GN/ Conversion of genetic information from DNA to proteins

Paradigm
Paradigm
Published in
27 min readJul 21, 2021

Genetics biweekly vol.7, 7th July — 21st July

TL;DR

  • mRNA plays a key role in the conversion of genetic information from DNA to proteins. Their production is a delicate process. A research team has now identified a crucial factor.
  • Researchers deloped a new genetic circuit called the Equalizer that leads to uniform gene expression.
  • Protein design researchers have created a freely available method, RoseTTAFold, to provide access to highly accurate protein structure prediction. Scientists around the world are using it to build protein models to accelerate their research. The tool uses deep learning to quickly predict protein structures based on limited information, thereby compressing the time for what would have taken years of lab work on just one protein. Predicting intricate shapes of proteins vital to specific biological processes could speed treatment development for many diseases.
  • Researchers have discovered that a molecule found within many of the body’s cells kills germs by dissolving their protective membranes.
  • Scientists have uncovered a way to control many genes in engineered yeast cells, opening the door to more efficient and sustainable production of bio-based products.
  • There are striking similarities in the development of two types of specialized sensory cells: the so-called ‘hair cells’ that receive sound vibrations in the inner ear, and the Merkel cells that sense light touch at the surface of the skin. These developmental similarities are a legacy of shared evolutionary history.
  • To grow and multiply efficiently, bacteria must coordinate cell division with chromosome segregation. Key to this process is a protein called Nucleoid Occlusion Factor or Noc. A small and abundant molecule called Cytidine Triphosphate (CTP) is key to the functions of Noc. CTP binding enables Noc to ‘spread’ on DNA to form a large protein complex. CTP also ‘switches on’ the membrane-binding ability of Noc.
  • Researchers have described a novel primary immunodeficiency due to a mutation in AIOLOS. This acts through a novel pathogenic mechanism termed ‘heterodimeric interference’, whereby when two different proteins bind together in a heterodimer, the mutant protein hijacks the function of the normal protein. In a mouse model, they were able to restore some of the lost functions by interfering with the mutated protein, suggesting a possible therapeutic approach to disorders of this nature.
  • Engineers and plant pathologists have developed a way to engineer a protein that blocks fungi from breaking down cell walls, as well as a way to produce this protein in quantity for external application as a natural fungicide. The work could lead to a new way of controlling plant disease that reduces reliance on conventional fungicides.
  • Researchers have developed a new technique that can alter plant metabolism. Tested in tobacco plants, the technique showed that it could reduce harmful chemical compounds, including some that are carcinogenic. The findings could be used to improve the health benefits of crops.
  • And more!

Overview

Genetic technology is defined as the term which includes a range of activities concerned with the understanding of gene expression, advantages of natural genetic variation, modifying genes and transferring genes to new hosts. Genes are found in all living organisms and are transferred from one generation to the next. Gene technology encompasses several techniques including marker-assisted breeding, RNAi and genetic modification. Only some gene technologies produce genetically modified organisms.

Modern genetic technologies like genome editing would not be possible without all the previous generations of genetic technologies that have enabled scientists to discover what genes are, what they do and how DNA can be modified to add, remove or replace genes. You can find major genetic technologies development milestones via the link.

Gene Technology Market

  1. The valuation of the genetic engineering market is projected to escalate to USD 6.90 MN by the end of 2027.
  2. Global Genetic Engineering Market is projected to grow at 12.48% CAGR during the assessment period (2017–2027).
  3. North America holds the largest share in the global genetic engineering market, followed by Europe and the Asia Pacific, respectively.
  • Another research provider, MarketsandMarkets, forecasts the genome editing, genome engineering market to grow from USD 3.19 billion in 2017 to USD 6.28 billion by 2022, at a compounded annual growth rate (CAGR) of 14.5% during the forecast period. The key factors propelling market growth are rising government funding and growth in the number of genomics projects, high prevalence of infectious diseases (like COVID-19) and cancer, technological advancements, increasing production of genetically modified (GM) crops, and growing application areas of genomics.

Latest News & Research

Targeted protein degradation reveals a direct role of SPT6 in RNAPII elongation and termination

by Ashwin Narain, Pranjali Bhandare, Bikash Adhikari, Simone Backes, Martin Eilers, Lars Dölken, Andreas Schlosser, Florian Erhard, Apoorva Baluapuri, Elmar Wolf in Molecular Cell

The corona pandemic has ensured that the term “mRNA” is now also known to a large public beyond laboratories and lecture halls. However, the molecule is much more than an important component of a successful vaccine against the SARS-CoV-2 virus. “mRNAs are a central component of all living things on our planet. Without them life as we know it would not function,” says Elmar Wolf.

Wolf is a professor for tumour system biology at the Department of Biochemistry and Molecular Biology at the University of Würzburg. With his research team, he has now deciphered new details about the formation of mRNA which provide novel insights into how a fundamental process inside cells works: the transcription.

Transcription: If one can still remember their biology lessons, then they know that it is the process by which the genetic information in the DNA is translated into messenger RNA — or as how scientists like to call it: mRNA. Only the mRNA is capable of transmitting the information from the genetic material of the DNA in the nucleus of the cell to the sites of protein biosynthesis outside the nucleus. “The mRNA composition thus decides how the cells of our body look and how they function,” Wolf says.

The transcription process from DNA to mRNA sounds relatively simple: “You can think of transcription as an obstacle race. The RNA polymerase starts the reading process at the beginning of the gene, then moves through the entire gene and, finally reach the finish line,” Wolf explains. If the polymerase makes it through to the end, the mRNA has been produced. Scientists have long known that a lot can go wrong in this process. After all, many genes are a long “race track” with plenty of obstacles.

