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How Will Nanomachines Change the World? – The New Yorker

Posted: June 14, 2024 at 2:40 am

Ana Santos, a microbiologist at Rice University, grew up in Cantanhede, a small city in Portugal that is known as a biotechnology hub and a source of good wine. When she was a child, her grandfather, who bound books for a living, was an energetic man who often rode his bicycle around town. But by 2019, his health had deteriorated and he depended on a catheter. One day, he spiked a fever; doctors found that his urinary tract was infected with a highly drug-resistant form of Klebsiella pneumoniae, a bacteria that is commonly found in the gut. None of their antibiotics could treat it. A few days later, he died. There was literally nothing they could do for him, Santos told me recently, fury in her voice. A simple bacterial infection kills him? I thought medicine had dealt with that.

At the time, Santos was at the Centre for Interdisciplinary Research in Paris, studying genes that allow some bacteria to live longer than others. But after her grandfathers death she decided to focus instead on new ways of killing pathogens. One problem with traditional antibiotics is that bacteria, which are always evolving, can develop resistance over time. To stay competitive in the arms race between bacteria and biotechnology, Santos reasoned, scientists might need entirely new weapons. She read in Nature that scientists at Rice, led by the chemist James Tour, had developed molecular machines that spun like microscopic drills and were roughly ten thousand times smaller than the width of a human hairsmall enough to puncture and kill individual cells. Shortly thereafter, Santos moved to Houston to join Tours lab.

Now in her late thirties, Santos is congenial but reserved, with straight brown hair, rectangular glasses, and lightly accented English. She seems like the kind of person who would be the first to finish her homework, and the first to help her peers with theirs. When I visited her at Rice, this past February, she led me past microscopes, fume hoods, and amber glass jugs; the chemicals in the lab gave off a faintly sweet smell, as though the walls were painted with banana-scented varnish. I could see an inflatable T. rex on top of a fridge, grinning, and a red-white-and-blue portrait of Charles Darwin, modelled on Barack Obamas 2008 campaign posters. Very gradual change we can believe in, it read.

When we reached Santoss desk, she pulled up an image of kidney-bean-shaped bacteria on her computer. She explained that, in a petri dish, molecular machines are tiny enough to enter bacteria, affix themselves to the inside of bacterial cell walls, and tunnel through the tough outer membrane, rupturing it. The machines are activated by an intense blue light, which causes them to rotate millions of times per seconda hundred thousand times faster than a power drill. Santos showed me an image of the aftermath. The bacteria now resembled shrivelled lumps with angry blisters on their surface. She looked pleased.

Lets see these things in action, Santos said, and led me to a small room on the other side of the lab. A neon-orange biohazard sticker was plastered outside.

Dangerous pathogens in there? I asked.

She paused longer than I would have liked. Mostly mild stuff, she said. Just try not to touch anything.

We donned lab coats, gloves, and safety goggles. From an overhead shelf, Santos retrieved two petri dishes that each contained five beige moth larvae. Before Id arrived, shed injected the larvae with MRSA, an antibiotic-resistant bacterium that can cause devastating infections. Now, using a tiny syringe, she injected the larvae in one dish with a solution containing molecular machines. She slid that dish under the glow of a blue light, and I imagined thousands of little drills sticking to each bacterium and then whirring to life.

After a minute or so, Santos moved the dishes to an incubator and took out two others, which had undergone the same procedure a few hours earlier. In the first dish, which had been infused with MRSA and molecular machines, the larvae wriggled happily. I watched as one climbed on top of another, like puppies at play. In the second, the larvae that had been injected with only MRSA were crusted black. Four of them lay flat against the dish, motionless. The fifth rolled meekly to one side and lifted its darkened head. Then it dropped down, stopped moving, and died.

A few days after Christmas, 1959, in a lecture at the California Institute of Technology, the physicist Richard Feynman considered a future in which molecular machines could arrange the atoms the way we want, creating a vast array of possibilities. Such machines might, for instance, allow us to swallow the surgeon, he saidwe could ingest tiny machines that swim through our bodies to repair faulty heart valves or failing organs. Feynmans talk established the conceptual foundations for manipulating matter at the nanoscalethe scale of atoms. (If you cut a grain of sand into half a million slices, each fragment would be about a nanometre wide.) For decades, however, scientists didnt have the technology to test the idea.

A turning point came in the nineteen-eighties, when a pair of physicists invented the scanning tunnelling microscope, which was powerful enough to observe individual atoms. A few years later, K. Eric Drexler, then a research affiliate at M.I.T., published Engines of Creation: The Coming Era of Nanotechnology, a book in which he imagined nano-assemblers capable of reorganizing atoms. Drexler co-founded an organization to promote the development and use of nanotechnology, but, at the same time, he worried that without proper safeguards nanomachines could be built to replicate themselves. Drexler envisioned one apocalyptic scenario in which they fed on the materials of life and turned everything into gray goo. (Todays nanomachines are not self-replicating, but A.I. pessimists have popularized a strikingly similar thought experiment, in which an out-of-control A.I. turns everything into paper clips.)

In the nineties, a Dutch chemist named Bernard Feringa made another breakthrough: he constructed a molecule that had the unusual property of spinning continuously in one direction when exposed to UV light. The molecules central element was a carbon axis, and it spun like a pinwheel, generating a small propulsive force. Feringa later described these tiny motors as a crucial step toward realizing Feynmans vision. In 2016, he shared the Nobel Prize in Chemistry. I feel a little bit like the Wright brothers, he said, after winning the award. People were saying, Why do we need a flying machine? And now we have a Boeing 747 and an Airbus.

In 2006, Tour, the chemist at Rice, built on Feringas work to create the worlds first motor-propelled nanocar, which was roughly the width of a single strand of DNA. He attached four round formations of carboncalled buckyballsto an axle and chassis made of hydrogen and carbon. When researchers shone a UV laser on the molecule, the electrons in its central bond jumped to a higher energy state and then relaxed again, causing the motor to spin, the wheels to rotate, and the vehicle to speed forward. In 2017, a team led by Tour won the first international nanocar race, which pitted academic labs against one another in the South of France. (Scientists peered at their creations using a scanning tunnelling microscope and cheered them on; Tours achieved an average speed of ninety-five nanometres per hour.) That year, Tour published the paper that caught Santoss attention. Molecular machines could do more than compete in nano-Daytona 500s. They could potentially help deliver drugs to specific points in the body. They could also home in on dangerous cells, drill holes into their membranes, and trigger a swift and violent death.

Tour, a fit man in his mid-sixties, is courteous but playful, with salt-and-pepper hair that gives him the air of a more professorial version of Mr. Rogers. In his office, he pulled out a tray of vials, each holding different molecules; behind them were sketches of their chemical structures. Tour had constructed the molecules in the two-thousands, as a way of demonstrating the precision with which nanoscale structures could be created. The drawings looked like stick figures, and each molecule had its own nickname and headgear. One appeared to be wearing a crown (NanoMonarch); another had on a graduation cap (NanoScholar). Between them was a molecule with a cowboy hat. This was NanoTexan.

We sat down at a long mahogany table. Above us hung a portrait of Tour, sketched in the worlds thinnest known solid, graphene. Tour developed a novel production process for graphene, which he hopes could be implemented at scale; although the much-touted material was widely hyped, it has not yet entered widespread use. (He is also known for engaging in a rancorous online debate about the origins of life.)

