Tag: Fibroblast

Resistant starch diet proves a game changer for weight loss and diabetes control
[ad_1]
In a recent study published in the journal Nature Metabolism, a team of scientists investigated whether modulation of the gut microbiome using dietary fiber supplementation in the form of resistant starch could help with insulin resistance and weight loss and offer a potential treatment avenue for metabolic disorders.

Study: Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota. Image Credit: Sokor Space / Shutterstock

Background

Obesity has been classified as a global epidemic, with substantial research being conducted on strategies to reduce weight and prevent obesity. It contributes significantly to the global mortality rates by increasing the risk of metabolic diseases such as diabetes, as well as cardiovascular disease risk. Weight management and effective weight loss can lower the risk of these diseases.

Increasing evidence indicates that the gut microbiome plays a pivotal role in the regulation of human physiology and development of various diseases. Gut microbiome composition and diversity are intricately linked to the metabolism of glucose and fat and inflammation.

Furthermore, while fecal microbiome transplantation has been used to establish healthy gut microbiome communities, the procedure has not yielded effective or long-term results. However, diet can be used to modulate the gut microbiome, and dietary interventions, either alone or in conjunction with fecal microbiome transplantation, could potentially improve the clinical outcomes.

About the study

In the present study, the team conducted a randomized, crossover clinical trial involving overweight individuals to determine whether dietary supplementation with resistant starch positively impacted obesity and metabolic phenotypes. They also conducted metagenomic and metabolomic analyses to understand how the resistant starch affected the composition of the gut microbiome and its function.

Furthermore, they studied antibiotic-treated mice that had received gut microbiomes from human donors that had already been modified through resistant starch supplementation to understand how gut microbiomes modified through supplementation with resistant starch influence glucose metabolism and adiposity. The metabolomic advantages offered by the gut microbiome modified through resistant starch supplements were also explored.

Resistant starch cannot be broken down by the amylase enzymes produced in humans, functioning as a dietary fiber. During digestion, resistant starch does not get broken down in the stomach or small intestine but moves into the large intestine or colon, where the gut microbiome ferments this dietary fiber. Rodent model studies have shown a decrease in body fat and better metabolic outcomes when the carbohydrate portion of their diet consists mainly of resistant starch.

The present clinical trial included participants with excess body weight who did not have any chronic disorders, were not using any probiotics or antibiotics, and were not undergoing any treatments that would impact their glucose metabolism. The participants were randomly assigned to the treatment or control group, with the treatment group receiving resistant starch in the form of high-amylose maize and the control group receiving amylopectin with no resistant starch.

The starch was provided in sachets in powdered form, and all the participants in the treatment and control groups consumed one packet of the appropriate starch twice a day before a balanced, isoenergetic meal that was provided thrice a day. Since this was a crossover clinical trial, all the participants underwent two eight-week-long interventions, one for the resistant starch treatment and the other for the control treatment.

Results

The results showed that supplementation with resistant starch helped achieve a mean weight loss of about 2.8 kg and improved insulin resistance in overweight participants. The study also found that the beneficial effects of resistant starch supplementation were associated largely with gut microbiome composition changes.

The bacterium Bifidobacterium adolescentis was found to be associated with resistant starch supplementation in humans, and the monocolonization of mice with this bacterium protected them from diet-induced obesity. Resistance starch impacted lipid and fat metabolism by reducing inflammation, restoring the intestinal barrier, and altering the bile acid profile.

The gut microbiota impacts the host physiology through signaling metabolites, of which bile acids play a significant role. Secondary bile acids, such as glycodesoxycholic acid, deoxycholic acid, glycocholic acid, and taurodeoxycholic acid, are important in improving insulin sensitivity and ameliorating hepatic steatosis. The enzyme bile salt hydrolase carries out the deconjugation of secondary bile acids.

The study found that resistant starch supplementation decreased the production of bile salt hydrolase and increased the levels of secondary bile acids. The results were reciprocated in the mice after they were monocolonized with B. adolescentis from humans who underwent resistant starch supplementation.