Transcription elongation defects result from processivity loss due to SPT6 depletion. (A) RNAPII activity simulation. Schematic (top) of RNAPII transcribing in an unperturbed, slowly, or unprocessive manner. Regions of transcripts labeled with 4sU during the experimental pulse are marked in green. Simulated RNAPII activity on an 80 kb gene, as observed with 4sU-seq, RNAPII ChIP-Rx, and DRB-4sU-seq methods. Local fold change (LFC) profiles from simulated 4sU-seq and ChIP-Rx in slow versus unperturbed conditions (bottom, left) and unprocessive versus unperturbed conditions (bottom, right). (B) LFC plot for example gene FAM208B. The plot shows log2 fold changes in read counts in non-overlapping 1 kb windows for the pooled 4sU-seq replicates in U2OSSPT6-AID-C1 cells in the presence versus absence of auxin. © Scatterplots comparing expression levels in the absence of auxin with processivity defects for expressed genes with an accurate LFC fit, according to gene length. Processivity defect is the rate of the exponential decline in 4sU signal along the gene body for auxin-treated samples compared with control samples. RPKS, reads/kilobase after spike normalization. (D) Schematic and metagene plot of total-RNAPII ChIP-Rx experiments. Metagene plot shows the read distribution over the gene body averaged overexpressed genes in U2OSSPT6-AID-C1 cells. Shadows around curves indicate SEM. (E) Metagene plot of pS2-RNAPII ChIP-Rx experiments. The plot shows the distribution of read density over the gene body overexpressed genes in U2OSSPT6-AID-C1 cells. p values (two-sided Wilcoxon test) for the difference (auxin/control), calculated from the density values of individual genes at each genomic location, are shown in a heatmap. Shadows around curves indicate SEM. (F) Relative and scaled heatmaps of pS2- or total-RNAPII ChIP-Rx experiments in U2OSSPT6-AID-C1 cells. Shown are Z scores calculated from normalized log2 fold changes between reads from control and auxin-treated conditions. Orange indicates fewer reads and blue indicates more reads in control condition. (G) LFC plot for example gene CRIM1. The plot shows log2 fold changes in read counts in non-overlapping 1 kb windows for pS2-RNAPII ChIP-Rx experiment in U2OSSPT6-AID-C1 cells with or without auxin. p value (likelihood ratio test) calculated from the values of the individual bins. (H) Scatterplots correlating processivity defects with pS2-RNAPII occupancy for genes with accurate LFC fits for the 4sU and ChIP-Rx data. Processivity defect is the rate of exponential decline in 4sU signal along the gene body for auxin-treated cells compared with control cells. The genes are stratified by expression level and length. Pearson’s correlation, t test.

In order to better understand what happens at the molecular level during the race, Wolf and his team took a close look at the process of transcription.

“We studied an important component of the RNA polymerase: the protein SPT6,” explains Wolf. The question they explored is: “Is SPT6 important for the process of transcription and — if so — in what way?”

What do the scientists do when they want to learn about the function of a protein: they remove it from the cells and see what happens. That’s exactly what Wolf and his team did. The result was quite clear: “Interestingly, RNA polymerase starts making mRNA even in the absence of SPT6,” Wolf described. But then it regularly gets stuck in difficult places — you could say that it falls over an obstacle.

SPT6 is essential for the recruitment of termination factors and prevents replication stress. (A) Schematic of quantitative mass spectrometry for identifying RNAPII-associated proteins. (B) Volcano plot showing proteins whose association with RNAPII changed in response to SPT6 depletion in U2OSSPT6-AID-C1 cells treated with auxin. Negative log2 fold change values indicate the protein requires SPT6 to associate with RNAPII. RNAPII-interacting proteins are shown as green. Selected termination factors are labeled. © Browser tracks showing CSTF2 ChIP-Rx experiments for C6ORF48 gene in U2OSSPT6-AID-C1 cells with or without auxin. (D) Metagene plots showing distribution of read density from CSTF2 ChIP-Rx positioned around PAS, averaged for 4.5 kb upstream and 12.5 kb downstream in U2OSSPT6-AID-C1 cells. Shadows around curves indicate SEM. (E) Top: immunoblots of U2OSSPT6-AID-C1 cells treated for the indicated times. Vinculin, loading control. Bottom: quantification of pRPA2 and γH2AX relative to RPA2 and H2AX, respectively, from two replicates. (F) Absolute and relative heatmaps of reads from γH2AX ChIP-Rx in U2OSSPT6-AID-C1 cells in the presence or absence of auxin (24 h). Shown are Z scores calculated from spike-normalized reads (left) or log2 fold changes between spike-normalized reads (right) from control and auxin conditions. Heatmaps are positioned around PAS, showing 4.5 kb upstream and 12.5 kb downstream regions. (G) Immunofluorescence images of proximity ligation assays (PLAs) between RNAPII and γH2AX in U2OSSPT6-AID-C1 cells. Scale bar: 10 μm. (H) Quantification of PLA foci per nucleus from the corresponding conditions in (G). p values (one-sided Wilcoxon test) for the likelihood of auxin being more than control. (I) Cell cycle distribution assay. Cells were treated with auxin (24 h), labeled with BrdU, stained with PI, and analyzed using flow cytometry. The amount of intercalating PI (top) and the correlation of BrdU to PI (bottom) are shown. Cells that are BrdU positive in S phase are marked green, while those that are negative are red. (J) Model of SPT6-induced RNAPII transcription changes.

This failure has two consequences that have a negative impact on cell function: On one hand, hardly any RNA polymerase makes it to the destination, which is why hardly any mRNA is produced. On the other hand, however, the gene itself is also affected. “Without SPT6, the polymerase destroys the obstacles and the racetrack, which is why functional RNA polymerases are then unable to find their way,” says Wolf. Thus, it is clear that the SPT6 protein is a central element in the production of mRNA in cells.

With these findings, the researchers are helping to shed more light on the process of transcription: “Until now, scientists had assumed that the only thing that mattered for mRNA production was how many RNA polymerases started transcription,” Wolf says.

Thanks to the results that have now been published, it is now clear that by no means all RNA polymerases that start the transcription process actually make it to the end of the gene and that the protein SPT6 is essential for this arrival.

Accurate prediction of protein structures and interactions using a three-track neural network

Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, David Baker in Science

Scientists have waited months for access to highly accurate protein structure prediction since DeepMind presented remarkable progress in this area at the 2020 Critical Assessment of Structure Prediction, or CASP14, conference. The wait is now over.

Researchers at the Institute for Protein Design at the University of Washington School of Medicine in Seattle have largely recreated the performance achieved by DeepMind on this important task.

Unlike DeepMind, the UW Medicine team’s method, which they dubbed RoseTTAFold, is freely available. Scientists from around the world are now using it to build protein models to accelerate their own research. Since July, the program has been downloaded from GitHub by over 140 independent research teams.

Network architecture and performance. (A) RoseTTAFold architecture with 1D, 2D, and 3D attention tracks. Multiple connections between tracks allow the network to simultaneously learn relationships within and between sequences, distances, and coordinates (see methods and fig. S1 for details). (B) Average TM-score of prediction methods on the CASP14 targets. Zhang-server and BAKER-ROSETTASERVER were the top 2 server groups while AlphaFold2 and BAKER were the top 2 human groups in CASP14; BAKER-ROSETTASERVER and BAKER predictions were based on trRosetta. Predictions with the two-track model and RoseTTAFold (both end-to-end and pyRosetta version) were completely automated. © Blind benchmark results on CAMEO medium and hard targets; model accuracies are TM-score values from the CAMEO website (https://cameo3d.org/).

