New method now enables scientists to study gene expression in single cells (“RNA velocity”)

When scientists (typically) determine “liver gene transcription, mRNA or protein level, or enzyme activity” –– the entire organ is homogenized and analyzed. Same with lung, kidney, brain, mammary or prostate gland, etc. However, each of these organs comprises multiple cell-types, many of which might not be expressing the “gene under study.” To make things even more complicated, it is very likely (in each organ) the 100 million cells of the type being studied are not behaving identically or synchronously. Human gene expression is the ulti­mate example of a complex system, with more than 20,000 genes orchestrating the functions of human tissues. Until now, however, we have lacked the tools to measure how genes vary in expression within individual cells of one organ or tissue over time.

Authors [see attached article] describe an intriguing method that enables the level and rate of change of expression (“RNA velocity”) to be estimated simultaneously for each gene in a single cell. This approach has considerable implications for studying cellular dynamics –– especially during disease progression and in complex processes such as embryonic development. Biologists face operational problems when trying to understand dynamic changes in gene expression that occur as cells age, dif­ferentiate, or become diseased: [a] Techniques that enable researchers to broadly measure the expression of all genes in a given cell involve destroying the cell of inter­est (this prohibits analysis over time and thus provides only a snapshot of gene expression). [b] Techniques that enable long-term measurements of gene expression in living cells can be used to track only a limited number of genes.

Authors show that RNA velocity — the time derivative of the gene-expression state — can be directly estimated by distinguishing between unspliced and spliced messenger RNAs (mRNAs) in common single-cell RNA-sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a time-scale of hours. Authors [see attached] validate the accuracy of RNA velocity in the neural crest lineage, (during embryonic development). They demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. It is anticipated that RNA velocity will greatly aid the analysis of developmental lineages and cellular dynamics –– particularly in humans (e.g. progression of clinical diseases)..!!

DwN

Nature 23 Aug 2o18; 560: 434–435 [editorial]

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Bacterial death and population dynamics affect mutation-rate estimates and evolvability — in response to ENVIRONMENTAL STRESS

The topic today is directly central to “gene-environment interactions.” Three decades ago, experiments in the lab of John Cairns showed that the effect of “environmental stress” on the mutation rate in bacteria can be remarkably strong (the “stress” they used was “to withhold an important nutrient). Those findings are relevant to evolution: as a species evolves in any niche on the planet, it will “sense” environmental signals (both beneficial and adverse conditions), and a mechanism must be in place to alter the DNA sequence (i.e. mutations) in relevant genes –– in order for that species to survive (i.e. find food, avoid predators, and reproduce). These gene-environment interactions can be studied in malignant tumors, as well as in bacteria, and the basic mechanisms are more or less the same similar.

More recent bacterial experiments are consistent the older findings. For example, there is plenty of evidence that various antibiotics increase mutation rates –– when the antibiotic is used at sub-inhibitory concentrations. It is therefore suggested that such treatments promote “resistance evolution,” because they enhance the process of genetic variation on which natural selection can act. However, existing methods to calculate the mutation rate fail to consider the effect of these environmental stress signals on “rate of death” or on population dynamics.

Developing new experimental and computational tools, authors [see attached publication] find that taking death into account significantly lowers the signal for stress-induced mutagenesis. Furthermore, authors show treatments that increase mutation rate do not always lead to increased genetic diversity –– which questions the standard paradigm of increased evolvability under stress. Most of the controversy surrounding Cairns’ original experiments initially came from the question of whether these mutations were Lamarckian [Lamarck (1744-1829) proposed that an organism can pass on, to its offspring, characteristics –– that it has acquired through use or disuse during its lifetime. The oft-used example is “the neck of giraffes became longer because they needed to eat leaves higher in trees during times of drought), i.e. do these mutations arise at higher rate when cells “sense” that such mutations would be beneficial? However, many additional experiments quickly suggested that this phenomenon can be explained by more standard Darwinian mechanisms (i.e. genetic changes are not targeted, but rather occur randomly, and then are either selected for, or rejected).

