Category Archives: Gene Medicine

We live in a unique time when for the first time in human history there is a real opportunity to extend our lives dramatically. Recent scientific discoveries and technological breakthroughs that soon will translate into affordable and accessible life-extending tools will let us break the sound barrier of the current known record of 122 years. I am talking about breakthroughs in genetic engineering, regenerative medicine, healthcare hardware, and health data.

Very soon, slowing, reversing, or even ending aging will become a universally accepted ambition within the healthcare community. Technology is converging to make this a certainty. Developments in the understanding and manipulation of our genes and cells, in the development of small-scale health diagnostics, and in the leveraging of data for everything from drug discovery to precision treatment of disease are radically changing how we think about healthcare and aging.

When I speak of the Longevity Revolution, what I really mean is the cumulative effect of multiple breakthroughs currently underway across several fields of science and technology. Together, these parallel developments are forming the beginning of a hockey-stick growth curve that will deliver world-changing outcomes.

Completed in 2003, the Human Genome Project successfully sequenced the entire human genomeall 3 billion nucleotide base pairs representing some 25,000 individual genes. The project, arguably one of the most ambitious scientific undertakings in history, cost billions of dollars and took 13 years to complete. Today, your own genome can be sequenced in as little time as a single afternoon, at a laboratory cost of as little as $200.

The consequences of this feat are nothing short of revolutionary. Gene sequencing allows us to predict many hereditary diseases and the probability of getting cancer. This early benefit of gene sequencing became widely known when Angelina Jolie famously had a preventative double mastectomy after her personal genome sequencing indicated a high vulnerability to breast cancer. Genome sequencing helps scientists and doctors understand and develop treatments for scores of common and rare diseases. Along with advances in artificial intelligence, it helps determine medical treatments precisely tailored to the individual patient.

Longevity scientists have even identified a number of so-called longevity genes that can promise long and healthy lives to those who possess them. Scientists now understand far better than ever before the relationship between genes and aging. And while our genes do not significantly change from birth to death, our epigenomethe system of chemical modifications around our genes that determine how our genes are expresseddoes. The date on your birth certificate, it turns out, is but a single way to determine age. The biological age of your epigenome, many longevity scientists now believe, is far more important.

Best of all, however, science is beginning to offer ways to alter both your genome and epigenome for a healthier, longer life. New technologies like CRISPR-Cas9 and other gene-editing tools are empowering doctors with the extraordinary ability to actually insert, delete, or alter an individuals genes. In the not terribly distant future, we will be able to remove or suppress genes responsible for diseases and insert or amplify genes responsible for long life and health.

Gene editing is just one of the emerging technologies of the genetic revolution: Gene therapy works by effectively providing cells with genes that produce necessary proteins in patients whose own genes cannot produce them. This process is already being applied to a few rare diseases, but it will soon become a common and incredibly effective medical approach. The FDA expects to approve 10 to 20 such therapies by the year 2025.

Another major transformation driving the Longevity Revolution is the field of regenerative medicine. During aging, the bodys systems and tissues break down, as does the bodys ability to repair and replenish itself. For that reason, even those who live very long and healthy lives ultimately succumb to heart failure, immune system decline, muscle atrophy, and other degenerative conditions. In order to achieve our ambition of living to 200, we need a way to restore the body in the same way we repair a car or refurbish a home.

Several promising technologies are now pointing the way to doing just that. While it is still quite early, there are already a few FDA-approved stem cell therapies in the United States targeting very specific conditions. Stem cellscells whose job it is to generate all the cells, tissues, and organs of your bodygradually lose their ability to create new cells as we age. But new therapies, using patients own stem cells, are working to extend the bodys ability to regenerate itself. These therapies hold promise for preserving our vision, cardiac function, joint flexibility, and kidney and liver health; they can also be used to repair spinal injuries and help treat a range of conditions from diabetes to Alzheimers disease. The FDA has approved 10 stem cell treatments, with more likely on the way.

Its one thing to replenish or restore existing tissues and organs using stem cells, but how about growing entirely new organs? As futuristic as that sounds, it is already beginning to happen. Millions of people around the world who are waiting for a new heart, kidney, lung, pancreas, or liver will soon have their own replacement organs made to order through 3D bio-printing, internal bioreactors, or new methods of xenotransplantation, such as using collagen scaffoldings from pig lungs and hearts that are populated with the recipients own human cells.

Even if this generation of new biological organs fails, mechanical solutions will not. Modern bioengineering has successfully restored lost vision and hearing in humans using computer sensors and electrode arrays that send visual and auditory information directly to the brain. A prosthetic arm developed at Johns Hopkins is one of a number of mechanical limbs that not only closely replicate the strength and dexterity of a real arm but also can be controlled directly by the wearers mindjust by thinking about the desired movement. Today, mechanical exoskeletons allow paraplegics to run marathons, while artificial kidneys and mechanical hearts let those with organ failure live on for years beyond what was ever previously thought possible!

The third development underpinning the Longevity Revolution will look more familiar to most: connected devices. You are perhaps already familiar with common wearable health-monitoring devices like the Fitbit, Apple Watch, and ura Ring. These devices empower users to quickly obtain data on ones own health. At the moment, most of these insights are relatively trivial. But the world of small-scale health diagnostics is advancing rapidly. Very soon, wearable, portable, and embeddable devices will radically reduce premature death from diseases like cancer and cardiovascular disease, and in doing so, add years, if not decades, to global life expectancy.

[Photo: BenBella Books]The key to this part of the revolution is early diagnosis. Of the nearly 60 million lives lost around the globe each year, more than 30 million are attributed to conditions that are reversible if caught early. Most of those are noncommunicable diseases like coronary heart disease, stroke, and chronic obstructive pulmonary disease (bronchitis and emphysema). At the moment, once you have gone for your yearly physical exams, stopped smoking, started eating healthy, and refrained from having unprotected sex, avoiding life-threatening disease is a matter that is largely out of your hands. We live in a world of reactive medicine. Most people do not have advanced batteries of diagnostic tests unless theyre experiencing problems. And for a large percentage of the worlds population, who live in poor, rural, and remote areas with little to no access to diagnostic resources, early diagnosis of medical conditions simply isnt an option.

But not for long. Soon, healthcare will move from being reactive to being proactive. The key to this shift will be low-cost, ubiquitous, connected devices that constantly monitor your health. While some of these devices will remain external or wearable, others will be embedded under your skin, swallowed with your breakfast, or remain swimming through your bloodstream at all times. They will constantly monitor your heart rate, your respiration, your temperature, your skin secretions, the contents of your urine and feces, free-floating DNA in your blood that may indicate cancer or other disease, and even the organic contents of your breath. These devices will be connected to each other, to apps that you and your healthcare provider can monitor, and to massive global databases of health knowledge. Before any type of disease has a chance to take a foothold within your body, this armory of diagnostic devices will identify exactly what is going on and provide a precise, custom-made remedy that is ideal just for you.

As a result, the chance of your disease being diagnosed early will become radically unshackled from the limitations of cost, convenience, and medical knowledge. The condition of your body will be maintained as immaculately as a five-star hotel, and almost nobody will die prematurely of preventable disease.

There is one final seismic shift underpinning the Longevity Revolution, and its a real game-changer. Pouring forth from all of these digital diagnostic devices, together with conventional medical records and digitized research results, is a torrent of data so large it is hard for the human mind to even fathom it. This data will soon become grist for the mill of powerful artificial intelligence that will radically reshape every aspect of healthcare as we know it.

Take drug discovery, for instance. In the present day, it takes about 12 years and $2 billion to develop a new pharmaceutical. Researchers must painstakingly test various organic and chemical substances, in myriad combinations, to try to determine the material candidates that have the best chance of executing the desired medical effect. The drugs must be considered for the widest range of possible disease presentations, genetic makeup, and diets of targeted patients, side effects, and drug interactions. There are so many variables that it is little short of miraculous that our scientists have done so much in the field of pharmaceutical development on their own. But developing drugs and obtaining regulatory approval is a long and cash-intensive process. The result is expensive drugs that largely ignore rarer conditions.

AI and data change that reality. Computer models now look at massive databases of patient genes, symptoms, disease species, and millions of eligible compounds to quickly determine which material candidates have the greatest chance of success, for which conditions, and according to what dose and administration. In addition to major investments by Big Pharma, there are currently hundreds of startups working to implement the use of AI to radically reshape drug discovery, just as we saw happen in the race to develop COVID-19 vaccines. The impact that this use of AI and data will have on treating or even eliminating life-threatening diseases cannot be overstated.

But that is not the only way that artificial intelligence is set to disrupt healthcare and help set the Longevity Revolution in motion. It will also form the foundation of precision medicinethe practice of custom-tailoring health treatments to the specific, personal characteristics of the individual.

