Histone Deacetylase 3 Aggravates Type 1 Diabetes Mellitus by Inhibiting Lymphocyte Apoptosis Through the microRNA-296-5p/Bcl-xl Axis
Authors: Jing Tian
Qibo Hu
Guanghua Che
Yu Yang
Hongchang Xie
Journal: Frontiers in Genetics (2020)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: B-cell leukemia-XL; apoptosis; histone deacetylase 3; microRNA-296-5p; peripheral blood mononuclear cells
Affiliations: Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China.

Abstract:  Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by immune-mediated destruction of pancreatic beta-cells. Multiple microRNAs (miRNAs) have been implicated in T1DM pathogenesis. Although histone deacetylase 3 (HDAC3) has been reported to be involved in T1DM, the underlying mechanisms remain to be further elucidated. This study was designed to investigate the potential regulatory role of Hdac3 on T1DM progression. The expression of miR-296-5p and B-cell leukemia-XL (BCL-XL) was determined using RT-qPCR and Western blot assay in peripheral blood mononuclear cells (PBMCs) of patients with T1DM, tumor necrosis factor-α (TNF-α)- and cycloheximide (CHX)-induced cell model, and streptozotocin (STZ)-induced rat model. The binding affinity between miR-296-5p and Bcl-xl was verified by using dual-luciferase reporter gene assay, and the binding between Hdac3 and the promoter region of miR-296-5p was validated using chromatin immunoprecipitation assay. Western blot analysis and flow cytometry were conducted to assess the apoptotic events of lymphocytes. miR-296-5p expression was downregulated while BCL-XL expression was upregulated in PBMCs of patients with T1DM. An adverse correlation was identified between miR-296-5p and Bcl-xl in mouse TE15 B lymphocytes. Bcl-xl was further validated to be targeted and negatively regulated by miR-296-5p in 293 T cells. Hdac3 inhibited miR-296-5p expression by binding to its promoter region. The effects of overexpressed Hdac3 on lymphocyte apoptosis was counterweighed via downregulation of Bcl-xl or upregulation of miR-296-5p, the mechanism of which was further validated in a rat model of DM. Taken together, the Hdac3-mediated upregulation of Bcl-xl via inhibiting miR-296-5p promoter activity enhanced the anti-apoptotic capacity of lymphocytes to accelerate the occurrence of T1DM. 

Retracted: No

PubPeer comments:
 Overall, I love aspects of this paper. I think this paper asks a brilliant question. After speaking with Konrad on [#TheLearningSalon] , I now better understand their perspective. I think it boils down to a few criticisms of contemporary neuroscience practice: 1. Much of systems neuroscience is devoted to essentially estimating tuning curves. There are lots of other approaches one could take that have value; our field would be better served if diversify our approaches. 2. The conclusions from these studies often rest on questionable assumptions, and our field would be well-served if we questioned those assumptions more forcefully. I agree with those conclusions. However, I maintain that the evidence they bring to bear on those conclusions in this manuscript is weak, for the reasons I outline below. I think a paper that more forcefully and compellingly made the arguments that the made in #TheLearningSalon would be highly valuable to the community. but provides a nonsense answer. 1. The last line of the paper,  Ultimately, the problem is not that neuroscientists could not understand a microprocessor, the problem is that they would not understand it given the approaches they are currently taking. u00a0 feels like a strawman argument to me.u00a0 Neuroscientists don t just blindly apply algorithms that we know to data, rather, we try to answer a particular question about the data, and use whichever tools we have to answer them.u00a0 I think what you showed is that a data scientist that is given a bucket of neuroscience data cannot figure out how the brain works.u00a0 But a data scientist given a bucket of data without domain knowledge can almost never figure out how something works, that is not an accurate depiction of the scientific process.u00a0 2. Similarly, lots of neuroscientists specifically do not do the proposed approach.u00a0My favorite recent example is Paul Cisek su00a0phylogenetic refinement. Cisek s approach is just one manifestation of starting at Marr s top level, and then working your way down.u00a0 This paper only tried starting at Marr s bottom level, and working your way up.u00a0 3. Even still, the paper only considered analytical tools that basically could not identify interesting latent structure if it existed.u00a0 For example, manifold learning tools, hierarchical clustering, would likely have revealed *something* of interest about the data, that we could dive down into.u00a0 The authors even mention this in the 3rd to last sentence of