Authors state convincingly that “measuring mutation rates under stress is problematic, because existing methods do not take into account death.” Authors (correctly) insist that sub-inhibitory stress levels can induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size –– thus providing more opportunities to acquire further mutations. They show that ignoring death leads to a systematic over-estimation of mutation rates under stress. Using a system based on plasmid segregation that allows one to measure death and division rates simultaneously in bacterial populations, authors showed that a substantial death rate occurs at the tested sub-inhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account –– lowers (in fact, sometimes removes) the signal for stress-induced mutagenesis. In summary: [a] population dynamics and, in particular, the numbers of cell divisions, are crucial but neglected parameters in the evolvability of a population; [b] providing experimental and computational tools and methods to study evolvability under stress, authors propose that the magnitude and significance of the stress-induced mutagenesis paradigm needs to be reassessed.

DwN

PLoS Biol May 2o18; 16: e2005056

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Only certain histone posttranslatinal modifications appear to qualify as “having an epigenetic effect” ???

As often covered in these GEITP pages, multifactorial traits (e.g. phenotypes such as type-2 diabetes, drug efficacy, many dose-independent adverse drug reactions, autism spectrum disorder, cancer) represent the contributions of: [a] genetic predisposition (genotype; DNA-sequence changes); [b] epigenetic effects; [c] environmental factors (e.g. cigarette smoking, drug-drug interactions); [d] endogenous influences (e.g. cardiopulmonary or kidney disease); and [e] interactions of our microbiome (gut bacteria participate in the brain-gut-microbiome axis). As often mentioned in these GEITP pages, epigenetic events include DNA-methylation, RNA-interference, histone modifications, and chromatin remodeling.

Epigenetic effects are heritable alterations in gene expression that occur without DNA-sequence changes. Post-translational modifications (PTMs) of histones that package DNA into chromatin represent epigenetic effects. These PTMs occur on histones, and are mediated by enzymes in response to an “Initiator” –– ultimately altering chromatin structure (i.e. remodeling) and, consequently, gene expression. In multicellular organisms (e.g. sponges, mice, humans), cellular identity is established by Master Regulators (Initiators) that can activate or repress transcription by means of their sequence-specific DNA-binding activities. Heritable and accurate transmission of distinct gene expression profiles during cell division is essential for preserving the properties of cell lineages. Thus, a key feature of the epigenetic process is that, after the Initiator-effect goes away, these (highly informative) PTMs involved in chromatin-remodeling must be inherited (i.e. passed on to subsequent cell generations).

Numerous histone PTMs are known to occur –– but can they all convey epigenetic information? Authors [see attached 2-page article] describe the few repressive histone PTMs that qualify as epigenetic; authors also define the distinct features of the enzymes that participate in chromatin-remodeling, and therefore qualify as those involved in epigenetic effects. Why should repressive histone PTMs –– but not activating PTMs –– be epigenetically inherited? Authors propose that restraining improper activation of genes might be an evolutionary requirement of all multicellular organisms. Positive feedback loops for gene activation could carry excessive risks, because they might result in converting variable stimuli into permanent mistakes during cell-fate decisions, which would have potentially devastating consequences for the organism. In other words, when in doubt, “genomes should keep their transcriptome under rigorous control at all times.” 🙂

DwN

Science 6 Jul 2o18; 361: 33–34

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Dual-spindle formation keeps the two parental genomes apart in 1-cell mammalian embryo

Mammalian life begins with fertilization of the egg. Once the egg and sperm have fused, the parental chromosomes need to be combined. It was previously taught that a single microtubule-spindle is responsible for spatially combining the two genomes, and then segregating them, to create the two-cell embryo. Authors [see attached article] discovered that it is not that simple. Unexpectedly, the parental chromosomes do not mix immediately, but rather they occupy distinct zones in the zygote (fertilized egg) throughout the first cellular division. How the autonomy of parental genomes is retained after fertilization –– remains unclear.

Authors used elegant microscopy methods to illuminate this special moment, when the parental chromosomes first meet in live mouse zygotes, and they then followed how the chromosomes become distributed as the zygote divides to become a two-cell embryo. Unexpectedly, the male and female chromosomes each assemble their own chromosome-separation machineries. From an evolutionary survival point-of-view, this would increases the probability that chromosomes are separated into multiple, unequal groups –– which might lead to enhanced diversity in the offspring. Alternatively, this chain-of-events may compromise embryo development and give rise to a spontaneous miscarriage.