Proteins consist of strings of amino acids that fold up into intricate microscopic shapes. These unique shapes in turn give rise to nearly every chemical process inside living organisms. By better understanding protein shapes, scientists can speed up the development of new treatments for cancer, COVID-19, and thousands of other health disorders.

“It has been a busy year at the Institute for Protein Design, designing COVID-19 therapeutics and vaccines and launching these into clinical trials, along with developing RoseTTAFold for high accuracy protein structure prediction. I am delighted that the scientific community is already using the RoseTTAFold server to solve outstanding biological problems,” said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine, a Howard Hughes Medical Institute investigator, and director of the Institute for Protein Design.

In the new study, a team of computational biologists led by Baker developed the RoseTTAFold software tool. It uses deep learning to quickly and accurately predict protein structures based on limited information. Without the aid of such software, it can take years of laboratory work to determine the structure of just one protein.

RoseTTAFold, on the other hand, can reliably compute a protein structure in as little as ten minutes on a single gaming computer.

Enabling experimental structure determination with RoseTTAFold. (A and B) Successful molecular replacement with RoseTTAFold models. (A) SLP. (top) C-terminal domain: comparison of final refined structure (gray) to RoseTTAFold model (blue); there are no homologs with known structure. (bottom) N-terminal domain: refined structure is in gray, and RoseTTAFold model is colored by the estimated RMS error (ranging from blue for 0.67 Å to red for 2 Å or greater). 95 Cɑ atoms of the RoseTTAFold model can be superimposed within 3 Å of Cɑ atoms in the final structure, yielding a Cɑ-RMSD of 0.98 Å. In contrast, only 54 Cɑ atoms of the closest template (4l3a, brown) can be superimposed (with a Cɑ-RMSD of 1.69 Å). (B) Refined structure of Lrbp (gray) with the closest RoseTTAFold model (blue) superimposed; residues having estimated RMS error greater than 1.3 Å are omitted (full model is in fig. S5C). © Cryo-EM structure determination of p101 Gᵦᵧ binding domain (GBD) in a heterodimeric PI3Kᵧ complex using RoseTTAFold. (top) RoseTTAFold models colored in a rainbow from the N terminus (blue) to the C terminus (red) have a consistent all-beta topology with a clear correspondence to the density map. (bottom) Comparison of the final refined structure to the RoseTTAFold model colored by predicted RMS error ranging from blue for 1.5 Å or less to red 3 Å or greater. The actual Cɑ-RMSD between the predicted structure and final refined structure is 3.0 Å over the beta-sheets. Figure prepared with ChimeraX (35).

The team used RoseTTAFold to compute hundreds of new protein structures, including many poorly understood proteins from the human genome. They also generated structures directly relevant to human health, including those for proteins associated with problematic lipid metabolism, inflammation disorders, and cancer cell growth. And they show that RoseTTAFold can be used to build models of complex biological assemblies in a fraction of the time previously required.

RoseTTAFold is a “three-track” neural network, meaning it simultaneously considers patterns in protein sequences, how a protein’s amino acids interact with one another, and a protein’s possible three-dimensional structure. In this architecture, one-, two-, and three-dimensional information flows back and forth, thereby allowing the network to collectively reason about the relationship between a protein’s chemical parts and its folded structure.

Complex structure prediction using RoseTTAFold. (A and B) Prediction of structures of E.coli protein complexes from sequence information. Experimentally determined structures are on the left, RoseTTAFold models, on the right; the TM-scores below indicate the extent of structural similarity. (A) Two chain complexes. The first subunit is colored in gray, and the second subunit is colored in a rainbow from blue (N-terminal) to red (C-terminal). (B) Three chain complexes. Subunits are colored in gray, cyan, and magenta. © IL-12R/IL-12 complex structure generated by RoseTTAFold fits the previously published cryo-EM density (EMD-21645).

“We hope this new tool will continue to benefit the entire research community,” said Minkyung Baek, a postdoctoral scholar who led the project in the Baker laboratory at UW Medicine.

A synthetic circuit for buffering gene dosage variation between individual mammalian cells

by Jin Yang, Jihwan Lee, Michelle A. Land, Shujuan Lai, Oleg A. Igoshin, François St-Pierre in Nature Communications

The function of a protein can depend on its abundance in a cell. So, when investigating the properties of a new protein, it is essential to make sure that the same amount is produced by every cell. Researchers at Baylor College of Medicine and Rice University have found a new way to do just that through the creation of new genetic circuits called Equalizers.

The findings show how researchers engineered these genetic circuits to buffer protein output from variations in the number of copies of the gene inside the cell, thereby helping to create consistent protein expression. This property is called “gene dosage compensation.”

The researchers use an analogy of heating a house to help explain how Equalizers work. Imagine using randomly placed space heaters to heat your home. To ensure each room gets a heater you would purchase some extra ones, but that would mean some rooms might have extra heaters. Those rooms might be too hot, so a solution would be to have thermostats on each heater to downregulate the heat when a room becomes too hot. Those thermostats act as the Equalizer.

Gene expression from ideal dosage compensation circuits does not vary with plasmid copy number. a Expression from promoters with no control circuitry (i.e. open-loop circuits) is proportional to the plasmid copy number. b An ideal dosage compensation system uses control mechanisms to tune the per-copy expression rate, thereby maintaining constant expression regardless of copy number.

Researchers typically encode genes to be expressed on circular pieces of DNA called plasmids. Excess plasmids are often used to ensure that most cells get one, but some cells will get several. The Equalizer is composed of transcriptional negative feedback and post-transcriptional incoherent feedforward loops. These loops counteract the presence of extra plasmids: they sense the outputs, in this case the proteins and mRNAs that the plasmids produce, and tune down their expression if they rise too high.

“We didn’t invent the parts, but rather we invented a new way to connect them together into a circuit,” said Jin Yang, who shared co-first authorship of the paper with graduate student Jihwan Lee of Rice University. Jin was a bioengineering undergrad at Rice University while developing this work and currently is a Ph.D. student at the Massachusetts Institute of Technology.

“In natural systems, some gene networks must control gene dosage variation to remain functional and conserve their properties. We repurposed and combined two types of gene dosage compensation circuits to create a version that enables uniform expression of any protein scientists want to produce in the lab.”