Resistant starch (RS, 40 g d^-1) accompanied with isoenergetic and balanced diets led to an obvious reduction in body weight and improvement of insulin sensitivity, as well as alteration in metagenomics and metabolomics. Faecal microbiota transplantation (FMT) showed benefits of RS were associated with the reshaped gut microbiota composition. Monocolonization of mice with B. adolescentis, which was closely correlated with the benefits of RS in human protected mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota influenced metabolites of gut microbiome, reduced chronic low-grade inflammation by improving intestinal integrity, inhibited lipid absorption by modulating angiopoietin-like 4 (ANGPTL4), and improved the sensitivity of fibroblast growth factor 21 (FGF21) in adipose tissue. SPF, specific-pathogen-free; LPS, lipopolysaccharide; BCAAs, branched-chain amino acids; Erk1/2, extracellular signal-regulated kinase 1/2; FGFR1, fibroblast growth factor receptor 1. Created with BioRender.com.

Conclusions

To summarize, the study found that supplementation with resistant starch can facilitate weight loss by increasing the abundance of B. adolescentis in the gut microbiome. It can also help improve insulin sensitivity through gut microbiome-induced changes in the levels of secondary bile acids and lowering of inflammation.
Journal reference:

Li, H., Zhang, L., Li, J., Wu, Q., Qian, L., He, J., Ni, Y., KovatchevaDatchary, P., Yuan, R., Liu, S., Shen, L., Zhang, M., Sheng, B., Li, P., Kang, K., Wu, L., Fang, Q., Long, X., Wang, X., & Li, Y. (2024). Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota. Nature Metabolism. DOI: 10.1038/s4225502400988y, https://www.nature.com/articles/s42255-024-00988-y
[ad_2]

Source link
February 28, 2024
Hyaluronic acid injections offer lasting improvement in aging skin
[ad_1]
The fragmentation of type I collagen has been seen to impair the integrity of the dermal extracellular matrix (ECM). This results in lower type I procollagen synthesis and contracted or collapsed fibroblasts.

A recent Experimental Dermatology study assessed how these deleterious changes could be reversed by injecting cross-linked hyaluronic acid (CL-HA).

Study: Implications for cumulative and prolonged clinical improvement induced by cross-linked hyaluronic acid: An in vivo biochemical/microscopic study in humans. Image Credit: Ground Picture/Shutterstock.com

Background

Photoaging of the human skin is caused by chronic exposure to ultraviolet irradiation. This is characterized by fragility, wrinkles, and low elasticity, mainly caused by dermal deleterious molecular alterations.

ECM in the dermis comprises 80%–90% type I collagen, which supports and strengthens the human skin. Dermal fibroblasts produce type I collagen, which forms an interwoven mesh in the ECM. The interactions between the ECM and dermal fibroblasts determine cell functions.

As the skin photoages, fragmentation of type I collagen occurs due to a greater expression and enzymatic activity of metalloproteinases (MMPs). This weakens the collagenous scaffolding, making it difficult for fibroblasts to bind.

Prior research has shown that cross-linked hyaluronic acid (CL-HA) injection can reverse these changes.

About this study

To delve further into the biochemical mechanism of CL-HA action, CL-HA and vehicle (saline) were injected into the photoaged skin of human subjects aged 60 years and above.

To assess whether fibroblast activation led to the deposition/accumulation of dermal collagen, biochemical/microscopic analyses were performed.

Study findings

From 1 week to 6–9 months post-injection, fibroblasts demonstrated activation. Multiphoton microscopy at 1-week post-injection showed stretching of fibroblasts. This indicated greater dermal mechanical support.

A second harmonic generation microscopy analysis at four weeks post-injection showed densely packed thick collagen bundles around pools of injected CL-HA.

At 12 months post-injection, it was noted that thick collagen bundles accumulated, and a significant amount of CL-HA was also present.

Therefore, it was concluded that CL-HA enhanced mechanical support rapidly and durably by occupying space in the dermal ECM.

CL-HA prompted fibroblast activation and elongation, resulting in densely packed and thick type I collagen bundles at four weeks post-injection and continuing till at least week 52.

Important components of the TGF-β pathway were also stimulated post-injection, and this pathway is crucial for the synthesis of type 1 procollagen by fibroblasts.

The elements of the TGF-β pathway are decreased in photoaged skin, which reduces the synthesis of type 1 procollagen. The findings of this study suggest that the activation of the TGF-β pathway could regulate the response of fibroblasts to higher dermal mechanical support.

It was noted that post-injection with CL-HA, procollagen N- and C- proteinase enzymes increased rapidly. These enzymes aid in the assembly of intact type 1 collagen.