Authors labeled the maternal and paternal chromosomes in different colors (by taking advantage of distinct DNA sequences in the parental chromosomes, which came from different mouse strains). Authors then imaged the area of interest at very high spatial and temporal resolution, plus they used an innovative light-sheet microscope –– which illuminates the embryo selectively in the region of interest but not in adjacent regions (as is the case with standard microscopy approaches). This method is fast and allows authors to reconstruct the entire volume occupied by the chromosomes with unprecedented spatial and temporal resolution. In addition, they imaged microtubules, the proteinaceous fibers that form the spindle-apparatus which captures, aligns, and distributes the chromosomes equally between the two daughter-cells of the dividing zygote.

These two spindles aligned their poles before Anaphase (stage of mitosis after Metaphase –– when replicated chromosomes are split and the daughter chromatids are moved to opposite poles of the cell. Chromosomes also reach their overall maximum condensation in late Anaphase, to help chromosome segregation and reformation of the nucleus) –– but kept the parental genomes apart during the first cleavage. This spindle-assembly mechanism provides a potential rationale for erroneous divisions into more than two blastomeric (cells formed by cleavage of the fertilized ovum) nuclei observed in mammalian zygotes. These data reveal the mechanism behind the observation that parental genomes occupy separate nuclear compartments in the two-cell embryo

DwN

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SLE in in leptin transgenic pigs ???

For anyone interested in SLE (systemic lupus erythematosus), this is a weird finding, maybe a paper worth investigating…..

Symptoms of systemic lupus erythematosus are diagnosed in leptin transgenic pigs

Junchen Chen, Weiqi Zeng, Weirong Pan, Cong Peng, Jianglin Zhang, Juan Su, Weihu Long, Heng Zhao, Xiaoxia Zuo, Xiaoyun Xie, Jing Wu, Ling Nie, Hong-ye Zhao, Hong-jiang Wei, Xiang Chen

Overexpression of leptin in transgenic pigs resulted in a phenotype bearing a remarkable resemblance to human systemic lupus erythromatosus (SLE), sharing multiple aspects of the disease.

COMMENTS:
Walt ––––– This is an excellent criticism. And it touches on a topic near-and-dear to my heart –– from years ago when my lab was involved in creating transgenic mouse lines. When a gene (derived from mouse, human, or other species) is ligated (i.e. cut from a circular plasmid; opened up; linearized) and then “inserted randomly” into the recipient mouse’s genome, the gene under investigation often forms tandem (head-to-tail) structures (concatamers) –– which can represent two, or five, or ten identical genes.

On the other side, the “insertion point” in the recipient genome depends on a lot of factors (including nearby homologous sequences that might attract the incoming cloned gene construct). Inserted foreign DNA –– into or near a protein-coding gene or a conserved regulatory region –– sometimes will affect the expression of these nearby genes. This is called “the neighborhood effect” and, for any study to be complete, this possibility should be ruled out. Or demonstrated to be the cause for the phenotype (trait), which in this case is “symptoms of systemic lupus erythematosus.” 🙂

DwN

COMMENTS: From my quick look at this, I don’t see that they identified where their construct became integrated into the pig genome. So is this an effect of over-producing leptin? Or is this an artifact of untargeted disruption of some other pig gene?

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The evolution of genome size in maize

The full-length article about “genome size” [attached left pdf file] was shared with all of GEITP on Aug 31st, and that analysis is re-posted furthest below. The editorial commentary [attached right pdf file] was intended to accompany the full-length article. However, re-approaching this intriguing topic a second time is (in my humble opinion) worthwhile. It is well known that GENOME SIZE varies by many orders of magnitude across plants, fungus and animals –– but resolving the most important evolutionary forces driving this variation –– remains an active debate. As mentioned before, the human genome typically comprises coding DNA (~1.0-1.5%; i.e. “the exome”), noncoding (‘selfish’) repetitive sequences (~45%), and conserved noncoding sequences (~53-54%) that likely contain important heritable information that we do not yet understand.

We know that EUKARYOTIC (organisms carrying pairs of chromosomes) GENOME SIZE variation is not simply associated with complexity of the organism. Thus, genetic drift (process of change in the frequency of an allele (gene variant) in a population over time, caused by chance, or random events, rather than by natural selection) of the amount of noncoding DNA is one possible reason to explain genome size –– implicating population and species history as key drivers of shifts in DNA content. Alternatively, directional selection (i.e. the opposite of genetic drift) could be acting on DNA content. Because the predominant component of many eukaryotic genomes comprises selfish genetic elements such as transposable elements (TEs) and regions subject to meiotic drive (preferential production of certain gametes (egg, sperm) that alters segregation of genes from the Mendelian prediction) –– factors that influence their differential success across populations and species could account for much of the variation in genome size.