Equalizer-L has superior gene dosage compensation than an alternative circuit that combines post-transcriptional NF and IFF motifs. a–d Circuit schematics. mScarlet-I (RFP) and mCitrine (YFP) are reporters of circuit output and gene dosage, respectively. CMV © and OLP (d) do not have dosage compensation circuitry and are used as controls for Equalizer-L (a) and HYB (b), respectively. e Representative circuit output histograms. For all experiments (e–h), Equalizer-L was induced with 1 ng/mL of doxycycline. f HYB produces high cell-to-cell circuit output variability. The gene-dosage values were normalized to those obtained when transfecting 1 ng of plasmid. The circles represent independent transfections. n = 36 per circuit (6 per dose and 6 doses per circuit). The dashed lines are trend lines (linear for Equalizer-L and CMV; exponential for HYB and OLP). The inset shows the trend lines for the entire range of gene-dosage reporter levels. See Supplementary Statistics for statistical comparisons. Equalizer-L (g) showed superior gene dosage compensation than HYB (h) at the population level. The gene-dosage and circuit output values were normalized to those obtained when transfecting 1 ng of plasmid. Dashed lines indicate trend lines (hyperbolic for CMV and linear otherwise). Sample sizes are as in (f). p < 0.0001 for the two-sided Welch’s t-test comparing the trendline slopes of Equalizer-L and HYB.

Negative feedback and incoherent feedforward circuit subcircuits can each help compensate for gene dosage, but the researchers found that coupling the two improved overall performance. This is because each circuit is not perfect. For example, the incoherent feedforward loop can saturate because it requires other proteins that are present in limited quantities in the cell. The negative feedback loop is limited in its inhibitory capacity, similar to a leaky sink faucet that cannot be fully closed. But combining these two imperfect circuits produced robust performance, with each circuit helping mitigate the limitation of the other.

“The process we used for these findings was a collaborative effort bringing together computer simulations and biology. This is similar to how engineers work — they draw up their plans, create a model and then build their structure,” said Dr. Oleg Igoshin, professor of bioengineering, of biosciences and of chemistry at Rice University and a senior author on the paper. “In this case we were able to create our model and show the effectiveness through computational models before it was then synthetically engineered in the lab.”

A replicating variant of Equalizer-L enables the development of extra-chromosomal cell lines that have stable gene expression with low cell-to-cell variation.a Over the course of >8 weeks, the Equalizer-L episome produced similar cell-to-cell variation as a chromosomally integrated CMV cassette (CMV cell line) and lower variation than episomes with unregulated promoters. HEK293 cells were used. For all experiments a–c, Equalizer-L was induced with 1-ng/mL doxycycyline. The error bars represent SEM. n = 4 independent trials. Tukey’s multiple comparison test was used to compare the Equalizer-L episome with the other conditions. ns not significant; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. b Representative images of HEK293 cells expressing EGFP from episomes or the chromosome at 23 days post transfection. Each image is displayed with a linear lookup table with the minimum set to 0 and the maximum set to the sum of the mean intensity value and three standard deviations (see Methods). This approach enables a qualitative comparison of the cell-to-cell expression variability despite large differences in mean circuit output. Insets, binary masks to help identify regions of the images that correspond to cells (magenta region). Scale bar, 50 μm.

But why is minimizing expression variation important when it comes to biological research?

“The effect of a protein can depend on its abundance in a cell. If you’re studying a new protein and its concentration is too low, you may not be able to observe its function in the cell. If its concentration is too high, the protein may mislocalize, aggregate, produce cytotoxicity or otherwise produce responses that are not physiological. It is therefore important that a protein is expressed at the desired level in every cell under investigation,” said Dr. François St-Pierre, assistant professor of neuroscience and McNair Scholar at Baylor and corresponding author of this study. “We believe Equalizers will be of high value both for basic research and for industry.”

Others who took part in this research include Michelle A. Land and Shujuan Lai, both with Baylor College of Medicine.

A human apolipoprotein L with detergent-like activity kills intracellular pathogens

by Ryan G. Gaudet, Shiwei Zhu, Anushka Halder, Bae-Hoon Kim, Clinton J. Bradfield, Shuai Huang, Dijin Xu, Agnieszka Mamiñska, Thanh Ngoc Nguyen, Michael Lazarou, Erdem Karatekin, Kallol Gupta, John D. MacMicking in Science

Cells, like many of us, fend off germs with cleaning products. Researchers have discovered that a molecule made throughout much of the body wipes out invading bacteria like a detergent attacking an oily stain.

This killer cleaner, a protein known as APOL3, thwarts infections by dissolving bacterial membranes, Howard Hughes Medical Institute Investigator John MacMicking and his colleagues. His team tested the protein on the food-poisoning bacteria Salmonella and other similar microbes.

The work offers new insight into how human cells defend themselves against infection, a process termed cell-autonomous immunity. While scientists knew that cells could attack bacterial membranes, this study uncovers what appears to be the first example of a protective intracellular protein with detergent-like action.

MacMicking hopes the findings could one day aid efforts to develop new treatments for infections. “This is a case where humans make their own antibiotic in the form a protein that acts like a detergent,” says MacMicking, an immunologist at Yale University. “We can learn from that.”

When it comes to defending the human body, the specialized cells of the immune system act as a crew of cellular bodyguards. But the same alarm signals that mobilize these cells can also activate average citizens. A signal called interferon gamma, for instance, cranks up protein production in non-immune cells that compose our tissues and organs. But scientists know little about how such proteins help cells fight pathogens.

The researchers infected some of these non-immune cells with a strain of Salmonella, which invades cells’ watery interiors. Salmonella belongs to a class of bacteria bounded by two membranes. The outer bacterial membrane acts like armor, protecting the inner bacterial membrane from threats like antibiotics.

The team found that the interferon gamma alarm signal could prevent Salmonella from taking over human cells, but the researchers didn’t know which proteins came to the rescue. MacMicking’s team screened more than 19,000 of the human cells’ genes, looking for ones that might encode protective proteins. That work led the researchers to discover APOL3, which receives assistance from a second molecule, GBP1, and probably others. Using high-resolution microscopy and other techniques, the team pieced together the mechanism: GBP1 damages a bacterium’s outer membrane, allowing APOL3 through so it can break apart the inner membrane — the “coup de grace” that kills the bacterium, MacMicking says.

Like a laundry detergent, APOL3 possesses parts attracted to water and parts drawn to grease. Instead of removing dirt from fabric, these components remove chunks of the bacterial inner membrane, which is composed of greasy molecules called lipids.

This process must be highly selective, MacMicking says, since APOL3 needs to avoid attacking membranes of the human cell itself. The team found that APOL3 avoids cholesterol, a major constituent of cell membranes, and instead targets distinctive lipids favored by bacteria.

APOL3 appears likely to be in the toolbox of many cells. MacMicking’s team showed it defends cells within the blood vessels and gut. Because APOL3 appears in a variety of body tissues, the scientists believe it offers wide protection.

The discovery of this detergent-like molecule within non-immune cells “adds more evidence to the view that any cell in the body can be part of the immune system,” says Carl Nathan, who studies host-pathogen interactions at Weill Cornell Medical College, and who was not involved in this research. “It also adds a new example of one of the limited ways living things kill each other,” he notes.