Immediately after the injection, clinical improvement of the skin could be due to the filler, which occupies space in the dermal ECM and provides mechanical support.

Considering the fact that collagen bundles accumulated till at least week 52, it could be the case that following the CL-HA injection, the type 1 procollagen is converted to

durable and stable dermal type 1 collagen. Furthermore, the presence of the injected filler after 12 months suggests the are long-lasting clinical benefits of CL-HA.

Future research should investigate which properties of HA contribute to the clinical benefits of CL-HA.

The findings documented here have other clinical implications as well. Intact and mature type 1 dermal collagen has an extremely stable half-life of approximately 15 years.

This suggests that collagen bundles should continue accumulating for years after a single CL-HA injection, reducing the need for subsequent treatment following each re-injection.

The observations made here also support the theory that the integrity of the dermal ECM heavily determines functional decline and fibroblast collapse in photoaging.

Interestingly, these functional and cellular changes are reversible by stimulating fibroblasts in photoaged skin by augmenting dermal mechanical support.

Conclusions

In sum, the findings in this study suggest that interventions that provide more mechanical support to photoaged can activate fibroblasts durably and rapidly, thereby leading to collagen deposition.

More specifically, a CL-HA injection stimulated synthetic activation and fibroblast stretching durably (for 6-9 months) and rapidly (within one week).

The result was the accumulation of densely packed and thick type 1 collagen bundles. This accumulation started as early as four weeks and lasted at least a year post-injection.

A sustained clinical improvement is obtained by the accumulation of dermal collagen, which can last for years. The findings of this study pave the way for future research on the frequency and timing of repeat CL-HA injections.
Journal reference:

Wang, F., Do, T. T., Smith, N., Orringer, J. S., Kang, S., Voorhees, J. J., and Fisher, G. J. (2024) Implications for cumulative and prolonged clinical improvement induced by cross-linked hyaluronic acid: An in vivo biochemical/microscopic study in humans. Experimental Dermatology. 33(1), e14998. doi: https://doi.org/10.1111/exd.14998. https://onlinelibrary.wiley.com/doi/10.1111/exd.14998.
[ad_2]

Source link
February 19, 2024
Research demonstrates a bat species’ resistance to cancer, pinpoints key genes
[ad_1]
In a recent study published in the journal Nature Communications, researchers investigated seven species of bats to verify hypotheses about their potent cancer resistance empirically. A combination of in vitro and in vivo techniques revealed that one species, Myotis pilosus, displayed particular cancer resistance despite researchers intentionally activating the ontogenetic genes in their primary cells. Analysis of this phenomenon using transcriptomic and functional tests suggested that the unexpectedly potent downregulation of HIF1A, RPS3, and COPS5 genes and the loss of a COPS5-promoting enhancer along the HIF1A sequence may be the key behind M. pilosus’ extreme cancer resistance.

Study: Experimental evidence for cancer resistance in a bat species. Image Credit: Rudmer Zwerver / Shutterstock

Bats are proof that not all animals are built equal

Bats are considered one of the best-adapted mammalian groups in terrestrial and particularly arboreal environments. Bats come in all spaces and sizes, from the penny-sized Kitti’s hog-nosed bat to the six-foot-wide-wingspan flying fox and all 1,400 species in between. When accounting for the fact that bats comprise approximately 20% of all known mammalian species, their success becomes evident.

Scientists have studied bats to pinpoint the secrets of their success. Thus far, they believe the evolutionary dominance of bats to be attributable to a few crucial adaptations, most notably their evolution of actual flight, echolocation, high viral resistance, and commendable longevity. Their longevity, in particular, is extraordinary and comparable to genuine relative size-age outliers like the naked mole rat and blind mole rat. Indeed, 18 out of 19 size-corrected mammalian species with natural lifespans longer than our medically-assisted ones are bats, with some species like Myotis myotis living eight times longer (41 years) than expected by size alone.

Given the observed evolutionary interplay between cancer and longevity, bats are hypothesized to mirror naked mole rats and elephants in having evolved adaptations that prevent cancer onset and proliferation. Unfortunately, this hypothesis remains untested within an empirical scientific framework. Verifying this hypothesis and elucidating the mechanisms responsible would provide crucial insights into natural cancer resistance and the potential for developing novel anticancer therapeutics.

