Sunday, February 21, 2016

Skinny Genes: Human Warming and the Reverse Butterfly Effect

Obesity in humans is associated with a region around FTO, a "fat mass and obesity-associated” gene on chromosome 16. Although this is the strongest genetic association, it accounts for only a 1-2% difference in Body Mass Index (BMI) and the mechanism was unknown. Here, the investigators looked at regulator proteins binding to variants DNA sequences within the FTO region, particularly those binding to sites with single nucleotide polymorphisms (SNPs), in 100 healthy Europeans -- 52 subjects were homozygous for 3 risk-variant SNPs (both alleles, all 3 loci) and the remaining 48 were homozygous for the non-risk variants.

They found that the change of a T-to-C at one SNP within a risk allele of FTO prevented the binding of the repressor protein ARID5B. For want of this binding site, the repressor is lost. For want of this repressor, the expression of 2 linked genes doubles: IRX3, located about half a million base pairs (~0.5 Mbp) away, and IRX5, ~1 Mbp away. IRX3 repression made mice thinner by “increased energy dissipation without a change in physical activity or appetite”, i.e. not changing eating or exercise but rather elevating ‘metabolism’. The doubled expression of IRX3 led to a 5-fold reduction in mitochondrial thermogenesis and a 7-fold difference in brown/white adipose tissue development.  (Brown fat is brown because it holds more mitochondria, little furnaces that burn fat and produce heat.)

The risk alleles are causative, and mediate through IRX3 and IRX5, because mimicking the repression of IRX3 or IRX5 in 8 carriers of the risk alleles, but not 10 carriers of the non-risk allele increased stimulated metabolism (Fig 3D, shown, left panel). And overexpression of IRX3 or IRX5 reduced stimulated metabolism in non-risk allele carriers (because their endogenous genes are repressed) but not in risk allele carriers (Fig 3D, right panel).

Figure 3D. Oxygen consumption rate (OCR), basal and stimulated, in cells with risk or non-risk alleles.

The take-home messages are that the causative SNP can be kilobases away from the genes that mediate the effect (not a huge surprise) and that small risks might develop from relatively big differences in particular developmental and cell biological pathways (reverse butterfly effect: not small-change-to-big-effect but big effect leads to small change (1-2% BMI)).

N Engl J Med. 2015 Sep 3;373(10):895-907. “FTO Obesity Variant Circuitry and Adipocyte Browning in Humans.” Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W, Haugen C, Glunk V, Sousa IS, Beaudry JL, Puviindran V, Abdennur NA, Liu J, Svensson PA, Hsu YH, Drucker DJ, Mellgren G, Hui CC, Hauner H, Kellis M

Sunday, November 22, 2015

Normalizing T lymphocyte metabolism treats lupus autoimmunity

Glucose is metabolized in two pathways to fuel cellular functions: glycolysis, which splits glucose, yielding little energy but providing pyruvate and other materials for synthesis, and oxidative phosphorylation, which degrades glucose in the mitochondria and produces ~15-fold more energy. Glucose uptake is a limiting in activated T lymphocytes through CD28 costimulation.  Glucose metabolism is dysregulated in T lymphocytes of patients with the autoimmune disease Systemic Lupus Erythematosus (SLE, lupus, review).

These investigators blocked glycolysis with 2-deoxy-D-glucose (2DG) and oxidative phosphorylation with metformin (Met), and observed that disease was reduced and even reversed in mice “triple congenic” (TC) with three lupus-predisposing genetic regions: Sle1-Sle2-Sle3 (review).  2DG is glucose with its 2-hydroxl group replaced by a hydrogen, thereby blocking glycolysis.  Met is a small molecule that was discovered in 1920s to reduce blood glucose, probably by interfering with mitochondrial respiration.  The authors show here that Met reduces extracellular acidification rate (ECAR) and 2DG reduces oxygen consumption rate (OCR), both measures of glucose metabolism, in activated T cells (fig. 1).  