Authors [see attached article] capitalize on the remarkable genome size variation in maize and its wild relatives, which differs by 40% to 70% within and between subspecies. It is estimated that ~85% of the maize genome is composed of TEs, B chromosomes, and heterochromatic knobs subject to meiotic drive –– highlighting the success of selfish genetic elements in this lineage (compare ~85% in maize with ~45% for the human genome). Nevertheless, clines of genome size along with phenotypic and environmental variables in Zea mays spp. have often been described, with a number of studies showing evidence of genome reductions, including the loss of knobs and B chromosomes, in regions where the maize is grown at higher altitudes and latitudes.

Authors use whole-genome-sequence data from three altitudinal clines (populations that have adapted to three varying altitudes) to obtain detailed information, not only about the kinship and structure of their samples, but also about the contribution of each type of repeat to overall genome size. Using this approach to test for local adaptation, authors are able to reject the hypothesis of the neutral model –– concluding that genome size differences among the altitudinal clines are too extreme to be explained solely by genetic drift. Authors instead found a strong correlation of TE copy-number with genome size and altitude. These data strongly support the likelihood that environmental adaptation (i.e. growth at higher altitudes and higher latitudes) could be an important determinant of transposable element abundance, mediated through its effects on genome size.

DwN

PLoS Genet May 2o18; 14: e1007249.

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The prehistoric peopling of Southeast Asia

Anatomically modern humans are known to have expanded into Southeast Asia at least 65,000 years ago –– leading to formation of the Hòabìnhian hunter-gatherer tradition. Although Hòabìnhian foragers are considered to be the ancestors of present-day hunter-gatherers from mainland Southeast Asia, the East Asian phenotypic affinities of the majority of present-day Southeast Asian populations suggest that diversity was influenced by later migrations involving rice farmers and millet farmers from the north. These observations have generated two competing hypotheses: [a] The Hòabìnhian hunter-gatherers adopted agriculture without substantial external gene flow; and [b] (the “two-layer” hypothesis) states that farmers from East Asia replaced the indigenous Hòabìnhian inhabitants ~4,000 years ago.

Studies of present-day populations have not resolved the extent to which migrations from East Asia affected the genetic makeup of Southeast Asia. From a Language point-of-view, the movement of Neolithic populations from southern China commenced as two separate migrations. One, ancestral to modern speakers of Tai and perhaps also Austroasiatic languages (the latter including Khmer and Vietnamese), spread by land into mainland Southeast Asia. Along with it spread rice, millets, and domesticated pigs and dogs. The other, ancestral to speakers of Austronesian languages such as Malay and Hawaiian, spread by sea into Taiwan, later the Philippines, and onward into Indonesia and Oceania, again carrying a cultural component of food production.

There is evidence indicating that the Hòabìnhian and Neolithic populations had already undergone some genomic mixing before Neolithic migrants expanded out of southern China. Moreover, in an archaeological site in northern Vietnam, skeletons of both populations are found buried together at ~1800 B.C. The Asian Neolithic population, however, appears to have been most prevalent among those skeletons. Two other important observations can be made from these studies. Mainland Southeast Asian populations such as Tais and Vietnamese Kinh received, perhaps unsurprisingly, another layer of quite heavy gene flow from China starting ~2,500 years ago. This was when the Warring States and subsequent Qin and Western Han (206 B.C. to 220 A.D.) Chinese empires conquered southern China and northern Vietnam, imposing Sinitic (Han) settlement, languages, and literacy on many of the indigenous Bronze and Iron Age societies to the south.

By sequencing 26 ancient human genomes, authors [see attached article & editorial] show that neither interpretation fits the complexity of Southeast Asian history: Both Hòabìnhian hunter-gatherers and East Asian farmers contributed to current Southeast Asian diversity, with further migrations affecting island Southeast Asia and Vietnam. These results help resolve one of the long-standing controversies in Southeast Asian prehistory. As is seen the world over, genetic admixtures between populations frequently occur. And they are influenced by agriculture and warring societies.