Whether perforating, poisoning, or starving a pathogen, the immune system has developed several methods for killing threatening cells. APOL3 joins the group of mechanisms already known to fatally break down membranes, Nathan says.

Researchers are still a long way from being able to apply this discovery to therapies for infections. But deciphering the body’s defenses could give humanity new tools against microbes that are increasingly evolving ways to thwart conventional antibiotics. Dialing up cellular detergents and other devices the body uses to kill bacteria, for instance, could help supplement the natural immune response, MacMicking says.

Efficient multiplexed gene regulation in Saccharomyces cerevisiae using dCas12a

by Klaudia Ciurkot, Thomas E Gorochowski, Johannes A Roubos, René Verwaal in Nucleic Acids Research

Scientists have uncovered a way to control many genes in engineered yeast cells, opening the door to more efficient and sustainable production of bio-based products.

The study by researchers from DSM’s Rosalind Franklin Biotechnology Center in Delft, the Netherlands, and the University of Bristol has shown how to unlock CRISPR’s potential for regulating many genes simultaneously.

Baker’s yeast, or Saccharomyces cerevisiae to give it it’s full name, is considered as a workhorse for biotechnology. Not only has it been used for producing bread and beer for thousands of years, but today it can also be engineered to produce an array of other useful compounds that form the basis of pharmaceuticals, fuels, and food additives. However, achieving optimal production of these products is difficult, requiring the complex biochemical networks inside the cell to be rewired and extended through the introduction of new enzymes and the tuning of gene expression levels.

Effect of NLS number and position in dCas12a E925A Mxi1 fusions. (A) Genetic diagram of plasmids used for expression of dCas12a E925A Mxi1 with NLS fusions (pC-Mxi1, pC-Mxi1-NLS, pC-NLS-Mxi1-NLS, pC-NLS, pC-NLS-Mxi1). (B) Repression pattern in strain FR003 with eGFP integrated into INT4 and © strain FR009 with eGFP integrated into INT2. CRISPRi was assessed using two gRNAs targeting the Kl_ENO1 promoter controlling eGFP expression. Bars represent fold repression between targeting and non-targeting gRNA ±1 standard error (n = 4). Dashed line indicates 1-fold change (i.e. no repression)

Klaudia Ciurkot, first author of the study and an EU-funded industrial PhD student based at DSM stated: “To overcome the challenges of optimising S. cerevisiae cells for bio-production, we explored the use of a less widely employed CRISPR technology based on the Cas12a protein. Unlike the Cas9 protein that is more commonly used, Cas12a can be rapidly programmed to interact with sequences that are responsible for controlling gene expression and easily targeted to many different sequences at the same time. This made it an ideal platform for carrying out the complex gene regulation often required for producing industrially relevant compounds.”

She went on to add: “What was particularly exciting for me was that this study is the first to demonstrate Cas12a’s ability to control gene expression in S. cerevisiae and through joint research across DSM and the University of Bristol, we were able to figure out the rules for how this system is best designed and used.”

Thomas Gorochowski, a co-author on the work and Royal Society University Research Fellow based in the School of Biological Sciences at the University of Bristol further stated: “It is hugely exciting that Cas12a has been shown to work so well for gene regulation in the yeast S. cerevisiae, an organism that has huge industrial importance. In addition, the systematic approach we have taken to pull apart and analyse the many difficult aspects of the system, act as a firm foundation for future optimisation.”

Simultaneous downregulation of β-carotene production with dCas12a E925A NLS Mxi1 and single crRNA array. (A) Carotenoids production in S. cerevisiae is a multistep pathway catalysed by three enzymes: crtE, crtYB and crtI derived from Xanthophyllomyces dendrorhous. The pathway can be downregulated using dCas12a in sinlgeplex manner with a gRNA or multiplex using single crRNA arrays targeting promoters in front of crt genes resulting in decreased enzyme levels of crtE, crtYB and crtI and ultimately decreased amounts of carotenoids. Abbreviations: FPP, farnesyl pyrophosphate; GGPP, geranylgeranyl pyrophosphate. (B) β-carotene and phytoene levels in strain CAR-041 upon singleplex downregulation with dCas12a and gRNAs harbouring a single spacer complementary to promoters in front crt genes. © Cell pellet obtained after growing transformants of strain CAR-041 with (left panel) gRNAs used in singleplex downregulation for targeting crtI, crtE and crtYB. For comparison transformants with non-targeting gRNA (gNone) and wild type strain; (right panel) single crRNA arrays targeting simultaneously crtE, crtYB and crtI, solely crtI and non-targeting control. Columns correspond to biological replicates. (D) β-carotene and phytoene levels in strain CAR-041 upon multiplex downregulation with dCas12a and single crRNA array. Arrays with three targeting spacers (array_1–6) targeting promoters Sbay_TDH3, Smik_TEF1 and Kl_ENO1 which control expression of crtE, crtYB and crtI, respectively are shown in dark grey whereas arrays with a single targeting spacer (array_7–9) targeting Kl_ENO1 promoter controlling crtI and two non-targeting spacer in light grey. Non-targeting control (array_10) is depicted in white. Bars represent mean ± 1 standard deviation (n = 2). (E) Effect of dCas12a expression in wild type and carotenogenic strains. DC001 Cas12a, DC002 dCas12a D832A, DC003 dCas12a E925A, CAR-034 carotenogenic strain, CAR-041 carotenogenic strain with genomically integrated dCas12a E925A Mxi1 controlled by the TEF1 promoter, CAR-042 carotenogenic strain with genome integrated dCas12a E925A Mxi1 controlled by the PGI1 promoter.

In addition to analysing how the Cas12a-based system is best engineered, the scientists went on to show its use in robustly controlling the production of β-carotene — an industrially important compound used in production of food additives and nutraceuticals.

René Verwaal, senior author and Senior Scientist at DSM ended by stating: “By demonstrating the capabilities of this system to control the biosynthesis of β-carotene, we have opened the gates to its broader application for other key bio-based products. I cannot wait to see how our system is used to develop more sustainable production platforms for everyday products we all rely on.”

POU4F3 pioneer activity enables ATOH1 to drive diverse mechanoreceptor differentiation through a feed-forward epigenetic mechanism

by Haoze V. Yu, Litao Tao, Juan Llamas, Xizi Wang, John D. Nguyen, Talon Trecek, Neil Segil in Proceedings of the National Academy of Sciences

The sensory cells in the inner ear and the touch receptors in the skin actually have a lot in common, according to a new study from the USC Stem Cell laboratory of Neil Segil.