Anti-nuclear antibodies (ANA), a hallmark of lupus, are particularly dangerous because they damage glomeruli, the kidney’s filtration units, causing glomerular nephritis (GN).  The authors show a remarkable reduction in ANA and spleen size (fig. 4, panels, C, D and a portion of panel E shown here) as well as improvement in kidney pathology (fig. 4 panel I)
Although these metabolism inhibitors are not targeted to pathogenic T cells, there are no obvious adverse consequences for the animal or even the immune system.  Treated mice raise antibody responses following protein immunization, generating normal levels and avidities of circulating antibodies (supplemental).  Perhaps the limiting effect of glucose uptake by pathogenic, chronically activated T cells make them more sensitive to inhibition.  How treatment influences control of chronic infections (e.g., EBV, CMV) is also worth knowing.  There was no change in body weight on Met. 
Testing 2 other mouse models of lupus (NZB/W and chronic graft-vs-host (cGVH)), they found a mixture of responses.  For example, in cGVH, combined treatment doesn’t reduce spleens (though Met alone does), while treatment of NZB/W mice reduces ANA but doesn’t improve GN.  Human patients exhibit a range of symptoms and might also be expected to show a range of responses.  This is inspired and inspiring work that cuts across as many disciplines as it does organ systems and raises as many questions as hopes. 
Y. Yin, S.-C. Choi, Z. Xu, D. J. Perry, H. Seay, B. P. Croker, E. S. Sobel, T. M. Brusko, L. Morel, Normalization of CD4+ T cell metabolism reverses lupus. Sci. Transl. Med. 7, 274ra18 (2015).

Sunday, March 15, 2015

Stem Cells: Unstable in Culture

Stem Cells (SC) can differentiate into many different cell types, offering the potential of replacing failing cells, tissues, or even entire organs with new ones generated from the patient’s own or related donor SC (National Academy of Sciences Workshop summary).  To be useful for therapy in the clinic, it would be necessary to grow and expand SCs in culture.

The authors explored the proliferation of human embryonic SC (HESC), which are prepared from disrupted embryos, and the less-controversial human inducible pluripotent stem cells (hiPSCs), which can be prepared from several adult tissues, including blood, skin, and fat.  They obtained 1 HESC line, WA09, from the WiCell Research Institute and they generated 3 hiPSC lines from fetal dermal fibroblasts by over-expressing the ‘standard reprogramming factors’ (pluripotency-conferring genes, transduced OCT4/POU5F1, SOX2, KLF4, and MYC).

They compared four standard SC culture conditions: with or without a feeder cell layer and enzymatic or “mechanical” (dissection) disruption, with 6 replicate cultures per condition, for over 100 “passages” (transfers to fresh cultures). Previous studies cited here revealed genomic changes (small duplications) that are not detectable by karyotyping, particularly on chromosome 12, where the pluripotency-related gene NANOG is encoded, and chromosome 20, where the survival gene Bcl-xL is encoded.  In addition to measuring proliferation, telomere length, pluripotency by teratoma formation, they also analyzed over a million reference SNPs around the genome and used those SNPs to assess copy number variation (CNV). 

Not surprisingly, genomic changes increased with time in culture, both in aberration number (A) and total length (B) (Figure 2, shown, WA09 HESC: left duplications and right deletions).  The number of aberrations was lowest in “EcmMech” condition, i.e. cultures without feeder cells (only extracellular matrix, ECM), and disrupted mechanically (blue line).  The number and length of aberrations was worst with MefEnz (green line), cultured with feeder cells (mouse embryo fibroblasts, Mef) and disrupted enzymatically.  They conclude that there is a “need for careful assessment of the effects of culture conditions on cells intended for clinical therapies”.  