the paper,  Lastly, better data analysis methods, e.g. those that can explicitly search for hierarchical structure or utilize information across multiple processors. so even the naive data science reductionist wouldn t have failed so completely as the authors did. In summary, I think a good data scientist operating in a vacuum would have made progress, a better data scientist who collaborated with a neuroscientist would have made more progress, and a neuroscientist who collaborated with a data scientist, where the neuro person asked the question and provided domain knowledge, would make even more progress.u00a0u00a0 This is not just a guess; rather, Hopfield and Brody s 2000 PNAS paper, and their 2001 follow-up, explicitly demonstrated that a neuroscientist (in fact, several) can figure out a neural system using just the data they have available to them.u00a0 In other words, 17 years prior to the publication of your paper, a previous paper was published demonstrating your conclusions were false.u00a0 Given that, one can reasonably wonder what value does your paper add to the field? A not so unreasonable interpretation of this paper it that it is unjustified pessimism based on a flawed experimental design, and its main outcome has been to terrify sadden early career researchers (ECRs) to show them that even if they get the best data, we are doomed, because neuroscientists are dumb. Is it not our job to write papers that inspires the ECR s, to tell them what neuroscientists could figure out about a microprocessor, and then show the legitimate limitations of our capabilities, with a call to arms for developing more effective techniques? , 
 JTV asks what value does this paper add to the field; but he s already answered that question himself when he says that  The conclusions from these [systems neuroscience] studies often rest on questionable assumptions, and our field would be well-served if we questioned those assumptions more forcefully. What could be more important than to highlight and analyze, for those to whom it isn t yet clear, why common approaches lack rigor? The hostility engendered by the paper, referenced in the Learning Salon (and that JTV seems somewhat to share), shows that its points were not ho-hum stuff everybody knows, but a point of view that needed to be pushed forward and outward. Students need to be made aware sooner rather than later to think critically and that approaches or practices they re being taught may not be, well, valid.  This is not just a guess; rather, Hopfield and Brody s 2000 PNAS paper, and their 2001 follow-up, explicitly demonstrated that a neuroscientist (in fact, several) can figure out a neural system using just the data they have available to them. Hopfield and Brody didn t show anything about a neural system, just a simple computing network they invented. No one really knows how biological neural systems work, so this doesn t seem like as strong a point as JV seems to think. Kind of circular, really; we have to know how neural systems work to show we re capable of figuring out how neural systems work.  with a call to arms for developing more effective techniques? What I ve heard Kording say often is there s a lack of theories in neuroscience. Technique follows theory, not the other way around. Speaking of theories, Brette (2018) argues that the view of neural functioning based on the  coding metaphor isn t a good one for a number of reasons that seem related to the problems KK highlights with  tuning curves. But other than BBS where it was originally published, I m not aware of the issue approach being debated or challenged. Maybe a topic for LS? , 
 Mimosa makes a number of valid points. A refined view in light of your perspective: This paper could be interpreted in many ways, depending on the proclivities of the reader. One option would be for those are that are doing tuning curve and or some kinds of connectomics work, would be to look for other paradigms within computational neuro that are more likely to yield answers to the motivating questions, and rest on less shaky grounds. Another take is that the field is wide open, so ECR can make a huge difference by not already buying in too much to a failed dogma. I think the most important question K and E asked in this paper was whether they could find donkey kong neurons in their microprocessor, and they did. This particular experiment illustrates making a false conclusion based on false assumptions and perfectly fine algorithms. The others mostly found nothing interesting, but arguable were not led astray. So, one very important point that they make, that neuroscientists using data science tools often fail to appreciate, is that we need to check our tools with both false positive and false negative controls, much like knock-in and knock-out experiments. Our tools should fail when they should, and succeed when they should, and tell us they don t know when they don t know. Very few data science tools have that property, so it is a big opportunity. , 