“There are striking similarities in the development of two types of specialized sensory cells: the so-called ‘hair cells’ that receive sound vibrations in the inner ear, and the Merkel cells that sense light touch at the surface of the skin,” said Segil, who is a Professor in the Department of Stem Cell Biology and Regenerative Medicine, and the USC Tina and Rick Caruso Department of Otolaryngology — Head and Neck Surgery. “Ultimately, these developmental similarities are a legacy of shared evolutionary history. This demonstrates how the story of evolutionary developmental biology, or ‘evo devo,’ also extends to what we call the ‘epigenetic level’ — or how genes are regulated.”

In the study, PhD student Haoze (Vincent) Yu, postdoctoral scholar Litao Tao, and their colleagues identified a shared mechanism involved in gene regulation or epigenetics, that enables stem cells and progenitor cells to differentiate into more specialized hair cells and Merkel cells.

In order to begin the process of differentiation, the right parts of a stem cell’s DNA need to be taken out of storage. Each human cell can store around six feet of DNA in its nucleus, because this DNA is wound around tiny “spools” made up of proteins called histones. These spools of DNA and histone protein are further packed together to form what are known are nucleosomes, which are stacked to create chromatin, which is the material that makes up the chromosomes.

When DNA is wound tightly into this storage configuration, the chromatin is closed and inaccessible to the protein ATOH1. This protein is a “master regulator” that can activate a network of differentiation genes in the DNA within the chromatin — but not without first gaining access.

To this end, ATOH1 stimulates the production of a second protein known as POU4F3, an aptly named “pioneer factor” with the ability to venture into new frontiers by binding to closed and inaccessible chromatin. After POU4F3 blazes a trail by binding to the closed chromatin, ATOH1 is able to move forward with engaging and activating the network of genes that drives differentiation into hair cells and Merkel cells.

Strikingly, there is significant overlap in the specific regions of chromatin that POU4F3 makes accessible to ATOH1 in hair cells and Merkel cells.

“It’s remarkable that these two cell types, which are both involved in sensing mechanical stimuli but derive from distinct parts of the embryo, both rely on the same ATOH1/POU4F3 mechanism in order to differentiate,” said Segil. “Our study suggests that this mechanism is extremely ancient, and emerged before hair cells and Merkel cells diverged from a common evolutionary ancestor — an ‘ur-mechanoreceptor’ cell type.”

CTP regulates membrane-binding activity of the nucleoid occlusion protein Noc

by Adam S.B. Jalal, Ngat T. Tran, Ling J. Wu, Karunakaran Ramakrishnan, Martin Rejzek, Giulia Gobbato, Clare E.M. Stevenson, David M. Lawson, Jeff Errington, Tung B.K. Le in Molecular Cell

To grow and multiply efficiently, bacteria must coordinate cell division with chromosome segregation. Crucial to this process in the bacterium Bacillus subtilis (commonly found in soil and the guts of humans and ruminants) is a protein called Nucleoid Occlusion Factor or Noc.

Noc binds to particular binding sites on the chromosome and then recruits further Noc proteins to grow a larger protein complex.

Part of the Noc protein can also bind to the cell membrane, pulling chromosomal DNA towards the membrane, allowing space for cellular division machinery to split the cell in two while keeping the DNA away and undamaged from this process.

Previously it had been unclear how the physical link between chromosomal DNA, Noc protein, and the cell membranes is established and regulated. To solve this mystery, researchers in the group of Dr Tung Le and in the scientific platforms at the John Innes Centre teamed up with Prof. Jeff Errington and Dr Ling Wu at the Newcastle University.

The team showed that a small and abundant molecule called Cytidine Triphosphate (CTP) is key to the functions of Noc. CTP binding enables Noc to “spread” on DNA to form a large protein complex. CTP also “switches on” the membrane-binding ability of Noc. Mutants of Noc that are defective in CTP binding can no longer pull DNA towards the cell membrane.

Noc binds liposomes in the presence of CTP and NBS DNA, and the phenotypic effects of the Noc variants

Researchers note that small molecule “switches” such as those dependent on Adenosine Triphosphate (ATP) or Guanosine Triphosphate (GTP) are ubiquitous in biology, but CTP switches, such as the one in Noc, are still rarely reported.

The research suggests that these previously rarely identified CTP switches may be far more widespread than previously thought — and might open a pathway towards the development of drugs to target bacterial chromosome segregation or cell division.

“Previous research has shown that new practical applications and innovations stem from fundamental discoveries. Understanding the mechanisms underpinning CTP binding and hydrolysis and how CTP switches evolve will open many new and unexpected avenues for research and application,” said Adam Jalal, the first author of this study.

Exploring the potential of engineering polygalacturonase‐inhibiting protein as an ecological, friendly, and nontoxic pest control agent

by Tiffany Chiu, Anita Behari, Justin W. Chartron, Alexander Putman, Yanran Li in Biotechnology and Bioengineering

About 70–80% of crop losses due to microbial diseases are caused by fungi. Fungicides are key weapons in agriculture’s arsenal, but they pose environmental risks. Over time, fungi also develop a resistance to fungicides, leading growers on an endless quest for new and improved ways to combat fungal diseases.

The latest development takes advantage of a natural plant defense against fungus. In a paper published in Biotechnology and Bioengineering, engineers and plant pathologists at UC Riverside describe a way to engineer a protein that blocks fungi from breaking down cell walls, as well as a way to produce this protein in quantity for external application as a natural fungicide. The work could lead to a new way of controlling plant disease that reduces reliance on conventional fungicides.

To gain entrance into plant tissues, fungi produce enzymes that use catalytic reactions to break down tough cell walls. Among these are polygalacturonases, or PGs, but plants are not helpless against this attack. Plants produce proteins called PG-inhibiting proteins, or PGIPs, that slow catalysis.

A group of UC Riverside researchers located the segment of DNA that tells the plant how to make PGIPs in common green beans. They inserted complete and partial segments into the genomes of baker’s yeast to make the yeast produce PGIPs. The team used yeast instead of plants because yeast has no PGIPs of its own to muddy the experiment and grows quicker than plants.

Verification of BD-PG expression levels. The expression levels of AnPG2, BcPG1, and FmPG3 were analyzed by western blot of S. cerevisiae containg BD-PG plasmids using anti-HA antibody. The expected size of BD-AnPG2, BcPG1, and FmPG3 are 59 kDA, 61 kDA, and 61 kDA, respectively. The control is Ost1-PvPGIP1-HA, which has an expected size at 45.7 kDa. M represents the protein marker (Fischer BioReagents EZ-Run Prestained Rec Protein Ladder, Fischer Scientific, #BP3603500). The cell lysate samples indicate the presence of the protein inside the cell and show a comparable expression level between the PGs.