Increased Risk of Genetic and Epigenetic Instability in Human Embryonic Stem Cells Associated with Specific Culture Conditions  Garitaonandia et al. PLoS One 10(2), February 25, 2015

Saturday, January 24, 2015

Nurture Immunity: Immune system influenced more by environment than by genes

Differences in immune protection presumably explain why some people exposed to infection resist disease or recover while others succumb.  These authors sought to distinguish the influences of genes and environment on immunity. They compared the cellular and molecular components of the immune system among 210 twins: 78 monozygotic (MZ, “identical”) and 27 dizygotic (DZ, fraternal) pairs.  They measured 43 serum proteins and 72 immune cell populations repeatedly and longitudinally (over time) to assess actual variations and account for technical variations.  MZ twins, who have practically identical genomes, and DZ twins, who share half their genes, are especially valuable for assessing the relative contributions of “nature or nurture” (genes or environment) to phenotype.  Their analysis allowed them to detect as little as 20% heritability.

The levels of few proteins and cell populations are under strong genetic control, such as interleukin-6 and CD4+ “central memory” T cells, but most are only weakly heritable or not at all (Fig 1). They found that a common, chronic infection, by cytomegalovirus (CMV), influences the levels of most (58%) cell populations and proteins (Fig 5).  Variation between twins increased as they age, probably reflecting different environmental stimuli and epigenetic changes (Fig 4).  Most intriguing, they correlate the heritability of response to vaccines to the age of immunization, whereby early childhood vaccines are highly heritable while vaccines after early adolescence have no detectable heritability (Table 1, shown below).  

Brodin et al. Cell. 2015 Jan 15;160(1-2):37-47. Variation inthe human immune system is largely driven by non-heritable influences. 

Sunday, December 8, 2013

Levels of a common, chronic virus (TTV) reflects the immune competence of transplant recipients

Successful organ transplantation requires careful immune suppression: enough to block the rejection of transplant while permitting host defense against infectious microbes.   Viruses that are not cleared by our immune systems, are common in healthy people, and can complicate transplantation include cytomegalovirus (CMV) and Torque teno virus (TTV), which was first described in 1997 [review].  TTV is a small (3.8 kb), single-stranded, transfusion transmitted DNA virus, representative of a highly diverse family of anelloviruses 

The authors examined the influence of immune-suppressive drugs (e.g., tacrolimus, mycophenolate mofitil, cyclosporine) and the anti-CMV drug valgancyclovir on chronic, endogenous microbes.  From 96 heart or lung transplant recipients they collected 656 blood samples over time, some up to a year post-transplant, removed the cells, and identified remaining DNA by sequencing.  They found that 0.12% matched viral or bacterial or fungal sequences.  They validated some ‘hits’ with quantitative PCR.  Control preparations using water or bacteriophage demonstrated no relevant artifacts or contamination.  

They found that treatment with valgancyclovir reduced herpesviruses, including CMV, but dramatically increased the relative and absolute levels of anelloviruses, including TTVs (fig. 2, 3, 4).   Moreover, those patients who did not reject their transplants tended to have a greater increase in anelloviruses (Fig. 5A, shown; rejecting patients plotted in red).  The authors conclude that anellovirus levels might be used to monitor immune competence.   Focosi et al. made a related observation following autologous stem cell transplantation

 Cell. 2013 Nov 21;155(5):1178-87. Temporal response of the human virome to immunosuppression and antiviral therapy.  De Vlaminck I, Khush KK, Strehl C, Kohli B, Luikart H, Neff NF, Okamoto J, Snyder TM, Cornfield DN, Nicolls MR, Weill D, BernsteinD, Valantine HA, Quake SR. 