 You say J K may be interpreted in one of two ways, and add a third one of your own. The first two are: u201cOne option would be for those are that are doing tuning curve and or some kinds of connectomics work, would be to look for other paradigms within computational neuro that are more likely to yield answers to the motivating questions, and rest on less shaky grounds. Another take is that the field is wide open, so ECR can make a huge difference by not already buying in too much to a failed dogma.u201d These two seem pretty much alike to me. They both seem to agree current u201cdogma(s)u201d or u201cparadigms,u201d are failed. If I m reading it right, you re basically in agreement with these conclusions. Your third interpretation seems to flow more or less along the same lines, but frames failures in terms of lack of the right tools: u201cOur tools should fail when they should, and succeed when they should, and tell us they don t know when they don t know. Very few data science tools have that property, so it is a big opportunity.u201d What I m hearing is that neuroscience is has been failing, and that the tuning curve sensory coding literature may be equivalent in style and essence to what Jonas and Kording are describing, and that it needs to change course. Is it changing course? Is anyone working on more potentially productive approaches, where an ECR could have the space to begin addressing this challenge? How are ECR s supposed to exploit the opportunity of past failure if failure still looks like (and amounts to, in the career sense) success, if past lit is no guide and if novel and promising paradigms are lacking? (p.s. which past lit would you recommend and could you discuss which data science tools have the property referenced above?) , 
 Having read the microprocessor paper, written three years ago, and others like it, having watched KK lecture on it, having watched the recent discussion on this paper in the Learning Salon https: www.crowdcast.io e learningsalon, I have a feeling of deja vu. In this latest talk, Kording pulls no punches: u201clet me highlight how unbelievably single-mindedly unreflective neuroscience is...w in systems neuroscience, and I include fMRI in that, the field has been completely dominated by tuning curve mapping...for every brain area that has a name...and a lot of areas that didn t have a name before, someone got insanely famous for having stuck an electrode somewhere, changed the stimulus in some way, and said hey, this is [active] when I have dots moving but if I have lines moving it s not happening, or this is where faces are great but shoes don t work. And this is a large proportion of thte people who are famous in systems neuroscience. They didn t ask themselves what exactly does that mean to have those tuning curves. Every paper comes with like now we ve figured out how color works, the papers are full of allusions to mechanistic insight coming from there being a correlation. They didn t even bother to make sure that they go through all the different dimensions that those neurons could also be tuned for...instead there was this folk idea of what each brain area does. Show me the papers that really .... [justify] the bridge b ween mechanistic statements and what they actually did...u201d He also says: Neuroscientists do all these cool ways of thinking...as a group there s incredibly creative work that s logically great but the vast majority of studies that I see in neuroscience take a very unreflective perspective where they basically copy an experimental design that has been formed long ago without asking what we really learn from the data...u201d Could he please offer some particulars of researchers, articles, research programs u2013 that figure among the u201cunbelievably unreflectiveu201d ones, and, conversely, those that are u201cincredibly creative and logically greatu201d? Because it seems to me these perennial, generic discussions are having no effect on the bad work, which continues to be churned out at an alarming rate, on the one hand, and on the other, there s no recognition for the alternatives which Kording indicates exist without going into specifics (at least not in a forum I m aware of). Maybe if we named some names u2013 old and new u2013 eliciting feedback, evaluating, developing consensus on, e.g. H + W, who were basically doing primitive tuning curve stuff u2013 these discussions could make some kind of a difference. The kinds of things Kording is saying should be drawing blood; as it is, he might as well be blowing kisses. Being right is not enough. , 