Tour told me about two major developments in molecular machines since the twenty-tens, when he began exploring their use in medicine. The first involved the machines energy source. To activate the molecules, his team had initially used UV light, which can be toxic to our cells. (Wear sunscreen!) He walked to a bookshelf.

See this? he asked, holding up a brass-colored bullet as wide as his palm. It was hard not to. Its a .50-calibre bullet, he said. Thats UV lightit packs an enormous amount of energy. By attaching nitrogen or oxygen groups to his microscopic drills, Tours team had engineered them to instead rotate under a concentrated form of visible blue light. Some newer machines, Tour told me, could be activated with an even weaker light, known as near-infrared. Near-infrared is like a .22-calibre bullet, he said. A tiny little thing.

The second development related to how, and how rapidly, the molecules moved. A researcher in Tours lab, Ciceron Ayala-Orozco, discovered that molecules in some medical dyes could be stimulated to oscillate trillions of times a second, making them more like jackhammers than like drills. Ayala-Orozco and his colleagues went on to inject mice with millions of melanoma cells and, a week later, billions of molecular jackhammers. About half the mice who were treated became cancer-free.

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How Will Nanomachines Change the World? - The New Yorker

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Longeveron Announces Contract Development and Manufacturing Business and First Contract – GlobeNewswire

Posted: June 4, 2024 at 2:50 am

MIAMI, June 03, 2024 (GLOBE NEWSWIRE) -- Longeveron Inc. (NASDAQ: LGVN), a clinical stage regenerative medicine biotechnology company developing cellular therapies for life-threatening and chronic aging-related conditions, today announced the launch of its contract development and manufacturing business at the Companys 15,000 square feet state-of-the-art Good Manufacturing Practice (GMP) facility. This facility contains 3,000 square feet of cleanroom space, including eight ISO 7 cleanrooms and ancillary areas, as well as 1,150 square feet of process development, quality control and warehousing space. The Company also announced the initiation of work under its first manufacturing services contract with Secretome Therapeutics, a biotechnology company developing first-in-class therapeutics from neonatal mesenchymal stem cells (nMSC).

We are delighted to partner with Secretome Therapeutics to advance their portfolio of therapeutics in this, our first contract manufacturing agreement in a new, revenue generating business line, said Wael Hashad, Chief Executive Officer of Longeveron. With cellular therapy manufacturing expertise and capabilities in high demand, and Longeverons strength in both, we see a significant opportunity to employ currently unused capacity in our state-of-the-art GMP facility. We have assembled a team of experts and proprietary technologies that enable us to take a systematic approach to rapidly develop improved cell therapies. Longeverons manufacturing expertise, capabilities and facility provide other pharmaceutical organizations the ability to advance their development programs without building their own manufacturing facility. We believe this contract manufacturing opportunity can expand our teams experience and has the potential to generate approximately $4-5 million in annual revenues once it is up and running fully.

Our platform of neonatal stem cell-based therapeutics has the potential to revolutionize treatment for a wide range of chronic, inflammatory diseases, said Vinny Jindal, President and Chief Executive Officer of Secretome Therapeutics. As we move our lead product, STM-01, into clinical studies for HFpEF and dilated cardiomyopathy this year, we look forward to tapping into Longeverons extensive cellular therapy knowledge and manufacturing expertise, which has supported the launch of multiple clinical studies.

Longeveron is primarily focused on advancing development of its lead investigational therapeutic candidate, Lomecel-BTM, a proprietary, scalable, allogeneic cellular therapy, across multiple indications, including hypoplastic left heart syndrome (HLHS) (Phase 2 on-going), Alzheimers disease (Phase 2 completed), and Aging-related Frailty (Phase 2 completed).

About Longeveron Inc.

Longeveron is a clinical stage biotechnology company developing regenerative medicines to address unmet medical needs. The Companys lead investigational product is Lomecel-B, an allogeneic medicinal signaling cell (MSC) therapy product isolated from the bone marrow of young, healthy adult donors. Lomecel-B has multiple potential mechanisms of action encompassing pro-vascular, pro-regenerative, anti-inflammatory, and tissue repair and healing effects with broad potential applications across a spectrum of disease areas. Longeveron is currently pursuing three pipeline indications: hypoplastic left heart syndrome (HLHS), Alzheimers disease, and Aging-related Frailty. For more information, visit http://www.longeveron.com or follow Longeveron on LinkedIn, X, and Instagram.

Forward-Looking Statements

Certain statements in this press release that are not historical facts are forward-looking statements made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, which reflect managements current expectations, assumptions, and estimates of future operations, performance and economic conditions, and involve risks and uncertainties that could cause actual results to differ materially from those anticipated by the statements made herein. Forward-looking statements are generally identifiable by the use of forward-looking terminology such as believe, expects, may, looks to, will, should, plan, intend, on condition, target, see, potential, estimates, preliminary, or anticipates or the negative thereof or comparable terminology, or by discussion of strategy or goals or other future events, circumstances, or effects and include, but are not limited to, the potential demand for Longeverons contract manufacturing services and its ability to enter into additional service agreements. Factors that could cause actual results to differ materially from those expressed or implied in any forward-looking statements in this release include, but are not limited to, adverse global conditions, including macroeconomic uncertainty; inability to raise additional capital necessary to continue as a going concern; our history of losses and inability to achieve profitability going forward; the absence of FDA-approved allogenic, cell-based therapies for HLHS or other cardiac-related indications; ethical and other concerns surrounding the use of stem cell therapy or human tissue; our exposure to product liability claims arising from the use of our product candidates or future products in individuals, for which we may not be able to obtain adequate product liability insurance; the adequacy of our trade secret and patent position to protect our product candidates and their uses: others could compete against us more directly, which could harm our business and have a material adverse effect on our business, financial condition, and results of operations; if certain license agreements are terminated, our ability to continue clinical trials and commercially market products could be adversely affected; the inability to protect the confidentiality of our proprietary information, trade secrets, and know-how; third-party claims of intellectual property infringement may prevent or delay our product development efforts; the inability to successfully develop and commercialize our product candidates and obtain the necessary regulatory approvals; we cannot market and sell our product candidates in the U.S. or in other countries if we fail to obtain the necessary regulatory approvals; final marketing approval of our product candidates by the FDA or other regulatory authorities for commercial use may be delayed, limited, or denied, any of which could adversely affect our ability to generate operating revenues; we may not be able to secure and maintain research institutions to conduct our clinical trials; ongoing healthcare legislative and regulatory reform measures may have a material adverse effect on our business and results of operations; if we receive regulatory approval of Lomecel-B or any of our other product candidates, we will be subject to ongoing regulatory requirements and continued regulatory review, which may result in significant additional expense; being subject to penalties if we fail to comply with regulatory requirements or experience unanticipated problems with our therapeutic candidates; reliance on third parties to conduct certain aspects of our preclinical studies and clinical trials; interim, topline and preliminary data from our clinical trials that we announce or publish from time to time may change as more data become available and are subject to audit and verification procedures that could result in material changes in the final data; provisions in our certificate of incorporation and bylaws and Delaware law might discourage, delay or prevent a change in control of our company or changes in our management and, therefore, depress the market price of our Class A common stock; we have never commercialized a product candidate before and may lack the necessary expertise, personnel and resources to successfully commercialize any products on our own or together with suitable collaborators; and in order to successfully implement our plans and strategies, we will need to grow our organization, and we may experience difficulties in managing this growth. Further information relating to factors that may impact the Companys results and forward-looking statements are disclosed in the Companys filings with the Securities and Exchange Commission, including Longeverons Annual Report on Form 10-K for the year ended December 31, 2023, filed with the Securities and Exchange Commission on February 27, 2024, as amended by the Annual Report on Form 10-K/A filed March 11, 2024, its Quarterly Reports on Form 10-Q, and its Current Reports on Form 8-K. The forward-looking statements contained in this press release are made as of the date of this press release, and the Company disclaims any intention or obligation, other than imposed by law, to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise.