Sunday, October 27, 2013

Salt develops a taste for Th17 lymphocytes

Helper T lymphocytes that make the hormone interleukin-17 (IL-17), called Th17 cells, contribute to inflammation and autoimmune diseases (review). The development of Th17 cells was known to require IL-23 but it was not known exactly why.  To gain some perspective, the authors measured gene transcripts found in Th17 cells as they develop over time from naïve mouse T lymphocytes treated with transforming growth factor-beta (TGFb) and IL-6.  They found that SGK1, an enzyme that phosphorylates proteins and has been shown to regulate sodium (Na+) transport and salt (NaCl) balance in other cells, was induced nearly 200-fold.  They emphasize that IL-23 is “critical” to the induction and maintenance of SGK1 but much of that evidence is relegated to the supplement data.  “Network analysis” with a computer program strengthened their suspicion that SGK1 is a “node” in the IL-23 signaling pathway.  
Mice without SGK1 (SGK1-knockouts, KO) have fewer Th17 cells that make less IL-17 when treated with IL-23; notably SGK1 deficiency also alters genes regulating other T cell subsets, including interferon-gamma (Ifng), Tbx21, and Gata3 (see also the previous gloss on transcription factors regulating Th17).  To test the role of SGK1, they immunized “floxed” SGK1 (conditional KO) mice with a myelin protein (MOG), which induces in some mouse strains a multiple sclerosis (MS) like disease called experimental autoimmune encephalitis (EAE).  EAE severity was significantly reduced in mice without SGK1 in Th17 cells or CD4+ helper T cells,  (fig 2a, KO score <1 normal="" vs.="">3), which corresponded with a greatly reduced number of Th17 cells in the organ targeted by this autoimmune disease, the central nervous system (CNS).  They also saw that CNS-infiltrating cells in EAE had expressed IL-17 at one time (eYFP+, fig 2e) but that expression of IL-17 by CD4 cells was lost in SGK1-KO animals (eYFP+ IL17-), suggesting that SGK1 was required to maintain expression.
That was nice but now the spice – could dietary salt modulate immunity through SGK1?    Indeed, they found that a high salt diet (HSD) accelerates the development of EAE in normal mice (fig 4e, top 2 lines, trend line offset to the left is HSD) while it does nothing to the GSK1-KO animals (lower, lines).  And connecting at least one of the dots between diet and Th17, they found that HSD also increased more than 2-fold Th17 cells and to a lesser degree interferon-gamma expressing cells in the CNS,  and the induction depended on SGK1 (fig 5f, shown, Th17 left, IFNg right; open bars SGK1-CD4-KO).  A companion paper pursued the role of dietary salt in EAE   

Nature. 2013 Apr 25;496(7446):513-7.  Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1.  Wu C, Yosef N, Thalhamer T, Zhu C, Xiao S, Kishi Y, Regev A, Kuchroo VK.

Sunday, April 21, 2013

Recipe for Developing Th17 cells

Thymus-dependent “T” lymphocytes develop into several effector and regulatory lineages, including the well-characterized regulatory “helper” T (Th) cells that express the cellular differentiation marker 4 (CD4+) and CD8+ cytotoxic T lymphocytes (CTL, or Tc) that kill virus-infected cells. The CD4+ Th lineages further differentiate into Th1, Th2, and Treg cells that help protect against intracellular microbes, or helminthes, or specifically regulate immune responses, as well as Th17 cells, so-called because they make the interleukin-17 (IL17) that is required for protecting the mucosa against infection by bacteria and fungi.  

Development of cell lineages is controlled externally by cytokines and internally by “master” transcription factors.  RORgt (retinoic-acid-receptor-related orphan receptors gamma t) is expressed by Th17 cells and forced expression of RORgt gene in naïve CD4+ T cells (Th0) makes them express some genes characteristic of Th17 cells such as the IL-23 receptor and the chemokine receptor CCR5 but not the full range of Th17 products, which requires other TFs including STAT3, IRF4, BATF, and IkappaBzeta.  Other TFs may replace these for inducing some Th17 genes.  The myriad of TFs required for more or less full Th17 function led these investigators to try to sort out how they work together. 
The authors first looked where these implicated TFs bind on the genome of Th0 cells treated with Th17-inducing cytokines using chromosome immune-precipitation (ChIP).  They then compared genes that are transcribed, measured using RNA seq, in the absence of specific TFs, reduced using siRNA, to “build a network model for Th17 cells”.  