Today, healthcare largely follows a one-size-fits-all practice. But each of us has a very unique set of personal characteristics, including our genes, microbiome, blood type, age, gender, size, and so on. AI will soon be able to access and analyze enormous aggregations of patient data pulled together from medical records, personal diagnostic devices, research studies, and other sources to deliver highly accurate predictions, diagnoses, and treatments, custom-tailored to the individual. As a result, healthcare will increasingly penetrate remote areas, becoming accessible to billions of people who today lack adequate access to medical care.

I predict that the development of AI in healthcare will change how we live longer, healthier lives as radically as the introduction of personal computers and the internet changed how we work, shop, and interact. Artificial intelligence will eliminate misdiagnosis; detect cancer, blood disease, diabetes, and other killers as early as possible; radically accelerate researchers understanding of aging and disease; and reestablish doctors as holistic care providers who actually have time for their patients. In as little as 10 years time, we will look back at the treatment of aging and disease today as quite naive.

The Longevity Revolution lives not in the realm of science fiction but in the reality of academic research laboratories and commercial technology R&D centers. The idea of aging as a fixed and immutable quality of life that we have no influence upon is ready to be tossed into the dustbin of history.

Sergey Young is a renowned VC, longevity visionary, and founder of the $100 million Longevity Vision Fund. This is an adapted excerpt from The Science and Technology of Growing Young, with permission by BenBella Books.

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These 4 tech breakthroughs could help end aging - Fast Company

Approval provides opportunity to treat the underlying cause of CF earlier than ever before in Canada

TORONTO, Aug. 25, 2021 /CNW/ - Vertex Pharmaceuticals Incorporated (Canada) (Nasdaq: VRTX) today announced that Health Canada has granted Marketing Authorization for PrKALYDECO (ivacaftor) for use in children with cystic fibrosis (CF) as young as four months of age who have at least one of the following gating mutations in their cystic fibrosis transmembrane conductance regulator (CFTR) gene: G551D, G1244E, G1349D, G178R, G551S, S1251N, S1255P, S549N or S549R.

Vertex Pharmaceuticals Incorporated (Canada) Logo (CNW Group/Vertex Pharmaceuticals Incorporated (Canada))

"With today's approval, children in Canada as young as 4 months now have a medicine to treat the underlying cause of their disease," said Nia Tatsis, Executive Vice President and Chief Regulatory and Quality Officer, Vertex Pharmaceuticals. "This is another step in our goal to develop medicines to treat people living with CF as early in life as possible."

The label update is based on data from a cohort in the 24-week Phase 3 open-label safety study (ARRIVAL) consisting of six children with CF ages four months to less than six months who have eligible gating mutations.

PrKALYDECO (ivacaftor) is now approved for additional eligible patients in Canada, and Vertex will work with payers to secure access for this new patient population.

About Cystic Fibrosis

Cystic fibrosis (CF) is a rare, life-shortening genetic disease affecting more than 80,000 people globally. CF is a progressive, multi-system disease that affects the lungs, liver, GI tract, sinuses, sweat glands, pancreas and reproductive tract. CF is caused by a defective and/or missing CFTR protein resulting from certain mutations in the CFTR gene. Children must inherit two defective CFTR genes one from each parent to have CF. While there are many different types of CFTR mutations that can cause the disease, the vast majority of all people with CF have at least one F508del mutation. These mutations, which can be determined by a genetic test, or genotyping test, lead to CF by creating non-working and/or too few CFTR proteins at the cell surface. The defective function and/or absence of CFTR protein results in poor flow of salt and water into and out of the cells in a number of organs. In the lungs, this leads to the buildup of abnormally thick, sticky mucus that can cause chronic lung infections and progressive lung damage in many patients that eventually leads to death. The median age of death is in the early 30s.

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About KALYDECO (ivacaftor)

Ivacaftor is the first medicine to treat the underlying cause of CF in people with specific mutations in the CFTR gene. Known as a CFTR potentiator, ivacaftor is an oral medicine designed to keep CFTR proteins at the cell surface open longer to improve the transport of salt and water across the cell membrane, which helps hydrate and clear mucus from the airways.

About Vertex

Vertex is a global biotechnology company that invests in scientific innovation to create transformative medicines for people with serious diseases. The company has multiple approved medicines that treat the underlying cause of cystic fibrosis (CF) a rare, life-threatening genetic disease and has several ongoing clinical and research programs in CF. Beyond CF, Vertex has a robust pipeline of investigational small molecule medicines in other serious diseases where it has deep insight into causal human biology, including pain, alpha-1 antitrypsin deficiency and APOL1-mediated kidney diseases. In addition, Vertex has a rapidly expanding pipeline of cell and genetic therapies for diseases such as sickle cell disease, beta thalassemia, Duchenne muscular dystrophy and type 1 diabetes mellitus.

Founded in 1989 in Cambridge, Mass., Vertex's global headquarters is now located in Boston's Innovation District and its international headquarters is in London. Additionally, the company has research and development sites and commercial offices in North America, Europe, Australia and Latin America. Vertex is consistently recognized as one of the industry's top places to work, including 11 consecutive years on Science magazine's Top Employers list and a best place to work for LGBTQ equality by the Human Rights Campaign.

Special Note Regarding Forward-Looking Statements

This press release contains forward-looking statements as defined in the Private Securities Litigation Reform Act of 1995, including, without limitation, statements made by Nia Tatsis in this press release, and statements regarding the availability of KALYDECO to additional eligible patients in Canada and Vertex's work with payers to secure access for the new patient population. While Vertex believes the forward-looking statements contained in this press release are accurate, these forward-looking statements represent the company's beliefs only as of the date of this press release and there are a number of risks and uncertainties that could cause actual events or results to differ materially from those expressed or implied by such forward-looking statements. Those risks and uncertainties include, among other things, that data from the company's development programs may not support registration or further development of its compounds due to safety, efficacy or other reasons, and other risks listed under the heading "Risk Factors" in Vertex's most recent annual report and subsequent quarterly reports filed with the Securities and Exchange Commission at http://www.sec.gov and available through the company's website at http://www.vrtx.com. You should not place undue reliance on these statements. Vertex disclaims any obligation to update the information contained in this press release as new information becomes available.

(VRTX-GEN)

Vertex Pharmaceuticals Incorporated

SOURCE Vertex Pharmaceuticals Incorporated (Canada)

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Health Canada Grants Marketing Authorization for KALYDECO (ivacaftor) as First and Only CFTR Modulator to Treat Eligible Infants With CF as Early as...

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Chronic kidney disease (CKD) has a global prevalence of 816%, with serious morbidity and mortality.1 CKD is a direct risk factor for cardiovascular diseases, end-stage renal disease (ESRD)/CRF, and mortality.2 While replacement therapy with regular dialysis represents a temporary solution, renal transplantation is the permanent solution.3 Anaemia is one of the most important CRF complications, which develops early and worsens during the long-term progression of the disease.4 Coresh et al showed the association between lower Hb levels, the severity of anaemia and kidney function reduction.5 Erythropoietin (Epo), iron therapy, and continuous patient response monitoring provide a good tool for treating CKD-associated anaemia6 that helps to minimize transfusions and improve CKD patient survival.7 Although the response to rHuEpo is mostly good, resistance to Epo therapy among these cases ranges from 10% to 20%.8

Many factors may affect patients responses to replacement therapy with rHuEPO, including genetic factors, eg, ACE gene polymorphism that has an important impact on hematopoiesis. ACE gene is located at 17q23. It contains 26 exons and 25 introns.9 It has several single-nucleotide polymorphisms (SNPs). ACE G2350A (rs4343) SNP is located in exon 17 of the ACE gene and results in silent Thr 776 Thr (NP_000780.1) change. ACE gene SNPs may affect the patients response to Epo and could be useful genetic markers in assessing the required dose of Epo in such patients.10 ACE SNPs effect on CKD response to Epo therapy was evaluated with conflicting results. Varagunam et al reported a predictive role for it in determining Epo dosage in continuous ambulatory peritoneal dialysis English patients,11 while in another study in Korean HD patients, it was found to be associated with Epo resistance.10 ACE G2350A (RS4343) was selected for the present study based on a genome-wide-analysis study that reported the ACE G2350A (RS4343) is a good predictor of ACE activity12 due to the absence of wide genomic mapping in Arabian Countries, so our hypothesis that it may affect HD patients response to rHuEPO.

Although it was investigated concerning other clinical conditions, to the best of our knowledge, none of the international reports studied the effect of ACE G2350A (RS4343) gene polymorphisms on haematological markers of response to rHuEpo in CRF patients on HD. The current study aims to study the effect of ACE G2350A (RS4343) I/D gene polymorphisms on the response to rHuEpo, anaemia biomarkers, ACE content, inflammatory biomarkers, serum Epo and soluble Epo receptor (sEpoR) among CRF patients on HD.

Observational cross-sectional study.