Investor Contact: Derek Cole Investor Relations Advisory Solutions derek.cole@iradvisory.com

Photos accompanying this announcement are available at https://www.globenewswire.com/NewsRoom/AttachmentNg/cfb4911d-2b0b-4c52-b2fa-b6982947155d

https://www.globenewswire.com/NewsRoom/AttachmentNg/0874f258-0f96-4fb6-af30-b6c77ddb02ab

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Longeveron Announces Contract Development and Manufacturing Business and First Contract - GlobeNewswire

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Genetic association mapping leveraging Gaussian processes | Journal of Human Genetics – Nature.com

Posted: June 4, 2024 at 2:49 am

Gaussian Process (GP)

Gaussian Process (GP) is a type of stochastic processes, whose application in the machine learning field enables us to infer a nonlinear function f(x) over a continuous domain x (e.g., time and space). Precisely, f(x) is a draw from a GP, if {f(x1), , f(xN)} follows a N-dimensional multivariate normal distribution for the N input data points ({{{x}_{i}}}_{i = 1}^{N}). Let us denote (X={({x}_{1},ldots ,{x}_{N})}^{top }) and (f={(f({x}_{1}),ldots ,f({x}_{N}))}^{top }), a GP is formally written as

$$f sim {{{{{{{mathcal{N}}}}}}}}(m(X),k(X,X)),$$

where m() denotes the mean function and k(,) denotes the kernel function [11]. The simplest kernel function would be the linear kernel, such that k(X, X)=2XX, while the automatic relevance determination squared exponential (ARD-SE) kernel is defined as

$$k({x}_{j},{x}_{k})={sigma }^{2}exp left[-{sum }_{q=1}^{Q}frac{{({x}_{jq}-{x}_{kq})}^{2}}{2{rho }_{q}}right]$$

for the (j, k) element of k(X, X), where ({x}_{j},{x}_{k}in {{mathbb{R}}}^{Q}) are Q-dimensional input vectors. Here 2 is the kernel variance parameter and (rho ={({rho }_{1},ldots ,{rho }_{Q})}^{top }) is the vector of characteristic length scales, whose inverse determines the relevance of each element of the input vector. Typically, the mean function is defined as m(X)=0.

Because the GP yielding f(x) has various useful properties inherited from the normal distribution, GP can be used to estimate a nonlinear function f(X) from output data (y={({y}_{1},ldots ,{y}_{N})}^{top }) along continuous factor X. The extended linear model y=f(X)+ is referred to as the GP regression and widely used in the machine learning framework [12]. This model can be used to map dynamic genetic associations for normalized gene expression or other common complex quantitative traits (e.g., human height) along the continuous factor x (e.g., cellular states or donors age). Let us denote the genotype vector (g={({g}_{1},ldots ,{g}_{N})}^{top }) and the kinship matrix R among N individuals, the mapping model, as proposed by us or others [8, 10] can be expressed as follows:

$$y=alpha +beta odot g+gamma +varepsilon ,$$

(1)

where

$$alpha sim {{{{{{{mathcal{N}}}}}}}}(0,K),quad beta sim {{{{{{{mathcal{N}}}}}}}}(0,{delta }_{g}K),quad gamma sim {{{{{{{mathcal{N}}}}}}}}(0,{delta }_{d}Kodot R)$$

are all GPs with similar covariance matrices, where denotes element wise product between two vectors or matrices with the same dimensions, K=k(X, X) denotes the covariance matrix with a kernel function, and denotes the residuals. Intuitively, models the average baseline change of y in relation to x, while represents the dynamic genetic effect along x. The effect size is multiplied by the genotype vector g, indicating that the output yi varies between different genotype groups (gi {0, 1, 2}). In fact, the effect size (xi) is additive to the baseline (xi) at each xi, which is the same as the standard association mapping. Here statistical hypothesis testing is performed under the null hypothesis of g=0, as the strength of genetic association is determined by g.

It is important to note that the model (1) includes a correction term that accounts for the between-donor variation of dynamic changes along x, particularly when multiple data points are measured from the same donor or samples are taken from related donors. This term is essential for statistical calibration of the genetic effect , because other genetic associations scattered over the genome (trans effects) can confound the target genotype effect. Therefore, to adjust for the confounding effect, we need to include the extra GP , which is drawn from a normal distribution with the covariance matrix of K multiplied by the kinship matrix R.

Here, the kinship matrix is estimated by (hat{R}=sumnolimits_{l = 1}^{L}{tilde{g}}_{l}{tilde{g}}_{l}^{top }/L) using genome-wide variants gl(l=1, ,L), where ({tilde{g}}_{l}) is a standardized genotype vector (centered and scalced) based on the allele frequency at genetic variant l, while L denotes the total number of all variants across the genome [6]. The matrix is initially a NN dense matrix, but it can be simplified if donors are (sufficiently) unrelated. Let us introduce a design matrix of donor configuration, (Zin {{mathbb{R}}}^{Ntimes {N}_{d}}), for the Nd donors (i.e., zij=1 if the sample i is taken from the donor j; otherwise zij=0), the kinship matrix can then be approximated as R=ZZ. Thus, can be expressed as a linear combination of Nd independent GPs ({{gamma }_{j} sim {{{{{{{mathcal{N}}}}}}}}(0,{delta }_{d}K);j=1,ldots ,{N}_{d}}), such that (gamma =mathop{sum }nolimits_{j = 1}^{{N}_{d}}{gamma }_{j}odot {z}_{j}), where zj denotes the jth column vector of Z. This approximation is particularly useful for parameter estimation with large Nd (as discussed in section 2.4).

When the sample size N is large, an ordinary GP faces a severe scalability issue due to the dimension of the dense matrix K being NN, resulting in a total computational cost of ({{{{{{{mathcal{O}}}}}}}}({N}^{3})). As a result, the application of GP in the GWAS field is hindered, as the sample sizes often reach a million these days. However, there are several alternatives to approximate the full GP model, including Nystrm approximation (low-rank approximation), Projected Process approximation [13], Sparse Pseudo-inputs GP [14], Fully Independent Training Conditional approximation and Variational Free Energy approximation [15]. In this section, we introduce a sparse GP approximation proposed by [16].