They propose that TFs BATF and IRF4 bind cooperatively and open chromosomes to STAT3, which drives transcription of many genes including the lineage-specifying TF RORgt.  They also identify several putative new Th17 regulators, including the AP-1 family member Fosl2, which they belive is a key TF for Th17 development.  They derive many complicated, colorful figures.  A largely understandable, intriguing single figure is 5D, copied here, which shows that a block of Th17-related genes is increased (red) or decreased (blue) “log2 fold” (NB the genes, named on the right, are NOT the same) in Th17 cells but not other T cell subsets (Th1, Th2) treated with siRNA suppressing Satb1 (a “chromatin organizer"), Bcl11b (a zinc finger TF), Jmjd3 (a histone demethylase), and the old familiar RorC (encoding RORg).   But why no siRNA for Fosl2? 

A validated regulatory network for th17 cell specification.  Ciofani M, Madar A, Galan C, Sellars M, Mace K, Pauli F, Agarwal A, Huang W, Parkurst CN, Muratet M, Newberry KM, Meadows S, Greenfield A, Yang Y, Jain P, Kirigin FK, Birchmeier C, Wagner EF, Murphy KM, Myers RM, Bonneau R, Littman DR.   Cell. 2012 Oct 12;151(2):289-303.

Sunday, December 9, 2012

The peptide binding site accounts for the MHC link to Rheumatoid Arthritis

That genes within the major histocompatibility complex (MHC, human HLA) influence susceptibility to rheumatoid arthritis (RA) has been known for over 40 years, even before HLA nomenclature was well established (e.g., Dick et al. 1975). However, the few “classical” HLA genes constitute only a small fraction of the hundreds genes within the MHC, which include the inflammatory cytokine tumor necrosis factor (TNF), a key player in RA. Which genes are responsible for the association?

The authors investigated 5,018 “cases” of RA, all with antibodies against cyclic citrullinated peptides, CCP (i.e, seropositive, accounting for 70% of RA patients [review]) and 14,974 health, ethnically matched controls.

First they tested their ability to “impute” HLA alleles from their SNP data using a reference panel of 2,767 individuals. Conclusion, not bad: 98% accuracy for “two digit” mapping and >80% for 4 digit (allele). Then they found the most significant nucleotide (p<10^-526!) is part of a codon for amino acid 11 of the HLA-DR beta 1, and thus not part of the “shared epitope” (a 5-amino acid sequence and antibody epitope linked to RA [review]). A valine at this position confers a 3.8-fold higher risk whereas a (polar) serine is protective (the converse of risk). Comparing cases and controls shows a clear difference (shown here, from fig 3). Adding amino acids at positions 71 and 74 improved significance slightly, and alleles with these amino acids were independently shown to confer risk.

The authors conclude “These results are consistent with a disease model in which classical HLA genes and proteins are the dominant factors in rheumatoid arthritis pathogenesis, with only a minor contribution from non-HLA loci in the MHC”. It seems that someone might also explore whether these variants bind CCP better!

Raychaudhuri S, Sandor C, Stahl EA, Freudenberg J, Lee HS, Jia X, Alfredsson L, Padyukov L, Klareskog L, Worthington J, Siminovitch KA, Bae SC, Plenge RM, Gregersen PK, de Bakker PI. “Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis.” Nat Genet. 2012 Jan 29;44(3):291-6

Sunday, October 7, 2012

ENCODE salvages “junk” DNA

The “ENCyclopedia Of DNA Elements”, ENCODE, founded in 2003 with grants from the NIH Genome Institute, seeks to identify all the functional parts of the human genome, assessed by DNA and histone modifications, chromatin looping, transcription factor binding, chromatin compaction (DNAse accessibility), and transcripts.  The collaboration of ~37 groups, first developed technology. Recently they published their first salvo of 30 research papers, several published in Nature along with a News & Views.