Nephrology department and Biochemistry and molecular biology department, faculty of medicine, Cairo University.

Our cross-sectional study enrolled 256 CRF patients on HD for six months receiving rHuEpo therapy. They included 162 males and 103 females and aged 51.3 11.9 years. They were recruited from the nephrology unit, Internal Medicine Department, Cairo University, Cairo, Egypt, from April 2019 to June 2020. Matching 160 normal healthy control subjects were recruited from those accompanying outpatients and comprised 122 males and 38 females ageing 36.1 12.8 years (Table 1). Each participant had a five-minute interview to discuss the current studys objectives and aims before signing the informed consent and enrollment.

Table 1 General Characteristics and Laboratories of HD Patients versus Controls

Patients excluded from the study if age 18 years, acute renal failure, non-CKD-related anaemia, recent blood transfusion within the previous three months, a history of hepatitis B (HBV) or C (HCV) or HIV or other active acute or chronic infections, decompensated liver cirrhosis, pregnancy, and malignancy.

10 mL peripheral venous blood was collected on heparin. The recovered plasma by centrifugation (1000 x g for 10 min at 4 C) was aliquot stored at 40 C till used for assessment of ferritin, Transferrin (TF), soluble transferrin receptor (sTfR), EPO, sEpoR, ACE, and cytokines (IL-1, IL-6, and IL-10) content, iron workup (iron and total iron-binding capacity; TIBC). Iron (g/dL) and TIBC (g/dL) were assayed using colorimetric kits (Stanbio Laboratory, Boerne, TX, USA). Transferrin saturation (%) was calculated from iron and TIBC. Plasma proteins and cytokines were assayed using specific quantitative commercially available ELISA kits as instructed; ferritin in ng/mL and sTfR in nmol/L (Diagnostic Automation/Cortez Diagnostics Inc, CA, USA; cat#1601-16 and 3126-15), TF in mg/dL (Abcam, Cambridge, MA, USA, USA cat#ab187391), ACE in ng/mL and sEpoR in ng/mL (MyBioSource, Inc., San Diego, CA, USA; cat#MBS494753 and MBS702997), IL-1, IL-6, and IL-10 in pg/mL (RayBiotech, Inc., Peachtree Corners, GA, USA; cat# ELH-IL1b, ELH-IL6, and ELH-IL10), and Epo in mIU/mL (BioVision, Inc., Milpitas, CA, USA; cat# E4720-100). An aliquot of whole blood was also used to assess Hb, TLC count using a cell counter (Sysmex XT-4000i Automated Haematology Analyzer Lincolnshire, IL, USA). Hb level was measured in the 6th month three times, one week apart, the mean of these three readings was recorded. Half of the whole blood sample collected was used for genomic DNA extraction and real-time PCR analysis of ACE genes polymorphism.

Total DNA was isolated from whole blood mononuclear cells (MNC) using the extraction kit (Zymo Research, Irvine, CA, USA; cat# D302 Quick-DNA Microprep Kit) instructed. The DNA purity (A260/A280 ratio) and concentration were assessed spectrophotometrically (dual-wavelength Beckman, Spectrophotometer, USA). GAPDH house-keeping gene was assessed in all PCR reactions as an internal control and for DNA integrity. The extracted and purified DNA samples were stored at 80 C till used. ACE polymorphism genotyping and allelic discrimination was assessed using TaqMan Analysis. DNA was genotyped for ACE G/A at rs4343. PCRs were carried out in reaction volumes of 25 L containing 50 ng DNA, 10 L TaqMan Universal PCR Master Mix (Applied Biosystems, ThermoFisher Scientific Inc., Waltham, MA, USA) with the passive reference ROX (Perkin Elmer), 280 nmol/L of each primer and 200 nmol/L VIC-labeled probes for ACE G > A. Primers and minor groove binder probes were synthesized by Applied Biosystems. The primer sequence was forward: 5-GTGAGCTAAGGGCTGGA-3 and reverse: 5-CCAGCCCTCCCATGCCCATAA-3. PCR thermal cycler conditions included an initial incubation at 50 C for 2 minutes, 95 C for 10 minutes, followed by 35 cycles of 15 seconds at 92 C and 1 minute at 6062 C. Allele discrimination was accomplished by running endpoint detection using the StepOne and SDS 2.0 software. ACE AA = ACE Insertion/Insertion (I/I), ACE GA = ACE Insertion/Deletion (I/D) while ACE GG = ACE Deletion/Deletion (D/D).

Data were collected, tabulated, and analyzed using SPSS version 21 (IBM SPSS Statistics for Windows, Armonk, NY: IBM Corp). Deviation of genotype frequencies of the studied group of patients from Hardy-Weinberg equilibrium (HWE) was assessed by Chi-squared test with one degree of freedom (df) using the Michael H. Courts (20052008) calculator.13 If P 0.05, then the population is in HWE. For categorical data like gender was presented as frequency and percentage. Scale data like age, haematological parameters were presented as mean Standard Error of Mean (SEM). ShapiroWilk test was applied to determine the distribution of data. Chi-square test/ Fischer exact test was applied to measure the difference among categories. Independent samples t-test was used to measure the mean difference across two categories. Levenes test was applied to ascertain equal variance among the groups. One-way ANOVA with LSD posthoc analysis was applied to determine the difference in scale data among more than two categories. Correlations between ACE level and haematological parameters were using Pearsons correlation coefficient. The stepwise regression test was used to determine the independent parameters that may affect Hb or Hct values. A p-value < 0.05 was considered significant.

The current study protocol was approved by the Bioethics Committee, Medical College, Cairo University (Approval Number CU III F 40 20) and conducted following the Helsinki declaration.

Comparing HD patients vs healthy controls showed significant differences in plasma potassium, urea, creatinine, iron, TIBC, % TF Saturation, TF, sTfR, Hb, Hct, TLC, platelets count IL-6, IL-10 and IL-1, EPO, ACE and sEpoR (Table 1).

The prevalence of ACE G2350A (rs4343) I/D genotype among HD patients and healthy controls showed that the I/D genotype is the most prevalent while the I/I genotype is the least one. ACE G2350A (rs4343) I/D genotype distribution showed a significant difference in the gene allele distribution between HD patients compared to normal controls: I/D (n = 174 vs 85), I/I (n = 41 vs 6) and D/D (n = 50 vs 69) (p = 0.001). D allele is the most prevalent one either in HD patients (0.52) or among the control group (0.7) (Table 2).

Table 2 Patients and Control Group ACE Rs4343 Genotype and Allele Distributions

The mean Hb was highest in D/D genotype patients (11.120.19), followed by I/I (11.110.2) n I/D (10.470.1).

The effect of ACE G2350A (rs4343) genotypes on different parameters among CRF patients was evaluated using one-way ANOVA; a significant difference between the three categories was found, F= 5.9, P=0.003. Differences were significant between I/I and I/D genotype (mean difference=.63, P = 0.012), D/D and I/D genotype (mean difference =.65, P = 0.005). no significant difference was noted between I/I and D/D (P=0.956) Table 3.

Table 3 Comparison of Hb & Serum Iron in Different HD Patient Genotypes of ACE Gene Rs4343

The mean serum iron was highest in I/D genotype patients (44.53 .87), followed by I/I (40.951.3 n DD (40.61.05). A one-way ANOVA found a significant difference between three categories, F= 4.062, P=0.018. Differences were significant between I/D and II (mean difference=3.58. P =0.045), I/D and D/D (mean difference=3.93, P =0.018). I/I and D/D had not shown a significant difference (P= 0.871) Table 3.

There were insignificant differences among patients with I/I, D/D, or I/D genotypes regarding TLC (Figure 1A) or the inflammatory biomarkers (IL-6, IL-10, and IL-1) (Figure 1B).

Figure 1 Comparison of WBC (A), IL6 & IL10 & IL1 (B) regarding the ACE G2350A (rs4343) genotypes. Data presented as mean SEM. Evaluated by ANOVA test followed by LSD as a post hoc.

Figure 2 Comparison of Transferrin Saturation or sTfR (soluble transferrin receptor) (A), TIBC (Total Iron Binding Capacity), ferritin, and Transferrin (B) regarding the ACE G2350A (rs4343) genotypes. Data presented as mean SEM. Evaluated by ANOVA test followed by LSD as a post hoc.

There were insignificant differences among patients with I/I, D/D, or I/D genotypes regarding % TF Saturation and sTfR (Figure 2A), TIBC, Ferritin, or TF level (Figure 2B).

Figure 3 Comparison of Epo (erythropoietin), ACE (angiotensin-converting enzyme) and sEpoR (Soluble erythropoietin receptors) regarding the ACE G2350A (rs4343) genotypes. Data presented as mean SEM. Evaluated by ANOVA test followed by LSD as a post hoc.