After confirming the yeast was replicating with the new DNA, the researchers introduced it to cultures of Botrytis cinerea, a fungus that causes gray mold rot in peaches and other crops; and Aspergillus niger, which causes black mold on grapes and other fruits and vegetables.

Yeast that had both the complete and partial DNA segments that coded for PGIP production successfully retarded fungal growth. The result indicates the yeast was producing enough PGIPs to make the method a potential candidate for large-scale production.

“These results reaffirm the potential of using PGIPs as exogenous applied agents to inhibit fungal infection,” said Yanran Li, a Marlan and Rosemary Bourns College of Engineering assistant professor of chemical and environmental engineering, who worked on the project with plant pathologist Alexander Putman in the Department of Microbiology and Plant Pathology. “PGIPs only inhibit the infection process but are likely not fatal to any fungi. Therefore, the application of this natural plant protein-derived peptide to crops will likely have minimal impact on plant-microbe ecology.”

Li also said that PGIPs probably biodegrade into naturally occurring amino acids, meaning fewer potential effects for consumers and the environment when compared with synthetic small molecule fungicides.

“The generation of transgenic plants is time-consuming and the application of such transgenic crops in agricultural industry requires a long approval period. On the other hand, the engineered PGIPs that are derived from natural proteins are applicable as a fast-track product for FDA approval, if they can be utilized exogenously in a manner similar to a fungicide,” Li said.

Fungal spot assay testing the effects of natamycin (positive control), yeast harboring an empty vector (negative control), tPvPGIP2_5–8 secreting yeast, and full length PvPGIP2 secreting yeast on B. cinerea. B. cinerea was spotted onto potato agar plates containing bromophenol blue for enhanced contrast and treated with either natamycin (positive control), water (negative control), empty vector yeast (negative control), tPvPGIP2_5–8 secreting yeast, or PvPGIP2 secreting yeast. Growth was observed over the course of a week.

By tweaking the yeast a slightly different way, the researchers were able to make it exude PGIPs for external application. Previous studies have shown freeze drying naturally occurring microbes on apples, then reconstituting them in a solution and spraying them on crops, greatly reduces fungal disease and loss during shipping. The authors suggest that PGIP-expressing yeast could be used the same way. They caution, however, that because plants also form beneficial relationships with some fungi, future research needs to ensure plants only repel harmful fungi.

Li will continue to engineer PGIPs for enhanced efficiency and broader spectrum against various pathogenic fungi. Meanwhile, Li and Putman will keep evaluating the potential of using engineered PGIPs to suppress fungi-induced pre-harvest and post-harvest disease.

A variant in human AIOLOS impairs adaptive immunity by interfering with IKAROS

by Motoi Yamashita, Hye Sun Kuehn, Kazuki Okuyama, Satoshi Okada, Yuzaburo Inoue, Noriko Mitsuiki, Kohsuke Imai, Masatoshi Takagi, Hirokazu Kanegane, Masahiro Takeuchi, Naoki Shimojo, Miyuki Tsumura, Aditya K. Padhi, Kam Y. J. Zhang, Bertrand Boisson, Jean-Laurent Casanova, Osamu Ohara, Sergio D. Rosenzweig, Ichiro Taniuchi, Tomohiro Morio in Nature Immunology

Primary immunodeficiencies, such as severe combined immunodeficiency disease (SCID), occur when the immune system does not work properly, leading to increased susceptibility to various infections, autoimmunity, and cancers. Most of these are inherited and have an underlying genetic causes. A team at TMDU has identified a novel disorder resulting from a mutation in a protein called AIOLOS, which functions through a previously unknown pathogenic mechanism called heterodimeric interference.

The gene family known as IKAROS zinc finger proteins (IKZFs) is associated with the development of lymphocyte, a type of white blood cell involved in the immune response — meaning that mutations in this family can be involved in immune system deficiencies. Most research so far has focused on IKAROS protein, encoded by the gene IKZF1, although the underlying mechanism by which IKAROS mutations cause the deficiencies is not yet fully understood. A mutation in AIOLOS — another member of the IKZF family that is encoded by the gene IKZF3 — has now also been revealed to cause a hereditary immune deficiency. In addition to not functioning properly itself, the resultant mutant protein interferes with the functioning of IKAROS protein.

TMDU researchers uncovered this new mechanism while investigating the cause of a previously undescribed inherited B cell deficiency observed in a family of patients. After sequencing all of the protein-coding genes, the team focused their research on AIOLOS as IKAROS is known to be the cause of B cell deficiency. They showed that the mutant form of AIOLOS that was present in this family did not just fail to function, but actively bound to a different DNA sequence than the normal version of the protein.

Wild-type AIOLOS and AIOLOSG159R ChIP-seq in NALM-6 human pre-B cell line. a, Genomic sequence of the IKZF3 knock-out (KO) NALM-6 cell line. Exon 2 of IKZF3 was targeted by CRISPR-Cas9. Each allele of IKZF3 was cloned and sequenced. The knock-out clone had an indel in exon 2, resulting in a frameshift and premature termination of the protein. Grey shading indicates inserted nucleotides. Amino acid in red were changed by the frameshift. b, Western blotting of AIOLOS in wild-type (WT) and IKZF3-KO NALM-6 cell lines. Representative of three independent experiments. c, Triplicates of ChIP-seq tracks showing five representative loci with unique and common binding by AIOLOSWT and AIOLOSG159R in the IKZF3-KO NALM-6 cell line reconstituted with FLAG-tagged AIOLOSWT or AIOLOSG159R. Numbers represent the signal values of binding enrichment of the detected peaks. Structure of the genes are shown at the bottom. Locations of binding motifs (GGGAA and GGAGC) within the ChIP-seq track regions are indicated at the bottom. d, The top significant DNA binding motifs with p-values for AIOLOSWT and AIOLOSG159R abstracted from the peaks with all statistically different bindings and non-differential bindings between quadruplicate ChIP-seq samples. The AIOLOS consensus binding sequence (GGGAA) is delineated by the red square and TGGAA motif is delineated by the black square, whereas binding motifs specific to the AIOLOSG159R peaks (GGAGC, GGAGG, and GCAGG) are delineated by the blue square. GGGAA and TGGAA motifs were consistently associated with AIOLOSWT, whereas GGAGC, GGAGG, GCAGG, and CCCAGA motifs were repeatedly shown association with AIOLOSG159R. Peaks with non-differential binding between AIOLOSWT and AIOLOSG159R were enriched with relatively low accumulation of AIOLOS canonical binding motifs. e, EMSA showing binding of AIOLOSWT and AIOLOSG159R binding to AIOLOS consensus sequence (indicated in red font, IK-BS4 probe) or AIOLOSG159R specific motif (GGAGC, indicated in blue font) containing probe designed from genome regions with high AIOLOSG159R peaks. Direct binding of AIOLOSG159R to GGAGC motif was observed.