The sparse GP is a scalable model using the technique of inducing points [14]. Since the computational cost of the sparse GP is ({{{{{{{mathcal{O}}}}}}}}(N{M}^{2})) with M inducing points, we can greatly reduce the computational cost, which is essentially linear to N under the assumption of MN. Let us denote M inducing points by (T={({t}_{1},ldots ,{t}_{M})}^{top }) and corresponding GPs by (u={(u({t}_{1}),ldots ,u({t}_{M}))}^{top }), the joint distribution of f and u becomes a multivariate normal distribution. Therefore a lower bound of the conditional distribution p(yu) can be written as

$$log p(y| u) = log int,p(y| f)p(f| u)dfge intleft[log p(y| f)right]p(f| u)df\ = log {{{{{{{mathcal{N}}}}}}}}(y| bar{f},{sigma }^{2}I)-frac{1}{2{sigma }^{2}}{{{{{{{rm{tr}}}}}}}}{{tilde{K}}_{NN}}equiv {{{{{{{{mathcal{L}}}}}}}}}_{1},$$

where

$$bar{f}={K}_{NM}{K}_{MM}^{-1}u,quad {tilde{K}}_{NN}={K}_{NN}-{K}_{NM}{K}_{MM}^{-1}{K}_{MN},$$

and

$${K}_{NN}=k(X,X),quad {K}_{NM}=k(X,T),quad {K}_{MM}=k(T,T).$$

Therefore, the marginal distribution of the output y is approximated by

$$p(y) = int,p(y| u)p(u)duge intexp {{{{{{{{{mathcal{L}}}}}}}}}_{1}}p(u)du\ = log {{{{{{{mathcal{N}}}}}}}}(y| 0,V)-frac{1}{2{sigma }^{2}}{{{{{{{rm{tr}}}}}}}}{{tilde{K}}_{NN}}equiv exp {{{{{{{{{mathcal{L}}}}}}}}}_{2}},$$

where (V={sigma }^{2}I+{K}_{NM}{K}_{MM}^{-1}{K}_{MN}). The lower bound ({{{{{{{{mathcal{L}}}}}}}}}_{2}) is referred to as the Titsias bound and can be used for parameter estimation as well as statistical hypothesis testing.

Selecting the optimal number of inducing points M and their coordinates is crucial for accurately approximating a GP. Although a larger value of M provides a better approximation of GP, it is not feasible to increase M when N reaches hundreds of thousands in large-scale genetic association studies. Additionally, the accuracy of the GP is influenced by the complexity of nonlinearity of y and the dimension Q of input points x. There are few approaches inferring an optimal value of M from data [17], but the size of the example used in the study is too small (48 genes437 samples) to be applied to real-world data. However, it is worth noting that the optimal coordinate of inducing points with a fixed M can be easily learned from data, as described in the next section.

Genetic association mapping involves performing tens of millions of hypothesis tests. Therefore, it is almost impossible to estimate the parameters of GPs from each pair of trait and variant across the genome, even with use of the sparse approximation mentioned in the last subsection. Furthermore, both the baseline and the correction term share the characteristic length parameter (rho ={({rho }_{1},ldots ,{rho }_{Q})}^{top }) and the inducing points T. This can lead to unstable optimization and prolonged parameter estimation times. To address this issue, we have previously proposed a three-step parameter estimation strategy for performing the statistical hypothesis testing [10]. Especially, optimizing with respect to using a quasi-Newton approach (such as the BFGS method) is sufficient in the first step, because the variance explained by is typically much smaller than that explained by . The three steps are:

y=+ (baseline model: H0) to estimate and T.

y=++ (baseline model: H1) to estimate variance parameters d and 2. Here (hat{rho }) and (hat{T}) estimated in H0 are plugged into H1.

y=+g++ (full model: H2) to test whether g=0. Here ({hat{rho },hat{T},{hat{delta }}_{d},{hat{sigma }}^{2}}) estimated in H0 and H1 are used.

Here the Titsias bounds for these models are given by

$${{{{{{{{mathcal{L}}}}}}}}}_{2}^{h}=left{begin{array}{ll}log {{{{{{{mathcal{N}}}}}}}}(y| 0,V)-frac{1}{2{sigma }^{2}}{{{{{{{rm{tr}}}}}}}}{{tilde{K}}_{NN}},hfill &h={H}_{0},\ log {{{{{{{mathcal{N}}}}}}}}(y| 0,{V}_{d})-frac{1}{2{sigma }^{2}}{{{{{{{rm{tr}}}}}}}}{(1+{delta }_{d}){tilde{K}}_{NN}},hfill&h={H}_{1},\ log {{{{{{{mathcal{N}}}}}}}}(y| 0,{V}_{g})-frac{1}{2{sigma }^{2}}{{{{{{{rm{tr}}}}}}}}{(1+{delta }_{d}){tilde{K}}_{NN}+{delta }_{g}G{tilde{K}}_{NN}G},&h={H}_{2},end{array}right.$$

where

$${V}_{d}=V+{delta }_{d}({K}_{NM}{K}_{MM}^{-1}{K}_{MN})odot R,quad {V}_{g}={V}_{d}+{delta }_{g}G{K}_{NM}{K}_{MM}^{-1}{K}_{MN}G,$$

and G=diag(g) denotes the diagonal matrix whose diagonal elements are given by the elements of g. The estimators (hat{rho }) and (hat{T}) are obtained by maximizing ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{0}}) with respect to and T, and ({hat{delta }}_{d}) and ({hat{sigma }}^{2}) are obtained by maximizing ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{1}}) with respect to d and 2 given (hat{rho }) and (hat{T}).

It is worth noting that, when the kinship matrix R can be expressed as R=ZZ with a lower rank matrix (Z=({z}_{1},ldots ,{z}_{{N}_{d}})) with Nd

$${V}_{d}=V+{delta }_{d}left({K}_{NM}{K}_{MM}^{-1}{K}_{MN}right)odot (Z{Z}^{top })={sigma }^{2}I+A{B}^{-1}{A}^{top },$$

where

$$A = , (C,{{{{{{{rm{diag}}}}}}}}({z}_{1})C,ldots ,{{{{{{{rm{diag}}}}}}}}({z}_{D})C),quad \ B = , {{{{{{{rm{diag}}}}}}}}({K}_{MM},{delta }_{d}{K}_{MM},ldots ,{delta }_{d}{K}_{MM}),$$

and (C={K}_{NM}{K}_{MM}^{-1}), and B becomes a M(Nd+1)M(Nd+1) block diagonal matrix. Since the computational complexity of H1 or H2 is ({{{{{{{mathcal{O}}}}}}}}({N}_{d}^{2}{M}^{2}N)), for large Nd such as MNd>N, the total complexity is over ({{{{{{{mathcal{O}}}}}}}}({N}^{3})) and we again face the scalability issue.

However, if the donors in the data are unrelated, we can significantly reduce the memory usage and the computational burden to be ({{{{{{{mathcal{O}}}}}}}}({N}_{d}{M}^{2}N)). This is because the matrix A becomes a sparse matrix, with ({z}_{i}^{top }{z}_{{i}^{{prime} }}=0) for (ine {i}^{{prime} }), resulting in NM(Nd1) elements out of NMNd bing 0. Additionaly, non-zero elements of A are repeated and identical to the elements of C, and the block diagonal element of B is essentially ({K}_{MM}^{-1}).