The effect of ACE G2350A (rs4343) genotypes on levels of ACE, EPO, and sEpoR levels was evaluated among CRF patients. Our results showed insignificant differences between patients with different genotypes in that regard (Figure 3).

The D allele is the most prevalent allele among patients in the current study (Table 2). Analysis of the genotype correlation in a recessive mode of inheritance of the risk of D allele between Non-DD (II+ID) vs (DD) was done using an independent t-test. Our results showed a significant difference between the two groups regarding iron status (43.9.7, 40.61.1, respectively, F: 6.946, t: 2.529, CI: 0.7019:5.8004, P=0.013) and Hb level (10.6.1, 11.1.19, respectively, F: 0.261, t: 2.308, CI: 0.9797:0.0776, P=0.013) (Table 4).

Table 4 Comparison of Different Parameters Between Non-DD (ID+II) and DD Genotype Among HD Patients

Using Pearsons correlation coefficient, the correlation between the ACE level and haematological parameters among HD patients showed a significant positive correlation between the ACE level and Epo (r: 0.244, P=0.0001) and a significant negative correlation between the ACE level and HCT (r: 0.131, P=0.033) (Table 5).

Table 5 Correlations Between ACE Level and Haematological Parameters Using Pearsons Correlation Coefficient

Linear regression analysis revealed that among all parameters tested, ACE G2350A (rs4343) (R.194, P=0.001), TLC (R 0.282, P=0.001), and sEpoR (R 0.312, P=0.024) were independent predictors of Hb level (Table 6). While the ACE content (R. 0.292, P= 0.017), TLC (R. 0.255, P=0.015), and iron (R 0.209, P=0.001) were independent predictors of the Hct level (Table 7).

Table 6 Hb Stepwise Regression Test

Table 7 HCT Stepwise Regression Test

The current study is the first report that studied the effect of ACE G2350A (rs4343) gene polymorphism on the haematological indicators of response to rHuEpo therapy. It is well-established that genetic factors play an essential role in determining the efficacy and response to drug treatment.14 Pharmacogenomics analyses such relationships towards the personalization of medicine. Our lab showed the importance of such an approach in predicting the patients response to different drug therapy.15,16

The present study showed that HD patients with the ACE G2350A (rs4343) D/D and I/I genotype respond better to rHuEpo therapy than those with the I/D genotype as evidenced by the higher Hb level among the former group. This higher Hb level among D/D and I/I genotypes were not related to iron level. Our results showed that patients with the I/D allele had higher iron than patients with each of the D/D and I/I genotypes, despite the lower Hb level of the I/D allele holders. The better Hb response was recently partially reasoned to higher plasma angiotensin II (Ang II) levels in D/D and I/D genotypes compared to the II genotype.17

Ang II is the main effector member of the renin-angiotensin system acting through the AT1 receptor and is generated from Ang. I by an ACE-induced proteolytic cleavage.18 The Renin-angiotensin system plays a vital role in hematopoiesis and other diseases.19,20 However, the exact mechanism by which ACE may affect erythropoiesis and Hb level is still not well elucidated. Among the other plausible explanations is ACE inhibition of Ang IIinduced Epo release and prevention of the induction of pluripotent hematopoietic stem cells.21 ACE directs stem cell differentiation to erythroid progenitors synthesis.22 ACE may affect the Ang II level, directly increasing erythroid progenitors in vitro proliferation.23

Savin et al showed that the ACE D/D genotype is associated with higher Hb levels.24 Patients with the D/D genotype were shown to require less Epo dose than the I/I genotype.11

In a study that included 112 ambulatory peritoneal dialysis patients, Sharples et al25 showed that the ACE DD genotype requires less rHuEpo than other ACE genotypes, I/I or I/D. This result seems to be in line with our conclusion, albeit we could not identify the exact ACE SNPs that Sharples and his colleagues had examined. Similarly, Hatano et al26 showed that HD patients with D/D-allele require low rHuEPO.

The ACE rs4646994 D/D genotype was associated with a poor response to rHuEpo in HD Korean patients, suggesting that it could be a useful genetic tool in predicting Epo requirement and responsiveness in HD patients.10 Kiss et al,27 working on Hungarian and Al-Radeef et al,28 working on Iraqi HD patients, reported that ACE polymorphism had a non-significant effect on the Hb level. These variations may arise from the exact SNPs tested; we explored the ACE G2350A (rs4343) effect while they examined rs1799752 and rs4646994, respectively. Also, the small sample size of these studies compared to ours might have affected their conclusions.

Our results showed a higher iron store among the heterozygous ID genotype than II or DD genotype patients assuming a heterozygous advantage for the ACE G2350A (rs4343) ID genotype among HD patients included in the present study.

Heterozygote advantage or overdominant refers to better fitness of heterozygous genotype patients over both homozygous. It firstly appeared in 1922 to maintain polymorphism stability.29 Major histocompatibility complex (MHC) gene represent one of the prominent examples for the heterozygote advantage, in which MHC heterozygotes genetic diversity is abundant. Heterozygote genotype patients have better recognition of pathogen antigen and resist infections effectively than homozygous.30,31 Heterozygote advantage provides a protective effect against malaria for the sickle-cell anaemia allele carriers.32

Recently, A genome-wide association study revealed that heterozygous individuals were significantly healthy-aged compared to other individuals with other genotypes. Moreover, in the same age group population, a 10-year higher survival was associated with individuals with higher heterozygosity rates; the association is more likely to be explained by heterozygote advantage.33 Previous observations noted heterozygous advantages on ACE genotype patients among cardiovascular diseases; because of high linkage disequilibrium (LD) between the polymorphisms, ACE haplotypes needed to be determined in different populations with different evolutionary histories search for additional ancestral breakpoints. The phenotypes complexity also includes the possibility of multiple interactions between genes or genes and environmental factors. The high frequency of I/D, ie, 56.61%, could be because of heterozygote advantages against the two homozygotes D/D and I/I in cardiovascular diseases9 and kidney diseases; individuals with I/D genotype have the least levels of ACE. The DD genotype has the highest levels, followed by I/I34 or having lower plasma ACE levels,35 although these studies may differ from our study in its design, ethnicity, and allele distributions.

A 287-bp insertion/deletion (I/D) polymorphism in intron 16 of the ACE gene (17q22-q24, 26 exons, and 25 introns) in humans may control serum ACE levels. Many SNPs in linkage disequilibrium (LD) with the I/D polymorphism, including T5941C, A262T, T93C, T1237C, C4656T, and A11860G (rs 4343; exon 16),36,37 are known to influence serum ACE.38

Furthermore, rs1799752 is one of four SNPs that may be the most well-studied ACE SNP. It is an insertion/deletion of an Alu repetitive element in an ACE genes intron rather than a single nucleotide polymorphism.

ACE G2350A (rs4343) gene polymorphism is associated with increased ACE enzyme activity in physiological and pathological states.39 It increases ACE levels in subjects with a high-saturated-fat diet that enhances diet-dependent hypertension.40

Our data showed insignificant differences among the tested three ACE G2350A (rs4343) I/I, I/D, and D/D genotypes regarding the circulating ACE protein content. On the contrary, Mizuiri et al and Elshamaa et al demonstrated an opposite conclusion. ACE I/D genotype is associated with renal ACE gene expression in healthy Japanese subjects41 and plasma and tissue ACE levels.42 Nand et al showed D allele positively affects ACE serum level.43

Endogenous or rHuEpo binds to EPOr resulting in stimulation of erythropoiesis.44 sEpoR is generated from mRNA alternative splicing, and since it lacks the transmembrane domain, it is released into extracellular fluids. sEpoR buffers rHuEpo because of its high affinity to EPO; therefore, it acts as a potent antagonist to EPO, resulting in decreased response to rHuEpo treatment. sEpoR high level was correlated to a high need for rHuEpo dose.45,46

In the current work, there was an insignificant difference between ACE G2350A (rs4343) I/I, I/D, or D/D genotypes regarding plasma Epo and sEpoR content in the present study. This notion contradicts the finding of Al-Radeef et al, who showed that another rs1799752 I/D and D/D genotypes had a higher serum Epo level compared to the I/I genotype.28

Our patients were free of active infection, and the measured proinflammatory cytokine levels, IL-6, IL-1, and IL-10, were insignificant differences among the three ACE G2350A (rs4343) genotypes; I/I, I/D, or DD.

Increases in the inflammatory mediator, such as IL-6 and TNF-, lead to increases in the sEpoR level that would hinder erythropoiesis.46 sEpoR stabilizes proinflammatory cytokine ligand and modulates cytokine interaction to its membrane-bound receptor, leading to variation in its concentration.47 Inflammatory cytokines accompanying CRF and HD decrease rHuEpo efficacy. TNF-, IL-1, and IL-6 induce resistance against rHuEpo in erythroid progenitor cells reducing iron release from the reticuloendothelial system and decreasing Hb production.48,49 Betjes et al reported a lack of response to rHuEpo among CKD patients with cytomegalovirus infection mainly due to IFN- and TNF- production.50

Although our HD patients showed higher levels of % TF saturation and sTfR, TIBC, Ferritin, or TF, there were insignificant differences among patients with I/I, D/D, and I/D genotypes regarding these parameters.