They went on to use a mouse model that harbors equivalent AIOLOS mutation identified in the patients to outline the underlying pathogenic mechanism. AIOLOS and IKAROS bind together to form a “heterodimer.” The mutant form of AIOLOS retained the ability to bind IKAROS but then interfered with the normal function of IKAROS, and led to the heterodimer being recruited to the incorrect regions of the genome.

“This is a novel pathogenic mechanism that we termed heterodimeric interference,” says lead author Motoi Yamashita, “where a mutant protein in a heterodimer hijacks the function of the normal partner protein.”

The team were then able to rescue some of the immune function in the mouse model by deleting the dimerization domain of the mutant AIOLOS.

T cell phenotypes in Ikzf3+/G158R and Ikzf3G158R/G158R mice. a, Flow cytometric analysis of thymocyte and lymph node T cells in Ikzf3+/+, Ikzf3+/G158R, and Ikzf3G158R/G158R mice. Expression of indicated surface markers in total thymocytes, lymphocyte gated lymph node cells and CD3ε+ lymph node cells are shown. Numbers indicate the percentage of cells in each gate or each quadrant. Mature T cells in lymph node of Ikzf3G158R/G158R mice showed decrease of CD8+ T cells and increase of CD4−CD8− T cells. Similar but milder phenotypes were observed in Ikzf3+/G158R mice. CD4+ T cells in lymph node of Ikzf3G158R/G158R mice showed skewing to CD44+ memory phenotype, which also recapitulated the patient’s phenotype. b, TCRβ, CD3ε, CD4, and CD8α expression levels in thymocyte and lymph node T cell subsets of Ikzf3+/+, Ikzf3+/G158R, and Ikzf3G158R/G158R mice. Numbers represent relative MFI against Ikzf3+/+ mice. Similar to the human patients, Ikzf3+/G158R and Ikzf3G158R/G158R mice showed decreased expression of TCRβ and CD3ε expressions in thymocytes and lymph node T cells, respectively. c, Emergence of CD4loCD8+ cells in thymus of Ikzf3G158R/G158R mice. CD4 expression in CD8α+ thymocytes (delineated by red line) is shown in the histogram. Numbers represent relative MFI against Ikzf3+/+ mice.

“The fact we could rescue the phenotype in our mouse model indicates a potential therapeutic approach,” says Tomohiro Morio, senior author. “The deletion of the domain responsible for binding IKAROS in the mutant AIOLOS protein could ameliorate the immunodeficiency observed in the patients.”

The discovery of this new pathogenic mechanism, heterodimeric interference, may well help to shed light on many other disease processes such as autoimmunity and cancer development where mutant proteins act in the same way.

A de novo regulation design shows an effectiveness in altering plant secondary metabolism

by Mingzhuo Li, Xianzhi He, Christophe La Hovary, Yue Zhu, Yilun Dong, Shibiao Liu, Hucheng Xing, Yajun Liu, Yucheng Jie, Dongming Ma, Seyit Yuzuak, De-Yu Xie in Journal of Advanced Research

North Carolina State University researchers have developed a new technique that can alter plant metabolism. Tested in tobacco plants, the technique showed that it could reduce harmful chemical compounds, including some that are carcinogenic. The findings could be used to improve the health benefits of crops.

“A number of techniques can be used to successfully reduce specific chemical compounds, or alkaloids, in plants such as tobacco, but research has shown that some of these techniques can increase other harmful chemical compounds while reducing the target compound,” said De-Yu Xie, professor of plant and microbial biology at NC State and the corresponding author of a paper describing the research. “Our technology reduced a number of harmful compounds — including the addictive nicotine, the carcinogenic N-nitrosonornicotine (NNN), and other tobacco-specific nitrosamines (TSNAs) — simultaneously without detrimental effects to the plant.”

The technique uses transcription factors and regulatory elements as molecular tools for new regulation designs. Regulatory elements are short, non-coding DNA fragments that control the transcription of nearby coding genes. Transcription factors are proteins that help turn certain genes on or off by binding to regulatory elements. Xie hypothesized that these could be useful molecular tools to design new regulations for engineering new plant traits. Two Arabidopsis transcription factors in particular, PAP1 and TT8, are known to regulate the biosynthesis of anthocyanins, or classes of nutraceutical compounds with antioxidant properties. Xie further hypothesized that these proteins could be used as molecular tools to help repress a number of harmful chemical compound levels, such as nicotine.

“PAP1 regulates pigmentation, so tobacco plants with our overexpressed PAP1 genes are red,” Xie said. “We screened plant DNAs and found that tobacco has PAP1- and TT8-favored regulatory elements near JAZ genes, which repress nicotine biosynthesis. We then proposed that these elements were appropriate tools for a test. In all, we found four JAZ genes activated in red tobacco plants with a designed PAP1 and TT8 cassette overexpressed.”

Phenotypes of P + T-NL1 and P + T-KY1 plants versus their corresponding wild-type tobacco plants in the field and of air-cured leaves. Field farming practice of four genotypes was performed in Oxford, North Carolina. A, phenotypes of 30-day and 60-day old WT-NL vs. red P + T-NL1 plants. B, phenotypes of 30-day and 60-day old WT-KY171 vs. red P + T-KY1 plants. C-D, phenotypes of topped plants © and leaves in air curing (D). Plant name abbreviations are WT-NL: wild-type Narrow Leaf Madole (NL) variety, P + T-NL1: Stacked PAP1 and TT8 transgenic NL line 1, WT-KY171: wild-type KY171 variety, and P + T-KY1: stacked PAP1 and TT8 transgenic KY line 1.

Xie and his colleagues tested the hypothesis by examining tobacco plants in the greenhouse and in the field and showed the reductions of harmful chemical compounds and nicotine in both types of experiments. NNN levels were reduced from 63 to 79% in leaves from tobacco plants that had PAP1 and TT8 overexpressed, for example. Overall, four carcinogenic TSNAs were significantly reduced by the technique.

Xie believes that the technique holds the potential to be used in other crop plants to promote other beneficial traits and make some foods healthier.

MISC

Subscribe to Paradigm!

Medium. Twitter. Telegram. Telegram Chat. Reddit. LinkedIn.

Main Sources

Research articles

Nature Genetics

GEN: Genetic Engineering & Biotechnology News

National Institutes of Health

National Library of Medicine

PLOS Genetics

Science

Science Direct

Science Daily

Longdom

--

--