To perform GWAS with GP, it is crucial to reduce the computational time required to map a genetic association for each variant. The Score statistic to test g=0 can be computed from the first derivative of ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{2}}) with respect to g, and the variance parameters ({{hat{sigma }}^{2},{hat{delta }}_{d}}) of Vd are estimated from ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{1}}) once for every single variant to be tested. Therefore, it is ideal to test tens of millions of variants independently. To use the fact that the first derivative of ({V}_{g}^{-1}) given g=0 depends only on Vd, such that

$${left.frac{partial {V}_{g}^{-1}}{partial {delta }_{g}}rightvert }_{{delta }_{g} = 0}=-{V}_{d}^{-1}G{K}_{NM}{K}_{MM}^{-1}{K}_{MN}G{V}_{d}^{-1},$$

the Score statistic can be explicitly written as

$$S={y}^{top }{hat{V}}_{d}^{-1}G{K}_{NM}{K}_{MM}^{-1}{K}_{MN}G{hat{V}}_{d}^{-1}y,$$

(2)

whose distribution is the generalized 2 distribution, that is, the distribution of the weighted sum of M independent 2 statistics, such as (mathop{sum }nolimits_{m = 1}^{M}{lambda }_{m}{chi }_{m}^{2}) [8, 10]. It is known that the weights m(m=1, , M) are given by the non-negative eigenvalues of

$${K}_{MM}^{-1/2}{K}_{MN}G{hat{V}}_{d}^{-1}G{K}_{NM}{K}_{MM}^{-top /2},$$

where ({K}_{MM}^{-1/2}) can be computed using the Cholesky decomposition of ({K}_{MM}={K}_{MM}^{top /2}{K}_{MM}^{1/2}).

To compute the p-value from S, we can use the Davies exact method, implemented in the CompQuadForm package on R. Note that, if we use a linear kernel, S can be simplified as described [8]. Although the Score based approach is an easy and quick solution for genome-wide mapping, to check the asymptotic behavior and the statistical calibration of the Score statistics, we should use a QQ-plot to verify that the p-values obtained from multiple variants follow a uniform distribution under the null hypothesis.

If the collocalisation analysis [18] or Bayesian hierarchical model [19] is considered as a downstream analysis using the test statistics, a Bayes factor can also be computed using the Titsias bounds, such as

$$log (BF)={{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{2}}-{{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{1}}.$$

Here we would use some empirical values g={0.01, 0.1, 0.5} to average the Bayes factor, instead of integrating out g from ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{2}}) [20].

In a real genetic association mapping, most of genetic associations are indeed static and ubiquitous over the factor x. To capture such a static association, we can come up with the following model

$$y={alpha }_{0}{1}_{N}+alpha +{beta }_{0}g+beta odot g+{gamma }_{0}+gamma +varepsilon ,$$

where 0 denotes the intercept, 1N denotes the N-dimensional vector of all 1s, 0 denotes the effect size of the static genetic association, and ({gamma }_{0} sim {{{{{{{mathcal{N}}}}}}}}(0,{sigma }^{2}{delta }_{d0}R)) denotes the donor variation which confounds 0. For instance, in [8], the static genetic association 0 is modeled as a fixed effect, and the dynamic effect is tested using the Score statistic. On the other hand, in [10], the authors modeled both the static and dynamic associations as a random effect to test via a Bayes factor. In this case, the covariance matrix K can be rewritten as

$${K}^{* }={sigma }^{2}{e}^{-{rho }_{0}}{1}_{N}{1}_{N}^{top }+K$$

to estimate the model parameters in (1), and then the variance g=0 for is tested.

Note here that, the kernel parameter 0 is not necessarily common and shared across , and . Indeed, in [10], the authors estimated ({hat{rho }}_{0}^{alpha }) and ({hat{rho }}_{0}^{gamma }) independently in ({{{{{{{{mathcal{L}}}}}}}}}_{2}^{{H}_{1}}). To compute the Score statistic, the authors assumed that ({hat{rho }}_{0}^{beta }={hat{rho }}_{0}^{gamma }) for and , because the ratio of the static effect to the dynamic effect can be the same for cis and trans genetic effects.

In longitudinal studies, the factor x is typically observed explicitly (e.g., donors age or physical locations where samples were taken). This makes it straightforward to perform genetic association mapping along x using the Score statistics or Bayes factors, as described above. However, this is not often the case for the molecular studies, and therefore we need to estimate the underlying biological state from the data.

In single-cell biology, typically, the hidden cellular state x is often referred to as pseudotime", and the principal component analysis is normally used to estimate it as part of dimension reduction [21]. Gaussian process latent variable model (GPLVM) is a strong alternative to extract the pseudotime when the molecular phenotype gradually changes along pseudotime x in a nonlinear fashion [22, 23].

We have also proposed a GPLVM that uses the baseline model H0 to estimate the latent variable X from the single-cell RNA-seq data (see Section 3 for more details). Let Y=(y1,,yJ) be the gene expression matrix of J genes, whose column is a vector of gene expression for the gene j, the Titsias lower bound of the GPLVM based on the baseline model H0 can be written as

$$p(Y| X)ge {{{{{{{mathcal{MN}}}}}}}}(Y| 0,Sigma ,I+{K}_{NM}{K}_{MM}^{-1}{K}_{MN})-frac{J}{2}{{{{{{{rm{tr}}}}}}}}{{tilde{K}}_{NN}}={{{{{{{{mathcal{L}}}}}}}}}_{2}.$$

To obtain the optimal cellular state (hat{X}), this lower bound can be maximized with respect to {, X, T, } [10, 24]. Here (Sigma ={{{{{{{rm{diag}}}}}}}}({sigma }_{j}^{2};j=1,ldots ,J)) denotes the residual variance parameters of J genes, and ({{{{{{{mathcal{MN}}}}}}}}(cdot )) denotes the matrix normal distribution. Due to the uniqueness of the model parameters, the variance parameter in the kernel function is set to be 2=1. In addition, to maintain the uniqueness of the latent variable estimation, a prior probability on X is required. It is quite common to assume independent standard normal distributions for each of the elements of (X sim {{{{{{{mathcal{MN}}}}}}}}(0,I,I)) [24], although there are multiple alternatives to consider depending on the nature of the modeled data [10, 23].

In the parameter estimation, the limited-memory BFGS method can be used to implement GPLVM for large N. In addition, the stochastic variational Bayes approach can be used to fit GPLVM to larger data sets, while reducing the fitting time [25,26,27].