 #5  Could he please offer some particulars of researchers, articles, research programs u2013 that figure among the u201cunbelievably unreflectiveu201d ones, and, conversely, those that are u201cincredibly creative and logically greatu201d?  I second the motion. Point out the work you consider valid, at least. Someone could organize a  Peeriodical highlighting and analyzing these directions. , 
 As a philosopher of science, I have found this discussion rather fascinating. If Jonas and Kording are correct u2013 and their critique makes sense to me, while there also appears to be some degree of consensus within the expert community involved u2013 neuroscience is experiencing a crisis in the Kuhnian sense u2013 it s normal methodological tools and dominant paradigm aren t working, requiring a transition to a more effective paradigm. At the same time, and predictably, there is resistance to change. As one or two of the previous commenters have pointed out, in this phase there would seem to be value in a discussion of the more apparently promising u2013 creatively, logically - avenues being entertained during this transitional period. , 
 #2 Totally agree, one of the responses to Brette s piece was by Lee De Wit et al., who also wrote a wonderful paper called  Is neuroimaging measuring information in the brain? https: link.springer.com article 10.3758 s13423-016-1002-0 There absolutely needs to be more theoretical discussion and subsequent experiments about these issues! #5, #6 Just a hunch, but maybe he means some of the articles in the journal he is on the Editorial Board of, Neurons, Behavior, Data analysis, and Theory: https: nbdt.scholasticahq.com , 
 Correction: Hop Bro show that a neuroscientist can figure out a *simulated* neural system, not a real one. 100 years of neuroscience shows that neuroscientists can figure out some neural systems. , 
 #9 Not clear what you re saying, here, J.T.. Have 100 years of neuroscience shown that neuroscientists can figure out simulated systems, or real ones (and what is the connection between the two, as systems?). , 
 Sorry for confusion. Hop Bro show neuroscientists can figure out *simulated* neural systems. 100 years of neuroscience shows that neuroscientists can figure out some *real* neural systems. That is not to say our field does not have significant methodological and philosophical problems, alluded to in this paper, rather, they empirically demonstrate some neuro tools don t decider some system, so my counter-point is that neuroscientists can figure out some systems (both synthetic and real). , 
 #11 What are some examples of the real ones? Other than Hodgkin Huxley? I think someone asked this question earlier... , 
 Central pattern generators, lots of peripheral stuff, lots about eye movements, some auditory localization stuff. Mostly very simple stimuli response circuits, but fairly well characterized in my humble opinion. There is a legit question as to whether those approaches will scale to more complex neural circuits. , 
 #13 Hopfield and Brody (2000) is based on the  scaling up of very simple assumptions - far too simple. Their acoustic  pattern selective neurons cannot be reconciled with the facts of audition and the problems the system is confronted with, as explored by the outstanding work of Albert Bregman (see Auditory Scene Analysis 1990 MIT Press). For example H and B refer to a neuron that  fires in response to this word, whether it is spoken rapidly or slowly, or when spoken by a variety of speakers. But we know that what we hear is often not, if ever, based on an acoustic template. Words spoken in different accents may differ radically in their acoustic composition; moreover, as Bregman discusses, in a normal acoustic environment vibrations are mixed together and segregated  patterns do not arrive in clean packages, but as an intercalated sequence that needs to be analyzed and synthesized. The McGurk effect is another well-known case in point; what we hear does not correspond to either a visual or acoustic template. Most published models and simulations seem to be of the H B type; assumptions are selected based on simplicity (sometimes referred to as mathematicall tractability) not the facts of psychology and biology, H B and others description of their models as  biologically plausible notwithstanding. At a minimum, it doesn t seem too much to ask that modellers respect the facts; like theories, models should be simple, but not too simple. , 
 The crazy thing about this paper is that the stated conclusion is that these techniques  fall short of producing meaningful understanding of neural systems, regardless of the amount of data. But the crazy thing is that they don t fall short. The author s first naive attempt at PCA pulls out the clock in the First Principal component. Surely that is meaningful understanding of the microprocessor s function!!! ,