic gene clusters (BGC) of natural products and the complex regulation, it is difficult to precisely control the corresponding gene based on genetic manipulation. These BGCs can be composed of multiple genes, which are arranged in one or more operons under the complex and redundant regulation from outside or inside the gene, such as promoters, embedded feedforward and feedback cycle [38,39] . Therefore, it is hard to change the expression level of a single gene in the BGCs of natural product. Similarly, biological components and systems artificially designed and constructed are inevitably transferred to organisms for cultivation and functioning. However, the signal transduction pathways and metabolic networks from organisms may have some effects on synthetic biological components and systems. These factors have prompted researchers to simplify and reconstruct genes and even genomes based on DNA construction techniques including DNA synthesis and DNA assembly technology. It could make the synthetic biological systems more amenable to engineering efforts. During gene refactoring, all the untranslated region, regulatory protein genes and non-essential genes are removed. Then codons of essential genes are altered and scanned by computer to eliminate internal regulation [39] . By deleting non-essential genes and retaining essential genes associated with individual life activities, the genome can be simplified and the background of the chassis cells is easily defined and analyzed. For example, the researchers refactored the genome of bacteriophage T7 [40] . These modular DNA assembly schemes facilitate the emergence of a combination regulation strategies of manipulation pathway. The first step is to design optimal path when reconstituting the metabolic pathways of natural and non-natural chemicals. Next, the candidate enzymes derived from organisms can be introduced heterologously or in combination to establish new metabolic pathways. Combinatorial assembly and reconstitution of metabolic networks regulate the expression of related enzymes to optimize the synthesis of natural products, such as artemisinic [41] and catechin [42] .


Author information

Affiliations

Virginia Commonwealth University, Richmond, VA, USA

João MP Alves, Myrna G Serrano & Gregory A Buck

BAMBOO Team, INRIA Grenoble-Rhône-Alpes, Villeurbanne, France

Cecilia C Klein & Marie-France Sagot

Laboratoire Biométrie et Biologie Evolutive, CNRS, UMR5558, Université de Lyon, Université Lyon 1, Villeurbanne, France

Cecilia C Klein & Marie-France Sagot

Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil

Cecilia C Klein & Ana Tereza R Vasconcelos

Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil

João MP Alves, Flávia Maia da Silva, André G Costa-Martins, Marta MG Teixeira & Erney P Camargo

Laboratório de Ultraestrutura Celular Hertha Meyer. Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil


Watch the video: Biosynthesis of non essential amino acids-II (August 2022).