For the non-Gaussian output y, the Titsias bound ({{{{{{{{mathcal{L}}}}}}}}}_{2}) is not analytically available. However, for the Poisson distribution case, a lower bound of the conditional probability p(yu) can be computed as follows:

$${{{{{{{{mathcal{L}}}}}}}}}_{1}=mathop{sum}_{i}left[-log ({y}_{i}!)+{y}_{i}{bar{f}}_{i}-exp left({bar{f}}_{i}+frac{{tilde{k}}_{ii}}{2}right)right],$$

where ({tilde{k}}_{ii}) denotes the ith diagonal element of ({tilde{K}}_{NN}). Let i and wi be the working response and the iterative weight of GLM for the ith sample, such that

$${nu }_{i}={bar{f}}_{i}+({y}_{i}-{w}_{i})/{w}_{i}quad {{{{{{{rm{and}}}}}}}}quad {w}_{i}=exp left({bar{f}}_{i}+frac{{tilde{k}}_{ii}}{2}right)$$

for i=1, , N, the optimal (hat{u}) which maximizes (exp {{{{{{{{{mathcal{L}}}}}}}}}_{1}}p(u)) satisfies

$$left({K}_{MM}^{-1}+{K}_{MM}^{-1}{K}_{MN}W{K}_{NM}{K}_{MM}^{-1}right)u=Wnu ,$$

(3)

where W=diag(wi; i=1, , N), which suggests

$$nu | u sim {{{{{{{mathcal{N}}}}}}}}(bar{f},{W}^{-1})$$

as described in elsewhere [28]. Therefore, we can maximize

$${{{{{{{{mathcal{L}}}}}}}}}_{2}={{{{{{{mathcal{N}}}}}}}}(nu | 0,{W}^{-1}+{K}_{NM}{K}_{MM}^{-1}{K}_{MN})$$

with respect to {2, } where (u=hat{u}) is iteratively updated as in (3). Thus, to obtain the Score statistic for non-Gaussian y, we replace y= and ({hat{V}}_{d}={W}^{-1}+A{B}^{-1}A) in (2).

For a binary output y, it is more complicated than the Poisson case, bacause it is even impossible to analytically compute the ({{{{{{{{mathcal{L}}}}}}}}}_{1}) bound with logit or Probit link function. For logit link function, several useful alternatives to the ({{{{{{{{mathcal{L}}}}}}}}}_{1}) bound have been proposed [29]. For Probit link function [30], proposed an approximation of ({{{{{{{{mathcal{L}}}}}}}}}_{1}) using the Gauss-Hermite quadrature. However, in both cases, the computational cost is much higher than the Poisson case and it is rather impractical to conduct a large genome-wide association mapping at this moment.

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‘Fossil viruses’ embedded in the human genome linked to psychiatric disorders – Livescience.com

Posted: June 4, 2024 at 2:49 am

Ancient viral DNA embedded in the human genome may boost people's susceptibility to neuropsychiatric disorders, such as depression, bipolar disorder and schizophrenia.

A study published in May in the journal Nature Communications zoomed in on human endogenous retroviruses (HERVs) snippets of DNA that form approximately 8% of the modern human genome.

Psychiatric disorders tend to run in families, and studies of twins have also hinted that genetics plays a role in whether people develop them. Estimates suggest that schizophrenia and bipolar disorder may have a heritability as high as 80%, meaning most of the variability seen in these disorders comes down to differences in people's genetics.

Specific versions of genes, or gene variants, have been tied to these disorders, but not much is known about the influence of HERVs.

Related: Common cold virus may predate modern humans, ancient DNA hints

"We were fascinated by the concept that [HERVs] existed in the human genome and so much was not known about them," study co-author Timothy Powell, a neuroscientist and molecular geneticist at King's College London, told Live Science.

HERVs are bits of viruses that have been woven into the human genome over evolutionary time, with the oldest examples introduced to our ancestors over 1.2 million years ago. Some HERVs are known to be switched on in cancer cells, and they may contribute to the disease; others are active in healthy tissues or play important roles in early development, so they're not necessarily all bad. Some HERVs are even active in the brain, but it's not yet clear what they're up to.

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Previously, scientists have studied the role of HERVs in psychiatric disorders by comparing the genetic material of individuals without such disorders with that of people affected by a given disorder. A drawback of this method, however, is that it doesn't account for the influence of environmental factors or other conditions a person may have. This makes it difficult to say with certainty that a given stretch of DNA, in isolation, is strongly associated with the disorder.

The new study used a different approach to weigh the effects of thousands of HERVs. The researchers accessed genetic data from previous studies that involved tens of thousands of people, as well as from postmortem brain tissue samples collected from nearly 800 patients with and without psychiatric disorders. They then studied which gene variants different individuals carried, noting whether they seemed to affect nearby HERVs.

They found that specific gene variants were associated with a higher risk of three psychiatric disorders schizophrenia, depression and bipolar disorder. These variants also affected whether HERVs in the brain were "switched on" and to what degree.

"This [association] gives us much more certainty that the genetic differences we're seeing between cases and controls are more likely to be a true reflection of the biology of the disorder," Rodrigo Duarte, a research fellow at King's College London, told Live Science.

The team is the first to identify five new HERVs strongly tied to psychiatric disorders. Two were associated with schizophrenia, one was common to schizophrenia and bipolar disorder, and one was specific to major depressive disorder. These five HERVs are distinct from any previously linked with each of the conditions.

"It is a major advancement," said Dr. Avindra Nath, clinical director at the National Institute of Neurological Disorders and Stroke who was not involved in the study. "The way that we've been studying all these other neurological diseases, we need to look at them again using their technique," Nath told Live Science.

The study suggests that these HERVs enhance the chances of developing the disorders, but at this point, not much can be said for how much these genetic snippets boost an individual person's risk. Carrying one of the HERVs doesn't necessarily guarantee a person will be affected by the linked disorder.

Going forward, the group plans to manipulate HERV activity in brain cells in lab dishes to see whether they affect the way the neurons grow and form connections.

"From a genetic standpoint, it's an advancement of the field," Nath said. "But from a pathogenesis standpoint, much remains to be answered" about how the HERVs actually contribute to disease.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun? Send us your questions about how the human body works to community@livescience.com with the subject line "Health Desk Q," and you may see your question answered on the website!

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Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets – Nature.com

Posted: June 4, 2024 at 2:49 am

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He Does TRT, No Big Deal Health Expert Spills the Beans on Dana White’s Fitness Transformation With … – EssentiallySports

Posted: June 4, 2024 at 2:49 am

Dana White is shredded now. Just days ago, the UFC CEO shared a post with a two-year-old picture of him looking noticeably rotund, with his belly visible through his shirt. This picture was juxtaposed by another recent picture at the bottom, where the Las Vegas resident is ripped, complete with a full set of abs and well-defined muscles.

The UFC CEO credited this turnaround to Gary Brecka and his 10X health program, a comprehensive health system that claims to use advanced testing methods to arrive at tailored, customized health solutions for its users. However, one YouTuber has pointed out the deficiencies in White and Breckas claims and why people shouldnt blindly rush to them based on the UFC CEOs transformation, as he found some aspects of them fishy.

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James Linker on his Shredded Sports Science YouTube channel critically examined Dana Whites remarkable health transformation and his tall claims in a video on his channel. Linker pointed out that the UFC CEO has himself admitted that he has started working out and eating natural food over processed foods (which have been linked to serious health conditions like obesity, high blood pressure, and diabetes). This, the YouTuber felt, was the main reason for Whites much-improved well-being and physique.

So one of the most visible health coaches right now is Gary Brecka, whos become very famous from working with Dana White, who dramatically improved his health. Dana changed his diet; he started to eat more natural than processed foods; and he started exercising every day, which I would say are the key components to his health recomposition, he said.

To which Linker sardonically observed: Oh, and he does TRT, no big deal? The YouTuber pointed out that Dana White had contradicted himself as Testosterone Replacement Therapy (TRT), which the UFC CEO had admitted to being on, was a form of medication, contrary to Brecka and Whites claims.