Various tissues obtain their iron need via TF binding to its receptor, endocytosis of the complex, and iron download.51,52 The expression rate of the cell surface TF receptor is directly proportional to its iron need.53 The transmembrane glycoprotein TF receptor is formed of two disulfide-linked monomers; each polypeptide subunit comprises three major domains: a large C-terminal extracellular domain and a transmembrane and an N-terminal cytoplasmic domain. sTfR is the cleaved extracellular domain of the high-affinity iron-sensor TF receptor released soluble in extracellular fluids. Circulating levels of sTfR reflect the number of cells with receptors (erythropoietic activity) and the receptor density on cells (tissue iron status).54 Ferritin is used for diagnosing iron deficiency anaemia, but it could be falsely elevated in inflammation giving the erroneous impression of normal iron stores.55 sTfR is insensitive to inflammatory states and inflammatory biomarkers. It could detect anaemia even in subjects with the inflammatory condition; moreover, it could differentiate between anaemia due to iron deficiency or chronic diseases.56

Finally, we tested for independent factors that may affect the patients response to rHuEPO. Among all parameters tested, ACE protein level, TLC, and sEpoR were the independent predictors of Hb level. Simultaneously, ACE protein content, TLC, and iron are the independent predictors for the Hct level.

Previous works measured Hb level at the beginning, 3rd, and 6th months of treatment with rHuEpo [24, 28]. In the present study, we measured the Hb level after six months of the treatment with rHuEpo to allow more precision and avoid fluctuation of patient response to treatment. We took the mean of the three Hb levels in the 6th month. We could not retrieve accurate data considering the use of ACE inhibitors (ACEIs) or ARBs among our patients. We measured circulating ACE level as a protein rather than an activity that revealed insignificant differences among the three genotypes assessed to avoid any related confusion. We did not evaluate angiotensin II (Ang II) level in the current study and iron intake status, but we estimate Hct, iron, ferritin, TF, % TF saturation, sTfR, and TIBC. Many other ACE gene SNPs may affect the HD patients response to rHuEPOs as rs1799752, rs429, and rs4341 which may be in linkage disequilibrium with studied rs4343; however, the only studied here is the ACE G2350A (rs4343). These limitations of the current study are highly acknowledged and will be considered in our future studies.

Patients with either ACE G2350A (rs4343) I/I or D/D genotype showed better response to rHuEpo than those with I/D genotype. ACE protein content, TLC, and sEpoR may represent independent predictors for the HD patients response to rHuEPOs. Screening for ACE G2350A (rs4343) gene polymorphisms in the HD patients on HD before rHuEpo administration may predict patients response.

This project was funded by The Deanship for Scientific Research, Jouf University, Sakaka, Saudi Arabia (Grant # 40/345). The authors express their deepest thanks to Prof. Dr Dina Sabry (The Molecular Biology Lab, Faculty of Medicine, Cairo University, Cairo, Egypt) for facilitating the gene analysis and biochemical investigations.

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

The authors stated that they have no conflicts of interest for this work.

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19. Vlahakos DV, Marathias KP, Madias NE. The role of the renin-angiotensin system in the regulation of erythropoiesis. Am J Kidney Dis. 2010;56(3):558565. doi:10.1053/j.ajkd.2009.12.042

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23. Mrug M, Stopka T, Julian BA, Prchal JF, Prchal JT. Angiotensin II stimulates proliferation of normal early erythroid progenitors. J Clin Invest. 1997;100(9):23102314. doi:10.1172/JCI119769

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26. Hatano M, Yoshida T, Mimuro T, et al. [The effects of ACE inhibitor treatment and ACE gene polymorphism on erythropoiesis in chronic hemodialysis patients]. Nihon Jinzo Gakkai Shi. 2000;42(8):632639. Japanese.

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The rest is here:
Haematological Indicators of Response to Erythropoietin Therapy in Chr | PGPM - Dove Medical Press

Introduction

Colorectal cancer (CRC) has become the predominant cancer worldwide with more than 1.8 million new cases diagnosed annually.1,2 Furthermore, the five-year survival rate for patients with advanced-stage metastatic cancer is approximately 10%.1 Like other cancers, CRC is considered to be a heterogeneous disease in which gene aberrations, cellular context, and environmental influences concur with tumor initiation, progression, and metastasis.3 Despite advances in laparoscopic and robotic surgery, more aggressive resection of metastatic disease, radiotherapy, as well as neoadjuvant and palliative chemotherapies, the new treatments had an insignificant effect on long-term survival.4 Thus, it is critical to make a thorough inquiry into the underlying biological mechanism of the occurrences and metastases of cancers associated with prognosis so as to discover novel biomarkers for target therapies and prognosis predictions. Although accumulating evidence has demonstrated that multiple genes and cellular pathways participate in the occurrence and development of CRC,5,6 a paucity of knowledge regarding the potential precise molecules and potential mechanisms underlying CRC progression currently limits the ability to treat this disease.

Bioinformatics analyses, including the analysis of gene interaction networks, microarray expression profiles, and gene annotations are being utilized as powerful tools for identifying potential diagnostic and treatment-relevant biomarkers of cancers.7,8 For example, by analyzing data from the Gene Expression Omnibus (GEO) database, Cao et al9 identified five genes as potential biomarkers and therapeutic targets for gastric cancer. In addition, by analyzing data from GEO and The Cancer Genome Atlas (TCGA), Zhu et al found that high expression of cyclin-dependent kinase 1 (CDK1) is a prognostic factor for hepatocellular carcinoma (HCC), making it a potential therapeutic target and biomarker for the diagnosis of HCC.10 In particular, the method of integrated bioinformatics analysis can be used to overcome inaccuracies in sequencing arising from small sample sizes. Circular RNAs (circRNAs) are a novel class of endogenous non-coding RNAs that form a covalently closed-continuous loop by back-splicing events via exon or intron circularization.11 Due to the development of high-throughput sequencing, researchers have discovered that thousands of circRNAs are involved in the progression of oncogenesis, invasion, and metastasis by playing the role of sponges to microRNAs (miRNAs).12 For instance, Wang et al13 verified that circDLGAP4 regulated lung cancer cell biological processes by sponging miRNA-143 to regulate CDK1 expression and circDLGAP4 may serve as a potential biomarker for the diagnosis and treatment of lung cancer. However, at present, most studies involving circRNAs have been limited to the sequencing of a few samples or exploring the biological function of single circRNAs. To the best of our knowledge, currently, no researchers have used integrated analysis to investigate CRC-related circRNAs.

In this study, differentially expressed mRNAs (DEmRNAs) between human CRC tissues and adjacent non-tumor tissues were identified via analysis of public TCGA datasets. Next, to explore the roles of these DEmRNAs, functional enrichment analyses and pathway enrichment analyses were performed. Then, proteinprotein interaction (PPI) networks were successfully constructed. The key genes and significant modules in the networks were identified. KaplanMeier analysis was performed to evaluate the prognostic value of these hub genes. Furthermore, three additional circRNA expression profiles were analyzed to identify differentially expressed circRNAs (DEcircRNAs) and differentially expressed miRNAs (DEmiRNAs) between CRC and adjacent non-tumor tissues. Finally, circRNAsmiRNAsmRNA ceRNAs network was constructed. The research is expected to help to further elucidate the ceRNAs interactions in CRC and generate insight into the potential biomarkers and targets for the diagnosis, prognosis, and therapy of CRC.

CRC gene expression profile data were downloaded from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and standardized, including 41 normal samples and 473 tumor samples and their clinical information. Previous studies have demonstrated that without adjustment, TCGA-COAD READ data set could be generated by merging samples from TCGA-COAD data set and TCGA-READ data set, since principal components analyses and unsupervised hierarchical clustering showed no significant differences.14,15 CRC miRNA expression profiles from Illumina HiSeqmiRNASeq platforms, including 8 normal samples and 450 tumor samples, were downloaded from TCGA and standardized. In addition, 4 circRNA expression profiles (GSE121895, GSE126094, GSE138589, GSE142837) from Illumina HiSeqRNASeq platforms, including 23 tumor samples and 23 normal samples, were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo) by searching for the term CRC (July 2020), and batch effects were removed using the combat function in the R sva package.16

DEcircRNAs, DEmRNAs, and DEmiRNAs were identified using an R package DESeq2 (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html). |log2FC| >2 and FDR <0.05 were set as the cutoff criteria (FC, fold change; FDR, false discovery rate) based on the BenjaminiHochberg method for DEmRNAs and DEmiRNAs.17 DEcircRNAs were screened by |log2FC| >1 and FDR<0.05. R was used to visualize differential genes. For DEcircRNAs, we used Surrogate Variable Analysis to handle multiple GSE profiles as described above. Volcano maps were plotted based on the volcano map of R.