The paper by Djebali and scores of colleagues offers “a genome-wide catalogue of human transcripts”, together with their location (nucleus or cytoplasm), and whether they have a 7mG cap 5’ or a poly-A tail 3’.  They prepared RNA from 15 human cells lines after fractionation (whole cell, nucleus and cytosol) and separation of RNA into short and long (>200 nucleotides).  Long RNAs were further separated into +/- polyA tails.  They sequenced these RNAs and determined their initiation sites and their 5’ and 3’ termini (using technologies felicitously named CAGE and PET).  Then they did bioinformatics: compared to annotated genome (GENCODE) statistics, etc.,  All these data are available for your perusal using the RNA Dashboard
They made many interesting observations; e.g., they conclude there is very little “junk” DNA.  Nearly 75% of the genome is transcribed in at least one of the cell lines, though only a little over 50% in any given line.  (This is similar to previous findings, albeit not as “encyclopedic”).  Only 28% of the 7,053 small RNAs (including snRNAs, snoRNAs, miRNAs, and tRNAs) annotated by GENCODE are found in any of these cell lines, suggesting the expression of many annotated small RNAs is cell type specific.   
They also find that protein-coding transcripts are more abundant than long non-coding RNAs (lncRNAs) and that the same genes are transcribed in different cells.  Figure 3, shown here, plots the number of transcripts (r.p.k.m., reads per kilobase per million reads) on the x axis vs. the ratio of nuclear/cytoplasmic for protein-coding (orange), which are abundant (right) in the cytoplasm (down),  non-coding (blue), and novel intergenic (green), which tend to be expressed at lower levels (left) and mostly nuclear (up).  A few individual transcripts are also identified, giving appreciation for the range of expression.
Also not for the first time, they suggest that shrinking “intergenic” regions  “prompts the reconsideration of the definition of a gene”.   They “propose that the transcript be considered as the basic atomic unit of inheritance” and that “gene … denote … all those transcripts …. that contribute to a given phenotypic trait".  Mendel would approve. 
PubMed. Djebali et al. "Landscape of transcription in human cells." 2012 Nature Sep 6;489(7414):101-8.

Saturday, March 31, 2012

Monkey’s Uncle? Your HLA might be Neanderthal

Humans’ genomes are extremely similar to those of other primates because the species diverged relatively recently, approximately 6 million years ago in the case of our nearest cousins, Chimpanzees. Modern humans and Denisovans separated 250,000 years ago (10,000 generations). With the recent sequencing of extinct, ancient hominids, such as Neanderthals and their Denisovan relatives, it was realized that up to 6% of the genomes of humans now in Europe and Asia derive from these older lineages.

HLA genes are by far the most polymorphic within the human genome, with thousands of variants (alleles). Here, investigators first identified one particular HLA allele, HLA-B*73:01, as being more similar to homologous Chimpanzee alleles than other human HLA-B alleles. This allele diverged from other HLA-B alleles 16 million years ago, before the separation of humans and Chimps, and was lost from the majority of modern humans. Its reappearance in the human genome was most likely, they reckoned, a result of “introgression”, introduction from ancient humans such as Neanderthal. An alternative model, which computer simulations indicate is 100 times less probable, is that this allele came out of Africa late

They also simply “typed” (sequenced and matched) the most important HLA loci, HLA-A, -B and –C from 1 Denisovan and 2 Neanderthal subjects. Surprisingly, most of these archaic HLA alleles were identical to common HLA types of modern humans. HLA-A2, the most widespread allele at the HLA-A locus, was shared with and might have been acquired from Denisovans. Putative archaic HLA-A alleles are now more common in China and Europe than in Africa (Figure, from fig. 4d). The authors conclude that although a small minority of our genomes overall derived from archaic humans, about half of our HLA was acquired through interbreeding between modern humans migrating out of Africa and locally established archaic humans. These archaic alleles conferred fitness in the new environment, e.g., pathogen and allergen resistance, and so outcompeted and displaced previous human HLA alleles.

The shaping of modern human immune systems by multiregional admixture with archaic humans. Abi-Rached et al. Science. 2011 Oct 7;334(6052):89-94.