The UFC CEO had started taking certain supplements that Brecka had prescribed him. According to the latter, his health program involves running comprehensive tests on the patient to determine the state of their health. Based upon these findings, the patient is prescribed certain supplements to improve their health and well-being. And this is precisely the process Dana White went through before being prescribed the supplements he is currently on.

Linker then showed clips of Brecka and White poring over the latters test results. In the clip, Brecka pointed to the UFC CEOs health trending positively as evidence of his systems effectiveness, with White making certain observations, both of which rubbed Linker the wrong way.

Testosterone stayed right in the upper end of the range cuz were doing TRT hormone replacement therapy; not a huge deal there, Brecka observed, pointing to the results. This was followed by White claiming Im not taking any medication now; this is all just supplements.

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Yeah, you do [take medication], Dana. TRT is a literal medication for, for example, men over 50 who have low testosterone levels, Linker added, as he advised his viewers to focus on the time-tested methods to improve their health.

The fitness YouTuber did commend the UFC CEO for his new and improved physique and overall health but warned against falling into the trap of blindly following the latest health fads. He pointed out that while there was an abundance of newfangled and trendy ideas devised by health coaches, doing the basics, i.e., eating healthier food and eating less of it than your body could burn, combined with regular physical activity was the best bet to achieve improved health outcomes.

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Well done to Dana for improving his health but be careful with any new trending health coaching claims because, of course, calorie deficit eating, better quality of food, exercising will be the key components to a health recomposition replacement therapy,he added.

What are your thoughts on Linkers take on Dana White and Gary Breckas claims? Let us know in the comments section below.

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He Does TRT, No Big Deal Health Expert Spills the Beans on Dana White's Fitness Transformation With ... - EssentiallySports

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Next-Gen education opportunities in integrative health at Philly’s fingertips – Billy Penn

Posted: June 4, 2024 at 2:49 am

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Momentum is building in the field of integrative medicine. Can you feel it? Both patients and healthcare practitioners are becoming increasingly interested in this evolving field. The whole-person approach to care that focuses on prevention through lifestyle management and innovative solutions to chronic health challenges is beginning to take root.

Academic integrative medicine uses precision testing that includes genetics, genomics, nutrient profiling and gut microbiome testing. Therapies are informed by this testing and integrate conventional medicine with targeted nutrition, lifestyle management, hormonal balancing and much more, all with the goal of improving overall function, performance and health-related quality of life. Health professionals, including physicians, nurses, physician assistants and pharmacists, among others, are recognizing a gap in their traditional education regarding the critical elements of this integrative model.

The good news is that the leaders in integrative medicine at Thomas Jefferson University have created a Master of Science in Integrative Health Sciences for licensed healthcare practitioners. The program is offered through the College of Health Professions and much of the curriculum is developed by faculty of the historic, first-of-its-kindDepartment of Integrative Medicine and Nutritional Sciencesat Sidney Kimmel Medical College.

Natalie G., a 42-year-old full-time homecare nurse who is a current student in the program, says she was tired of not having answers. Her patients were often riddled with both acute and chronic illnesses and she was determined to pursue the pressing question: What more can I do to help them?

Despite learning the basics of nutrition, diet and lifestyle in nursing school, Natalie was limited in her capacity to answer the frequently asked questions her patients had, such as: What should I be eating? Why am I sick all the time? and Is stress playing a role here? Her old textbooks did not provide the answers, and she realized she needed more information, and from a credible source.

The reality is that traditional curricula for most health professions programs offer cursory information on topics like dietary planning, specific nutrient therapies, effective stress-reduction tools, the importance of the gut microbiome and lifestyle factors that impact chronic health issues. While this is slowly changing, many health professionals like Natalie are actively seeking the tools of next-gen health care.

In Jeffersons masters degree program, Natalie is getting foundational training in nutrition, mind-body health, advanced healing and wellness strategies and other crucial areas. The programs approach is to train healthcare practitioners to be evidence-based, patient-centric and integrative, with compassion and prevention at the center. Students in this program include physicians with many years of experience, medical students who are looking to fill a gap in their current training, chiropractors and physician assistants, as well as nurses like Natalie who are looking to advance their skillset.

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Now more than halfway through the program, Natalie reports feeling better equipped to answer her patients questions within her scope of practice. She has a newfound excitement in her approach to patient care and is incorporating what she is learning into her own health journey and with loved ones.

As a nurse, it became apparent that I could not continue to practice in a medical model that does not treat from a root cause perspective, says Natalie. This is when I came upon Jeffersons MS in Integrative Health Sciences and was delighted to meet clinicians from all fields, ranging from MDs to registered dieticians. After completing just three courses I already feel confident that I can start meaningful conversations with patients about their health and wellness from an integrative perspective. Im excited to continue in the program and expand both my personal and professional life because of the education I am getting in this program.If you or someone you know is a healthcare professional looking to change the way patients lives can be impacted through integrative health practices,you can learn more here.

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Next-Gen education opportunities in integrative health at Philly's fingertips - Billy Penn

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MTHFR Gene Variations: Everything You Need To Know, From Experts | mindbodygreen – mindbodygreen

Posted: June 4, 2024 at 2:49 am

Contributing Health & Nutrition Editor

Contributing Health & Nutrition Editor

Stephanie Eckelkamp is a writer and editor who has been working for leading health publications for the past 10 years. She received her B.S. in journalism from Syracuse University with a minor in nutrition.

Image by Clique Images / Stocksy

June 03, 2024

If you've delved into the realms of personalized medicine or precision nutritionor if you've used a home DNA test to discover your ancestry or genetic health risksyou've likely come across MTHFR gene variants. And no, it's not an abbreviation for your favorite swear word.

These genetic variants (known more scientifically as single nucleotide polymorphisms, or SNPs) have gained attention for the way they affect your ability to process and use the essential B vitamin folate and, thereby, affect a globally important (i.e., literally affects whole-body health) biochemical process called methylation.

Having an MTHFR genetic variant doesnt automatically mean you will have health issues, but if you do, there are often simple dietary and lifestyle modifications, including specific supplements, that can help support your health.*

Below, we break down which physiological processes are affected when an MTHFR gene variant is present, the health impact that may be associated with them, relevant clinical testing, and more.

Before we dive into the genetic variants and what they mean, let's talk about MTHFR itself and its role in the body when everything is functioning optimally.

MTHFR, or methylenetetrahydrofolate reductase, is an enzyme that converts dietary folate (from foods like leafy greens) and folic acid (from fortified foods and certain supplements) into a bioactive, methylated form called 5-methyltetrahydrofolate (5-MTHF).

This activated folate acts as a highly effective and required methyl donor and plays a key role in a biochemical process called methylationi.e., the transfer of methyl groups (simple structures of one carbon and three hydrogen molecules) to and from different compounds to support overall health.*

Specifically, 5-MTHF donates a methyl group1 to the amino acid homocysteine to convert it to the amino acid methionine, and methionine, in turn, can be activated to form S-adenosylmethionine (SAM-e), which travels around the body donating methyl groups to a variety of acceptors and helping regulate the activity of our cardiovascular, neurological, reproductive, and detox systems in the process.*

Holistically, this process or path is known as the methylation cycle. As Ashley Jordan Ferira, Ph.D., RDN, mbg's former vice president of scientific affairs, explains, "that first stepbioactive folate donating a methyl group to transform homocysteine to methionineis a rate-limiting step."