About the study

In the present study, researchers used somatic tissues (e.g., skin grafts) and genetic material from seven bat species to investigate their cancer resistance in vitro and in vivo. The included species were Chinese and comprised the big-footed bat (Myotis pilosus; MPI), the least horseshoe bat (Rhinolophus pusillus; RPU), the Szechwan myotis (Myotis altarium; MAL), the greater horseshoe bat (Rhinolophus ferrumequinum; RFE), the great leaf-nosed bat (Hipposideros armiger; HAR), the Chinese rufous horseshoe bat (Rhinolophus sinicus; RSI), and the Leschenault’s Rousette (Rousettus leschenaultii; RLE).

Researchers additionally used tissues from Mus musculus, the typical rat lab, as controls for all experiments. To gain insights into the resistance of sampled tissue to malignant transformation, systematic investigations of the tumor resistance of primary fibroblasts were carried out by priming fibroblasts to express oncogenic HRAS(G12V) and SV40 large antigen (SV40 LT) genes, followed by protein level quantification using immunoprecipitation assays. These Vitro assays were supplemented from luciferase immunofluorescence assays carried out in genetically modified murine (MSF^HRAS\SV40LT) and MPI (MPI-SF^HRAS\SV40LT) fibroblasts.

Once the atypical cancer resistance of MPI fibroblasts was established, researchers investigated the mechanism underlying observed resistance using transcriptome sequencing of fibroblasts, thereby identifying differential expression patterns of fibroblast-associated genes. Analyses included the signed weighted gene co-expression network analysis (WGCNA), the in-tandem computing of the module eigengene (ME), and the de-novo development of a protein-protein interaction network derived from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database.

To test if observed cancer resistance could be a function of specific gene downregulation, CRISPR-Cas9 gene-editing technologies were used to inhibit the expression of genes known to affect cancer resistance, including HIF1A, COPS5, RPS3, EP300, and EIF5B in MSF^HRAS\SV40LT. Finally, to elucidate the molecular basis underpinning natural gene downregulations, conserved non-coding elements (CNEs) were analyzed via the creation of a de-novo MPI genome followed by the Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-seq assay).

Study findings – not all bats are built equally, either

Both in vitro and in vivo fibroblast assays revealed that MPI fibroblasts were significantly more resistant to cancer and cancer-associated proliferation than controls and the other six investigated bat species. MPI fibroblast colonies were consistently found to be substantially smaller than those of the other tested cohorts, validating its profound anticancer properties. Repeating these experiments using other tissue types (intestine and tail tissues) provided comparable results, validating these findings and the hypothesis of bats displaying natural cancer resistance.

Transcriptomic protein expression quantification assays present that the relative expression levels of MPI HIF1A, EP300, EIF5B, COPS5, and RPS3 genes were significantly lower (downregulated) compared to the other cohorts, suggesting oncogene downregulation as the mechanism of action underpinning observed fibroblast results.

“Our results showed that the suppression of HIF1A, COPS5, and RPS3 expression significantly inhibited cell proliferation (P < 0.05; two-tailed Student’s t tests. However, the downregulation of EP300 and EIF5B had no remarkable effect on cell proliferation. Notably, these two genes were up-regulated during aging in the long-lived bat (Myotis myotis), suggesting their potentially pleiotropic roles in the bat lifespan.”

CNE analysis revealed a total of 437,414 CNEs across all evaluated species, 20,231 of which displayed accelerated evolution in MPI. ATAC-seq assays refined these results and highlighted that mutations in CNE143336, a potential regulatory element, could result in substantial transformation resistance via HEK 293T and NIH 3T3 gene modulation. Finally, cell-derived xenograft models revealed the essential role of COPS5 genes in malignant transformation resistance.

Conclusion

The present study empirically verifies preexisting hypotheses regarding bats’ natural anticancer resistance. It elucidates the mechanisms underpinning MPI’s remarkable anti-malignant-transformation potential using a combination of immunological, transcriptomic, and gene-editing techniques. Study findings highlight the role of gene downregulation and epigenetics as the basis for the natural cancer resistance of some bat species.