To identify the biological function of the ceRNAs network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses are widely used for gene annotation terms and pathway enrichment analysis. GO is a widely used tool for annotating genes with functions, especially molecular function (MF), biological pathways (BP), and cellular components (CC). KEGG Enrichment Analysis is a practical resource for the analytical study of gene functions and associated high-level genome functional information. ClusterProfiler package of R was performed to analyze and visualize functional profiles. A P-value < 0.05 was the threshold for significance for GO and KEGG terms.

The PPI network was conducted to analyze the functional interactions between proteins, providing insights into the mechanisms for the development of CRC. The minimum required interaction score is 0.5. The STRING website (https://string-db.org/) was employed to construct the PPI network.

Based on the median expression level of each DEmRNAs, the CRC patients were divided into high and low-expression groups. KaplanMeier analysis and the Log rank test were utilized to paint the survival curves to find the DEmRNAs that were significantly associated with the survival of CRC patients. A P-value < 0.05 was set as the threshold.

DEcircRNAs and DEmRNA matched by DEmiRNAs were retrieved using starBase database.18 Moreover, the prediction results of TargetScan, miRTarBase, and miRDB were integrated by starBase.1921 The candidates searched in three databases were associated with the most important DEmiRNAs. Finally, the circRNAsmiRNAsmRNA ceRNAs network was constructed and visualized using R.

Total RNA was prepared from colonic tissue using an RNA extraction kit (TIANGEN BIOTECH, Beijing, China), according to the manufacturers instructions. The extracted RNA was synthesized to form cDNA using a FastKing one-step kit (TIANGEN BIOTECH, Beijing, China). qRT-PCR was performed using a RealUniversal Color PreMix (SYBR Green) kit (TIANGEN BIOTECH, Beijing, China) to assess the expression of target genes. U6 was used as an internal control for DEmiRNAs. GAPDH was used as internal control for TIMP1. In addition, the relative expression of RNAs was quantified by using the 2Ct method.

Table 1 shows the clinicopathological data of 473 patients with CRC. According to the cutoff threshold, a total of 412 DEmRNAs (including 82 upregulated and 330 downregulated) were screened out between 473 CRC and 41 normal samples with the standard of logFC> 2 and an adjusted P value (adj.P.Val) <0.05 (Figure 1A). Two hundred and sixty DEcircRNAs (including 253 upregulated and 7 downregulated) were altered significantly between 23 CRC and 23 normal samples by log2FC > 1 and an adj.P.Val < 0.05 (Figure 1B). To further establish an circRNAsmiRNAsmRNAs ceRNAs network, we also matched DEmiRNA expression profiles in the 450 CRC and 8 normal samples. As a result, 190 DEmiRNAs reached the inclusion criteria including 82 upregulated and 108 downregulated miRNAs (Figure 1C). The top 10 DEcircRNAs, DEmiRNAs, and DEmRNAs are presented in Table 2.

Table 1 Clinicopathological Characteristics of 473 CRC Patients

Table 2 Top 10 DEcircRNAs, DEmiRNAs and DEGs in Human CRC

Figure 1 The volcano maps of DEGs between CRC samples and normal samples. (A) A total of 412 DEmRNAs including 82 upregulated and 330 downregulated genes. (B) A total of 260 DEcircRNAs including 253 upregulated and 7 downregulated genes. (C) A total of 190 DEmiRNAs including 82 upregulated and 108 downregulated genes. Red represents upregulated genes and green represents downregulated genes.

To further analyze the functional characteristics of DEmRNAs in CRC, GO and KEGG pathway analyses were performed using ClusterProfiler package of R. DEmRNAs were functionally classified into the biological process (BP), cellular component (CC), and molecular function (MF categories). In the BP category, four of the nine most enriched terms were regulation of protein processing, protein activation cascade, regulation of acute inflammatory response and complement activation. In the CC category, the four most enriched terms were extracellular matrix, collagen-containing extracellular matrix, blood microparticle and apical part of cell. In the MF categories, the three most enriched terms were antigen binding, receptor regulator activity and receptor ligand activity (Figure 2A). In addition, almost 16 KEGG pathways were significantly enriched in our analysis. The three most enriched terms were cytokine-cytokine receptor interaction, kineral absorption and steroid hormone biosynthesis (Figure 2B).

Figure 2 Functional enrichment analysis of DEmRNAs. (A) The top 9 enrichment scores in GO enrichment analysis of the DEmRNAs including biological process enrichment analysis, cellular components enrichment analysis, molecular function enrichment analysis. (B) The top 16 enrichment scores in KEGG enrichment analysis of the DEmRNAs.

A total of 412 DEmRNAs (82 upregulated and 330 downregulated) were used to construct the PPI networks, which included 226 nodes and 478 edges. The combined minimum required interaction score >0.5 was considered statistically significant (Figure 3). In addition, the degree distribution of each gene in the PPI network was analyzed, the top five genes [C-X-C chemokine receptor type 8 (CXCL8), TIMP1 (tissue inhibitor of metalloproteinase 1), CXCL1, secreted phosphoprotein 1 (SPP1) and CXCL12] with high connectivity were confirmed as hub genes and next were underwent survival analysis.

Figure 3 The plot of the PPI network of DEmRNAs including 226 nodes and 478 edges by the online database STRING. The combined minimum required interaction score>0.5 was considered statistically significant.

The prognostic values of the five hub genes were assessed in CRC patients using KaplanMeier analysis and Log rank test. The results indicated that CRC patients with high expression of TIMP1 showed worse overall survival (P=0.004). In contrast, the other four hub genes (CXCL8, CXCL1, SPP1, and CXCL12) were not related to the overall survival of CRC patients (P > 0.05) (Figure 4).

Figure 4 Kaplan-Meier survival curves for the top five hub genes including SPP1, CXCL1, TIMP1, CXCL8, and CXCL12. TIMP1 was significantly associated with survival rate of CRC patients.

miRNAs-mRNA interactivity was taken into account, in addition to the circRNAs-miRNAs, to construct an integrated ceRNAs network. Based on the starBase database, which masters the function of transcriptome-wide mircoRNA targeting prediction, we matched 61 DEcircRNAs and 3 DEmiRNAs. To clearly show the interaction in ceRNAs, the regulatory network contained some well-described biomarkers, including, hsa-miR-671-5p, hsa-miR-17-3p, hsa-miR-328-3p and TIMP1. This ceRNAs network is particularly informative in locating potential biomarkers for CRC. For instance, hsa-miR-671-5p interacted with TIMP1 and was mediated by hsa-circ-0002191. hsa-miR-17-3p interacted with TIMP1 and was mediated by has-circ-0023397 (Figure 5).

Figure 5 The ceRNAs network of circRNAs-miRNAs-mRNA in CRC. Blue represents DEcircRNAs; Black represents DEmiRNAs; Red represents DEmRNA.

To identify the authenticity and feasibility of the ceRNAs regulatory network, some vital DEmiRNAs and DEmRNAs are evaluated in colon cancer tissue and normal tissues. We found that TIMP1 is highly expressed in colon cancer tissue compared to normal tissue (P < 0.001). In contrast, the expression levels of hsa-miR-671-5p, hsa-miR-17-3p, and hsa-miR-328-3p were significantly decreased in colon cancer tissue (Figure 6).

Figure 6 The expression levels of DEmRNA and DEmiRNAs in colon cancer patients compared with those of normal samples. (A) The TIMP1 is highly expressed in colon cancer tissue. (BD) The miR-671-5p, miR-17-3p and miR-328-3p is low expression in colon cancer tissue, **P <0.01, and ***P <0.001.