DwN

Science 6 Jul 2o18; 361: 88–92 & pp 31–32 [Editorial]

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A protestor’s change-of-heart sheds light on the public’s reservations about genetically modified organisms (GMOs)

This [attached] article is a 1-page summary of a new book review (by José R. Dinneny, Dept of Biology at Stanford Univ). In Seeds of Science, author Mark Lynas has written this book in hopes of improving the “contentious debate that has tempered the enthusiasm of many governments and food producers.” For whatever reason, the hype and hysteria of genetically modified organisms (GMOs) in Europe seems to be considerably more intense than the hype and hysteria in North America or Australia.

In this book, author Lynas gives readers a first-hand look at both sides of the discourse. Lynas –– formerly a dyed-in-the-wool anti-GMO activist for Greenpeace –– is now an advocate for the safe use of GM technology. The book begins with exciting accounts of the law-breaking activities that Lynas engaged in, as one of the pioneers of the anti-GM) movement. He describes destroying corn plants in a research field, running from the police, and tearing up documents in the Monsanto headquarters. However, Lynas then comes face-to-face with evidence that contradicts what he thought he knew about GM technology. By the end of chapter 7, science has won the debate.

Most major global scientific organizations have firmly stated that science backs the efficacy and safety of genetic engineering. Yet, in the minds of many uninformed or misinformed non-scientists, “consuming food with a GMO–free label is a must.” Lynas argues that applying GM technology first to herbicide-resistant crops was a mistake that aligned the chemical manufacturing industry — which has always been regarded by many activists as “evil” — with the burgeoning technology. If pest-resistant crops that allowed farmers to apply fewer chemical pesticides had been introduced first, the narrative might have been different.

What actually bothers many people about genetically engineered crops is that, to produce a GMO, genes and genomes are treated like research agents and tools for scientists and engineers, at large multinational companies, to manipulate at will. Many feel that “there is something sacred about Nature” and that it should be preserved, as much as possible, in an untouched state. What is NOT mentioned in this book review is that Mother Nature has been “genetically modifying” plants for hundreds of millions of years: “Horizontal gene transfer” is a well known mechanism by which a plant’s genome can be “perturbed” by viral or fungal or other plant genes/genomes, leading to incorporation of these “foreign genes and DNA fragments” into plant genome. Thus, typically, each plant that animals eat –– contains at least several hundred “foreign genes.” 🙁

DwN

Science 29 Jun 2o18; 360: 1407

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The transition (during evolution) — from single cells to multicellular life

From time to time, these GEITP pages examine EVOLUTION –– especially from the standpoint of “how do an organism’s genes respond to the adverse environment at that moment in time, and mutations/insertions/deletions/inversions/duplications (i.e. DNA-sequence alterations) occur, as well as epigenetic changes (i.e. no DNA-sequence alterations) occur, in order to survive and perhaps diversify into a “new, improved organism” with even greater likelihood to survive?”

In order for everyone to “start on the same page,” let’s give a brief history of Earth and life on Earth. The planet formed ~4.54 billion years ago (BYA), and earliest forms of life (single cells) are believed to have originated between ~4.2 BYA and ~3.5 BYA (give or take a few months). The oldest fossils of single-celled organisms identified to date lived ~3.5 BYA. Fossilized microbes in rocks of Western Australia are claimed to have been alive ~3.4 BYA. Two main groups of (single-celled) life –– bacteria and archaea –– had diverged from one another, some time between ~4.0 BYA and ~3.0 BYA; viruses probably predated the time of this divergence.

For reasons still debated, between ~2.4 BYA and ~2.1 BYA atmospheric O2 increased dramatically on Earth. Around ~2.0 BYA eukaryotic cells (containing a nucleus plus organelles) first emerged, and then diversified ~1.5 BYA into the three major lineages: Plant (cellulose-containing cell wall, nucleus, chloroplast organelles with chlorophyll, participating in photosynthesis, i.e. CO2 uptake and O2 output) Fungus (chitin-containing cell wall, nucleus, no photosynthesis, but taking up nutrients via absorption), and Animal (cell membranes, nucleus, organelles including mitochondria, which create energy for the cell by O2 uptake and CO2 excretion). Earliest multicellular plants (e.g. slime molds) and animals (e.g. sponges) originated ~0.8 BYA (i.e. ~800 million years ago).