It is essential to mention that while MPI was found to outcompete other investigated bats in the current study substantially, this does not invalidate their anticancer potential via other untested mechanistic routes. Identifying additional mechanisms by which these surprisingly long-lived animals combat cancer may allow us to devise new ways for humanity to follow suit.
Journal reference:

Hua, R., Ma, Y., Yang, L., Hao, J., Hua, Q., Shi, L., Yao, X., Zhi, H., & Liu, Z. (2024). Experimental evidence for cancer resistance in a bat species. Nature Communications, 15(1), 1-15, DOI – 10.1038/s41467-024-45767-1, https://www.nature.com/articles/s41467-024-45767-1
[ad_2]

Source link
February 19, 2024
UVA scientists develop new approach to machine learning for identifying heart drug

[ad_1]

University of Virginia scientists have developed a new approach to machine learning – a form of artificial intelligence – to identify drugs that help minimize harmful scarring after a heart attack or other injuries.

Jeff Saucerman, PhD. Image Credit: University of Virginia

The new machine-learning tool has already found a promising candidate to help prevent harmful heart scarring in a way distinct from previous drugs. The UVA researchers say their cutting-edge computer model has the potential to predict and explain the effects of drugs for other diseases as well.

“Many common diseases such as heart disease, metabolic disease and cancer are complex and hard to treat,” said researcher Anders R. Nelson, PhD, a computational biologist and former student in the lab of UVA’s Jeffrey J. Saucerman, PhD. “Machine learning helps us reduce this complexity, identify the most important factors that contribute to disease and better understand how drugs can modify diseased cells.”

On its own, machine learning helps us to identify cell signatures produced by drugs. Bridging machine learning with human learning helped us not only predict drugs against fibrosis [scarring] but also explain how they work. This knowledge is needed to design clinical trials and identify potential side effects.”

Jeffrey J. Saucerman, PhD., UVA’s Department of Biomedical Engineering, a joint program of the School of Medicine and School of Engineering

The power of combining human learning and machine learning

Saucerman and his team combined a computer model based on decades of human knowledge with machine learning to better understand how drugs affect cells called fibroblasts. These cells help repair the heart after injury by producing collagen and contract the wound. But they can also cause harmful scarring, called fibrosis, as part of the repair process. Saucerman and his team wanted to see if a selection of promising drugs would give doctors more ability to prevent scarring and, ultimately, improve patient outcomes.

Previous attempts to identify drugs targeting fibroblasts have focused only on selected aspects of fibroblast behavior, and how these drugs work often remains unclear. This knowledge gap has been a major challenge in developing targeted treatments for heart fibrosis. So Saucerman and his colleagues developed a new approach called “logic-based mechanistic machine learning” that not only predicts drugs but also predicts how they affect fibroblast behaviors.

They began by looking at the effect of 13 promising drugs on human fibroblasts, then used that data to train the machine learning model to predict the drugs’ effects on the cells and how they behave. The model was able to predict a new explanation of how the drug pirfenidone, already approved by the federal Food and Drug Administration for idiopathic pulmonary fibrosis, suppresses contractile fibers inside the fibroblast that stiffen the heart. The model also predicted how another type of contractile fiber could be targeted by the experimental Src inhibitor WH4023, which they experimentally validated with human cardiac fibroblasts.

Additional research is needed to verify the drugs work as intended in animal models and human patients, but the UVA researchers say their research suggests mechanistic machine learning represents a powerful tool for scientists seeking to discover biological cause-and-effect. The new findings, they say, speak to the great potential the technology holds to advance the development of new treatments – not just for heart injury but for many diseases.

“We’re looking forward to testing whether pirfenidone and WH4023 also suppress the fibroblast contraction of scars in preclinical animal models,” Saucerman said. “We hope this provides an example of how machine learning and human learning can work together to not only discover but also understand how new drugs work.”

Findings published

The researchers have published their findings in the scientific journal PNAS, the Proceedings of the National Academy of Sciences. The research team consisted of Nelson, Steven L. Christiansen, Kristen M. Naegle and Saucerman. The scientists have no financial interests in the work.

The research was supported by the National Institutes of Health, grants HL137755, HL007284, HL160665, HL162925 and 1S10OD021723-01A1.

Source:

University of Virginia Health System

Journal reference:

Nelson, A. R., et al. (2024). Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts. Proceedings of the National Academy of Sciences. doi.org/10.1073/pnas.2303513121.

[ad_2]

Source link

February 5, 2024