CRC, the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths with notably aggressive biological behavior and poor survival rates, has always drawn close attention from researchers.2 It is crucial to identify reliable therapeutic targets and biomarkers in order to improve the clinical outcome for CRC patients. The ceRNAs hypothesis presents a new pattern of gene expression regulation that cicrRNAs could regulate mRNAs by competing with the corresponding miRNAs.22 Subsequently, benefits from developments in sequencing technology and the applications of bioinformatics confirm the increasingly important biological role in the initiation, progression, and metastasis of tumors.1921 CircRNAs differ from other long non-coding RNAs in the structure, which is characterized by covalently linked 5- and 3-ends. CircRNAs functionally act as miRNAs sponges, RNA-binding protein sponges, and gene expression regulators. Therefore, circRNAs regulate their target genes expression and proteins network in both transcriptional and post-transcriptional patterns.23 Increasingly, clinicians consider that circRNAs-miRNAs-mRNAs ceRNAs networks could provide an integrated view of regulatory crosstalk between these CRC-specific RNA transcripts.24,25

In this study, DEmRNAs were identified between tumor samples and normal control tissues. Then, GO and KEGG analyses were performed to further understand the role of DEmRNAs. The results of GO analyses showed that the DEmRNAs were enriched in regulation of protein processing, protein activation cascade, and acute inflammatory which is confirmed by the knowledge that protein-induced pathology and inflammatory networks underlying CRC are the main cause for tumor development and progression.2628 Furthermore, KEGG analyses showed that cytokinecytokine receptor interaction is a substantial factor in the occurrence of CRC. Cytokines such as TNF- and IL-6 are classically regarded as central players in CRC by driving activation of the NF-B and STAT329. Cytokines including IL-11, IL-17A, and IL-22 have gained attention as facilitators of CRC.29

The top degree hub genes (CXCL8, TIMP1, CXCL1, SPP1 and CXCL12) were presented in the PPI network with DEmRNAs. SPP1, also named Osteopontin, has been proven to be overexpressed in various malignant neoplasms including breast cancer, lung cancer, and gastric cancer.3032 Although Seo et al33 have evaluated the expression of SPP1 in 174 stage II and III CRC specimens and found SPP1 is significantly associated with cell invasion and adherence in CRC, the underlying mechanism was not revealed. Wang et al34 has shown that SPP1 functions as an enhancer of cell growth in hepatocellular carcinoma (HCC) targeted by miR-181c. Further studies have shown that SPP1 promotes the metastasis of CRC by activating epithelial-mesenchymal transition (EMT).35 CXCL8, as a prototypical chemokine, is responsible for the recruitment and activation of neutrophils and granulocytes to the site of inflammation which demonstrated that CXCL8 played a crucial role in facilitating tumor growth and progression in breast cancer, prostate cancer, lung cancer, colorectal carcinoma, and melanoma.36 Phosphorylation of Src-kinases and focal adhesion kinase (FAK) in cancer cells were increased in CXCL8 signaling, which contributed to cell proliferation and chemoresistance.37,38 The level of CXCL1 are elevated in CRC and increased level of CXCL1 are associated with tumor size, advancing stage, and patient survival.39,40 It was reported that CXCL1 could promote tumor growth by inducing angiogenesis and the recruitment of neutrophils into the tumor-associated microenvironment.41,42 CXCL1, the most abundant secreted chemokine by tumor-associated macrophages has been implicated in the promotion of breast cancer growth and metastasis via activating NF-B/SOX4 signaling.43 The similar phenomenon has been observed in human bladder cancer.44 Some researchers have indicated that CXCL1 could increase oncogenes expression in colon cancer, including forkhead box O1 (FOXO1) and transcription factor 4 (TCF4) in CXCL1-treated SW620 cells according to transcriptome analyses.45 CXCL1 is also vital for pre-metastatic niche formation and metastasis in CRC.46 CXCL12 also known as SDF-1 is widely distributed in human tissues and more than 23 different types of cancers.47 Importantly, it has been found that CXCL4 and its ligand CXCL12 are implicated in cell proliferation, angiogenesis, migration, EMT, and tumor metastasis.48 TIMP1 belongs to the tissue inhibitor of the metalloproteinases family which includes TIMP1, TIMP2, TIMP3, and TIMP4.49 In the present study, TIMP1 has been reported to indicate poor prognosis in CRC (P=0.004), which is consistent with the research of Song et al.50 Song et al considers that the expression of TIMP1 was clearly associated with the regional lymph node and distant metastasis. In addition, research by Song et al indicated that TIMP1 was an independent prognostic indicator for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway.50 Moreover, TIMP1 could promote receptor tyrosine kinase c-Kit signaling in CRC, while c-Kit is an important oncogene in CRC and plays a role in cell proliferation and migration.51 For other cancers, TIMP1 inhibited the chemosensitivity of breast cancer cells through the PI3K/AKT/NF-kB pathway.52 TIMP1 is in favor of cell survival in melanoma by activating the 3-phosphoinositide dependent kinase-1 signaling pathway.53 TIMP1/CD63/ERK signaling axis induces the formation of neutrophil extracellular traps and facilitates the development of pancreatic cancer.54 Clinical studies have demonstrated that the elevated level of TIMP1 was associated with poor prognosis in various tumors, such as breast cancer,55 cutaneous melanoma,56 and gastric cancer.57 The elevated plasma level of TIMP1 predicted a reduced response to second-line hormone therapy and low survival in women with metastatic breast cancer.58 Therefore, TIMP1 may be a potential biomarker to predict the prognosis of cancer and play a critical role as a therapeutic target. The TIMP-miRNAs axis has been believed to be a potential therapeutic target against aggressive or drug-resistant variants of human cancers.5961 For instance, angiogenesis and tumor growth were increased when TIMP1 banded to CD63 and stimulated miR-210 accumulation by activating PI3KAKTHIF1 signaling in the lung adenocarcinoma.60 As the hub elements of the ceRNAs network, miRNAs exhibited key roles among different RNA transcripts. In fact, hsa-miR-671-5p have been proven to interact with TIMP1 directly by cross-linking immunoprecipitation.62 However, the interaction between hsa-miR-671-5p and TIMP1 still needs to be verified in the occurrence and progress of CRC. The miR-671-5p had a protective role in gastric cancer by targeting upregulator of cell proliferation.63 Meanwhile, miR-671-5p inhibits EMT by directly downregulating FOXM1 in breast cancer.64 Interestingly, the levels of miR-671-5p are not only increased in colon cancer tissue but also increased cell proliferation, migration, and invasion by targeting tripartite motif containing 67.65 This finding runs against our findings. The same miRNAs can regulate multiple mRNAs molecules and produce different physiological effects. MiR-328-3p was identified in bladder cancer and suppressed cell proliferation, migration, and invasion by targeting integrin subunit alpha 5 as well as by inhibiting EMT and inactivated PI3K/AKT pathway.66 Similar tumor suppression effects were observed in colon cancer.67

The present study identifies a novel ceRNAs network, which implies that TIMP1 is a potential biomarker underlying the development of CRC, providing new insights into survival predictions and therapeutic targets. However, the limitation of the study still needs investigation. Too many circRNAs were chosen in this study, a more advanced approach to narrow the scope of research is needed. The results of the present CRC-related ceRNAs regulatory network are required to be verified by clinical trials and molecular experiments.

The present study identified a novel circRNAs-miRNAs-mRNAceRNAs network and provided candidate prognostic biomarkers for predicting the outcome of patients with CRC. Especially, TIMP1 is a potential indicator underlying the development of CRC. This study provided new insights for the survival predictions and therapeutic targets of CRC.

All the experiments were approved by the Ethics Committee of Tianjin Medical University General Hospital (Tianjin, China).

All authors contributed to data analysis, drafting or revising the article, gave final approval for the version to be published, agreed to the submitted journal, and agreed to be accountable for all aspects of the work. Ya-Fei Qin, Guang-Ming Li and Grace Wang are co-first authors of this paper.

This work was supported by grants to Hao Wang from the National Natural Science Foundation of China (No. 82071802), Tianjin Application Basis and Cutting-Edge Technology Research Grant (No. 14JCZDJC35700), Li Jieshou Intestinal Barrier Research Special Fund (No. LJS_201412), Natural Science Foundation of Tianjin (No. 18JCZDJC35800), and Tianjin Medical University Talent Fund; by a grant to Dejun Kong from Tianjin Research Innovation Project for postgraduate students (No. 2019YJSS184).

The authors declare that they have no competing interests.

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ceRNAs network in the pathophysiological development of CRC | TCRM - Dove Medical Press

Introduction

Klebsiella pneumoniae (K. pneumoniae) is an opportunistic pathogen and the leading cause of healthcare-associated infections.1 Multidrug-resistant (MDR) K. pneumoniae isolates are rapidly spreading, thus limiting the choice of antimicrobial agents for empiric treatment of infections caused by these microorganisms; hence, this is a public health challenge.2

Polymyxins are last-resort antibiotics used to treat infections caused by carbapenem-resistant K. pneumoniae (CRKP).3 The two polymyxins currently in clinical use are polymyxin B and colistin (polymyxin E). They have similar antibacterial activity, but their structures differ by only one amino acid.4

The antibacterial effect of polymyxins on gram-negative bacteria is mainly a two-step mechanism comprising initial binding to and permeabilization of the outer membrane, followed by the destruction of cytoplasmic membrane.5 Notably, with the increase in the clinical use of polymyxins, polymyxin resistance has emerged and is rising rapidly. Polymyxin-resistant K. pneumoniae often spread in different hospital wards, making clinical treatment more difficult.6 The previously reported mechanisms of polymyxin resistance are chromosomally mediated and involve the regulation of two-component regulatory systems (eg, pmrAB, phoPQ, and its negative regulator, mgrB, in the case of K. pneumoniae), leading to the modification of lipid A (phosphoethanolamine or 4-amino-4-arabinose) or in rare cases, the complete loss of the lipopolysaccharide.7

Researchers reported the first plasmid-mediated polymyxin resistance mechanism, mcr-1, in Enterobacteriaceae in China. This warranted immediate worldwide attention, and mcr-1 has since been detected in Enterobacteriaceae from animals, food, and healthy people outside of China, including in Europe and the USA.8 Recently, some countries have also reported that mcr-1 and blaNDM-5 genes coexist in Escherichia coli strains,9,10 which is a serious challenge to treatment efforts.