The transition from unicellular life to multicellular life –– was believed to be a momentous step forward in evolution. Single cells had a modest existence: simply feed yourself. In contrast, cells in a multicellular organism, from the four cells in some algae to the 37 trillion cells in a human, give up their independence to stick together and take on specialized functions (e.g. finding food, reproducing, and surviving predators).

Recently we have begun to understand more about this “transition from unicellular life to multicellular life” [see attached 4-page editorial]. Genomic comparisons between single-celled and multicellular organisms have shown that most of the genes were likely to have been in place well before multicellularity evolved. Experiments have now shown that single-celled life can evolve into the beginnings of multicellularity –– in just a few hundred generations — which, in terms of evolution, is less than a nanosecond. Plants and animals each made the leap to multicellularity just once. However, in fungi, the transition took place repeatedly.

All told, by surveying active protein-coding genes in 21 choanoflagellate species, authors discovered that these “simple” organisms have ~350 gene families once thought to have been exclusive to multicellular organisms. The ancestral versions of those genes, in many cases, do not perform the same functions, as compared with functions in multicellular organisms. For example, choanoflagellates have genes encoding proteins that are crucial to neurons, yet their cells do not at all resemble nerve cells. Likewise, their flagellum (tail) has a protein that, in vertebrates, helps create the body’s left-right asymmetry. What this “neuronal” protein does in the choanoflagellate is not yet known. In conclusion, these studies have shown that many functions of specialized cells in a complex organism are not new, but rather is derived from the same gene, found in single-celled organisms, that has taken on a new function. The term for this is convergent evolution.

DwN

Science 29 Jun 2o18; 360: 1388–1391 [editorial]

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Induction of innate immune memory: — microRNA targets chromatin-remodeling factors !!

Prolonged exposure of mouse or human immune cells to unwanted microbial products –– such as lipopolysaccharide (LPS) –– can induce a form of innate immune memory that suppresses subsequent responses to unrelated pathogens. This is called “LPS tolerance”. Sepsis (presence of harmful bacteria and their toxins in tissues of an animal, typically through infection of a wound) is a dysregulated systemic immune response to disseminated infection that has a high mortality rate. In some patients, sepsis leads to a period of immunosuppression, known as “immunoparalysis,” which is characterized by diminished inflammatory cytokine output, increased secondary infection, and increased risk of organ failure and mortality.

Lipopolysaccharide (LPS) tolerance recapitulates several key features of sepsis-associated immunosuppression. LPS tolerance is an immunosuppressive form of innate immune memory that can be modeled in cultured cells by prolonged LPS treatment of bone-marrow-derived macrophages. As a result of this functional reprogramming, most LPS-induced genes are transcriptionally silenced (i.e. ‘tolerized’) and are not expressed during re-stimulation. Using this cell culture model, authors [see attached] identified microRNAs (miRNAs) with expression patterns that correlated with “tolerance”.

As these GEITP pages have often described, DNA-sequence changes reflect the inherited genotype. Heritable changes independent of DNA sequence represent epigenetic factors –– which include DNA methylation, RNA interference (RNAi), histone modifications, and chromatin remodeling. RNAi includes regulation of genes by miRNAs. Although various epigenetic changes have previously been observed in “tolerized” macrophages, the molecular basis of tolerance, immunoparalysis, and other forms of innate immune memory has remained unclear. Herein authors [see attached file] screened for tolerance-associated miRNAs and identified two specific miRNAs (miR-221 & miR-222) as regulators of functional reprogramming of macrophage genes during LPS tolerization.

Prolonged LPS stimulation in mice leads to increased expression of miR-221 and mir-222 –– both of which regulate the Smarca4 gene (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4). This increased expression causes transcriptional silencing of a subset of inflammatory genes that depend on chromatin-remodeling mediated by SWI/SNF (switch/sucrose non-fermentable) and STAT (signal transducer and activator of transcription). This, in turn, promotes LPS tolerance. In patients with sepsis, increased miR-221 and miR-222 expression is correlated with immunoparalysis and increased organ damage. This study shows that specific microRNAs can regulate macrophage tolerization, and this may serve as biomarkers of immunoparalysis and poor prognosis in patients who have developed sepsis.

In this story, therefore, “LPS treatment” or “sepsis” is the environmental signal, whereas genes identified as responding to this signal include the down-regulation (silencing) of a subset of inflammatory genes plus induction of two miRNAs, acting as epigenetic effects.

DwN

Nature 5 Jul 2o18; 559: 114–119

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