What does that mean? Ferira shares more: "It means that without adequate methylated folate (5-MTHF) hanging around, homocysteine can't move along efficiently to its next critical step. It can build up, and the rest of the methylation cycle is also slowed or deprived as a result. As it turns out, that's a huge deal for your health."

So what's the big deal? Methylation is a foundational biochemical process in the body (i.e., in our cells) that takes place approximately one billion times per second and plays a role in just about everythingfrom keeping homocysteine levels in check (which is directly relevant to our cardiovascular and neurological health) to manufacturing important compounds like neurotransmitters and the master antioxidant glutathione to influencing gene expression, says functional medicine physician Robert Rountree, M.D.

The MTHFR gene provides instructions for the body to make the MTHFR enzyme. "Just like there are different alleles for all kinds of genes that contribute to the unique human you are, the same is true for gene variants that impact nutrients and their metabolism," explains Ferira.

For MTHFR, there are two common variants or SNPs (changes in the DNA sequence) that affect the enzyme's activity. This means that MTHFR is less efficient at converting folate and folic acid into the active 5-MTHF form.

The most common variant in the MTHFR gene is called C677T. About 20 to 40% of white and Hispanic individuals in the U.S. have one copy of C677T, which reduces enzyme function by approximately 35%, while 8 to 20% of the population has two copies of C677T (one from each parent), which reduces enzyme function by up to 70%.

Another variant called A1298C is found in about 7 to 12% of the North American population and carrying two copies may reduce enzyme function by 40%. In total, approximately 150 million Americans have an MTHFR gene variationthat's 50% of the population!

Because the MTHFR enzyme is less active with these variants, this can directly contribute to poor conversion to active folate (5-MTHF), which can mean your methylation cycle isn't running efficiently or optimally. As Ferira alluded to earlier and echoed by Rountree, this may cause subsequent elevations in homocysteine.

But just because you have an MTHFR gene variant doesn't necessarily mean you'll experience health issues, emphasizes Rountree. "Having a genetic variant generates a hypothesis, and then you have to test that hypothesis by looking for biomarkers," he says. "Testing homocysteine levels is the one reliable marker we have to determine if we're getting enough folate to support optimal methylation."

Elevated homocysteinewhich, in addition to these unique gene variants, can be brought on by poor diet, toxin exposure, and other lifestyle factorswould indicate subpar methylation and a greater need for methylated, active folate and other useful methyl donors in the diet and from targeted supplements.*

"Not everyone with MTHFR SNPs will have subpar methylation or functional folate status; however, the combination of an MTHFR SNP and a typical American diet will often lead to methylation issues," says functional medicine physician Karyn Shanks, M.D., who openly shared that she possesses a copy of the C677T gene variant.

Signs that you have an MTHFR gene variant have the potential to be quite diverse since methylation affects so many different bodily processes.

Research is still evolving in this area, "but so are the exciting fields of precision nutrition and the practical implementation of nutrigenomics, like specialized bioactive betaine and B vitamin complexes, like mbg's methylation support+"* shares Ferira.

Testing for MTHFR variants is not widely recommended unless you have excess levels of homocysteine. Unfortunately, it's not a genetic test commonly covered by health insurance. So, if you're curious about your MTHFR status, Rountree suggests starting with a homocysteine test. It's a relatively cheap and easy blood test, and it can guide further testing.

"If homocysteine levels are subpar, then even if you do have a genetic variant, your body is handling it," says Rountree. If the opposite is true, and you're eating a healthy diet, he adds, "it's time to ask yourself, 'I wonder if there's a genetic reason for that?'"

In functional and integrative medicine, homocysteine levels less than 7 mol/L2 are often considered optimal. But as Ferira explains, "Any biomarker range can be honed to optimize the individual's health when partnering closely with a health care practitioner, but generally speaking, the normal range for homocysteine is broader and also age-dependent. Most adults want to aim for less than 15mol/L."

For older adults, the upper end of the normal range expands a bit (up to 21 mol/L).

If you're seeking to support healthy homocysteine levels (for the sake of your heart, brain, or overall health), a genetic test may help you determine if your genes are playing a key role or if you should examine other aspects of your diet (including intake of other activated B vitaminslike B6, B12, riboflavin, and folate) and lifestyle.*

If you're interested in genetic testing, consider working with an integrative or functional medicine practitioner who can run a more comprehensive genetic panel and help make targeted recommendations based on those results.

The MTHFR enzyme maintains a certain level of activity even when you have a genetic variantit's just less efficient. Increasing folate intake from dietary sources may help compensate by increasing levels of overall folate in the body, according to registered dietitian Ali Miller, R.D.

Folate-rich foods include leafy greens, broccoli, avocado, asparagus, beets, citrus, animal proteins (particularly beef liver), and legumes.

However, as Ferira further explains, "While food folate is greatand oh, by the way, leafy greens and similar plants are chock full of other nutrients and phytonutrients toothe folate naturally intrinsic to foods is in the tetrahydrofolate form, while fortified foods have folic acid. To be clear, Neither of those is fully activated 5-MTHF folate. They require several conversion steps, one of which is via your MTHFR enzyme to be fully activated."

That's why taking a supplement that already contains the bioactive form of folate5-MTHF, aka methylfolatecan help your body absorb and utilize this essential B vitamin more readily and fully than supplements containing regular (not methylated) folic acid.*

Rountree has been using and recommending methylfolate in his clinical practice for the past 20 years.

Ferira explains that she also recommends methylated folate and other bioactive B's too, saying that approach helps "clear the noise of genetic variation and deliver effective nutrition solutions that are inclusive to everyone's genetic makeup."* Miller agrees, adding that methylfolate is the most directly beneficial supplement for people with this variant, but "other methyl donors can be helpful as well, such as methylcobalamin (methylated form of B12), B6, and choline."* Choline is a precursor to betaine, a major methyl donor in this methylation story.

Holistic nutritionist and author Kelly LeVeque is also a personal fan of effective B vitamin complexes featuring methylfolate. She previously shared that when "the most bioactive forms are utilized, including methylation technology for folate and B12, this directly supports personal differences in B vitamin metabolism and cellular energy for all."*

MTHFR genetic variants are extremely common (and thus, we think should be discussed more!) and can affect the conversion of dietary folate and folic acid into active 5-MTHF.

In some individuals, this may not manifest in tangible ways, while in others, it could cause a deficit of functional folate and affect the whole-body process of methylation, leading to extra homocysteine and more.

If you think you may have an MTHFR gene variant, talk to your doctor about a homocysteine test, which is a great indicator of your folate and methylation status.

And remember, even if your results come back with room for improvement, the fix is often relatively simple: Eat a minimally processed diet with plenty of B-vitamin-rich foods and consider leveraging a high-quality, gene-focused supplement like mbg's methylation support+ that contains active betaine and B's including methylfolate (5-MTHF).*

If you are pregnant, breastfeeding, or taking medications, consult with your doctor before starting a supplement routine. It is always optimal to consult with a health care provider when considering what supplements are right for you.

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BlueSphere Bio Announces IND Clearance of its First in Human Candidate and New Cell Therapy Portfolio for High-Risk Leukemia Patients

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Ascendis Pharma Presents New Data and Updated Results from Phase 1/2 IL-Believe Trial at ASCO 2024

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