This study assessed the current status of polymyxin resistance in CRKP isolates and investigated the possible coexistence of mcr-1 and -lactamase genes in K. pneumoniae in Nanchang, China.

From January 2018 to June 2019, a total of 107 nonduplicate CRKP isolates were isolated from hospitalized patients in different clinical departments in a tertiary teaching hospital in Nanchang, China. Different specimens were collected, and the K. pneumoniae isolates were identified using a VITEK-2 automated platform (bioMerieux, Marcy lEtoile, France). E. coli ATCC 25922 was used as a control strain.

The susceptibility of the K. pneumoniae clinical isolates to antimicrobials was determined using gram-negative susceptibility cards (AST-GN-16) on the VITEK system (bioMerieux, Marcy lEtoile, France) following the manufacturers instructions; the results were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) standards.11 The MICs of polymyxin B for CRKP were further determined using the microdilution broth method according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines.12 A total of 16 antimicrobial agents were tested, including carbapenems (imipenem), -lactam/-lactamase inhibitor complexes (amoxicillin/clavulanic acid and piperacillintazobactam), monocyclic -lactam (aztreonam), cephalosporins (cefoxitin, cefepime, cefazolin, and ceftriaxone), aminoglycosides (gentamicin and amikacin), fluoroquinolones (levofloxacin and ciprofloxacin), folate metabolic pathway inhibitors (sulfamethoxazole), tetracyclines (tobramycin and tigecycline), and polymyxin B. E. coli ATCC 25922 was used as a control.

The carbapenemases produced by CRKP isolates were determined using a modified carbapenem inactivation test (mCIM) recommended by CLSI.11 In addition, a double-disc synergy test was performed to confirm the presence of metallo--lactamases (MBLs).11 The carbapenemase (blaKPC, blaGES, blaNDM, blaIMP, blaVIM, blaOXA-48, blaSIM, blaSPM, blaSME, and blaGIM), extended-spectrum -lactamase (ESBLs; blaTEM, blaDHA, blaSHV, blaCMY-II, and blaCTX-M), and polymyxin B (mcr-1 to mcr-8) resistance genes were detected using polymerase chain reaction (PCR) and DNA sequencing as described previously.2,13

Multilocus sequence typing (MLST) was performed on the polymyxin B-resistant K. pneumoniae isolates by amplifying and sequencing seven housekeeping genes (gapA, infB, mdh, pgi, phoE, rpoB, and tonB) according to a previously described protocol. Sequence types (STs) were assigned using the online database.

Pulsed-field gel electrophoresis (PFGE) was performed to analyze the phylogenetic relatedness of the polymyxin B-resistant K. pneumoniae isolates. Genomic DNA was digested by XbaI for 4 h at 37 C. Electrophoresis was performed for 19 h at 14 C, at an angle of 120, with switch times of 4 and 40 s at 6 V/cm using the CHEF III system (Bio-Rad Laboratories, Hercules, CA, USA). The Salmonella H9812 strain was used as the size marker. Analysis of the PFGE patterns using the Dice similarity coefficient was performed using the Bionumerics software (Applied Maths, Sint-Martens-Latem, Belgium). Clusters were defined as DNA patterns sharing more than 80% similarity.

A donor isolate, N816, was cultured in lysogeny broth (LB), and an azide-resistant E. coli J53 strain was used as the recipient. Transconjugants were selected on LB agar plates with 2 mg/L of polymyxin B or imipenem and 150 mg/L of sodium azide. Multiple attempts to transfer blaNDM-5 plasmid failed. Plasmid DNA was extracted from N816, transferred to competent E. coli DH5, and screened on LB agar plates with 2 mg/L imipenem. After the experiment, the transconjugant (JN816) and transformant (ZN816) were obtained and verified using PCR with previously described primers. Antimicrobial susceptibility testing was subsequently performed on JN816 and ZN816.

Genomic DNA was extracted from JN816 and ZN816 with the Qiagen Midi kit (Qiagen, Hilden, Germany) and sequenced with an Illumina HiSeq 2000 sequencer following a paired-end 2100-bp protocol.14 The raw data were mapped to a reference sequence found on the CLC genomics workbench version 8.0. Sequence comparison and alignment were performed using MEGA 5.01.15

Among the 107 K. pneumoniae isolates, 15 (14.0%) were resistant to polymyxin B according to EUCAST 7.0 guidelines.12 The antimicrobial resistance rates of these isolates are shown in Table 1. These isolates were resistant to imipenem, aztreonam, cefazolin, cefepime, cefoxitin, ceftriaxone, ciprofloxacin, and sulfamethoxazole. The resistance rates of isolates for amikacin, gentamicin, tobramycin, and tigecycline were 46.7, 60.0, 53.3, and 13.3%, respectively.

Table 1 Antimicrobial Resistance Profiling of 15 Carbapenem-Resistant Klebsiella pneumoniae Isolates

Twelve of the 15 polymyxin B-resistant isolates were confirmed as carbapenemase producers as determined using the mCIM assay, among which two isolates had a positive result for the double-disc synergy test, indicating that they also produced an MBL. In addition, 10 CRKP isolates were positive for blaKPC-2, and two were positive for blaNDM. Other carbapenemase genes including blaGES, blaIMP, blaVIM, blaOXA-48, blaSIM, blaSPM, blaSME, and blaGIM were not detected in any of the tested isolates. In addition to blaKPC-2, all isolates were positive for blaSHV, and eight (53.3%) were positive for the ESBL gene, blaCTX-M-65. Only one CRKP isolate was positive for mcr-1, blaNDM-5, blaCTX-M-55, and blaSHV-27 (Figure 1).

Figure 1 Pulsed-field gel electrophoresis results for 15 carbapenem-resistant Klebsiella pneumoniae isolates.

Among the 15 CRKP isolates, five STs were identified, including ST11 (11 isolates), and one isolate each in ST34, ST334, ST485, and a novel ST. The PFGE results showed that the 15 isolates were divided into nine different PFGE clusters; cluster A (4; 26.7%), cluster C (3; 20.0%), and cluster D (2; 13.3%). Each of the remaining six isolates were classified as singletons (Figure 1).

The two CRKP N816 plasmids harboring mcr-1 and blaCTX-M-55, designated as pMCR-1-N816 and pCTX-M-55-N816, respectively, were successfully transferred into the recipient strain (J53) via filter mating conjugation. We confirmed the presence of mcr-1 and blaCTX-M-55 genes in these plasmids. The antimicrobial resistance patterns of CRKP N816 and its transconjugant are shown in Table 2. The blaNDM-5-harboring plasmid of CRKP N816, designated as pNDM-5-N816, was electroporated into E. coli DH5 as described previously. Growth was observed only on plates with imipenem 2 mg/L, and the transformants were screened for the presence of blaNDM-5 using PCR, and blaNDM-5 was located on the plasmid. The antimicrobial resistance patterns of CRKP N816 and its transformants are shown in Table 2.

Table 2 Minimum Inhibitory Concentrations of Antimicrobials Against N816, JN816, ZN816, J53, and DH5

pMCR-1-N816 is 33309 base pairs (bp) long, with an average guanine-cytosine (GC) content of 41.84%. It has 41 predicted open reading frames (ORFs) and belongs to the IncX4 incompatibility group. A BLAST search of the plasmid sequences against the GenBank database showed that pMCR-1-N816 is similar (with 100% query coverage and >98.0% nucleotide identity) to pKP15450-MCR-1, an IncX4-type plasmid carrying mcr-1 among K. pneumoniae isolates in China (Figure 2). Plasmid pCTX-M-55-N816 is 76526-bp in length, with an average GC content of 51.93%. It has 105 predicted ORFs, and a BLAST search of the plasmid sequences against the GenBank database showed that pCTX-M-55-N816 is similar to pKP32558-4, with 89% query coverage and >98.0% nucleotide identity (Figure 3). Furthermore, pNDM-5-N816 is 46286-bp in length, with an average GC content of 46.63%, 59 predicted ORFs, and belongs to the IncX3 incompatibility group. A BLAST search showed that pNDM-5-N816 is similar to pNDM5-LDR, an IncX3-type plasmid carrying blaNDM-5 among K. pneumoniae isolates in China, with 100% query coverage and >99.9% nucleotide identity (Figure 4).

Figure 2 Structure of plasmid pMCR-1-N816 carrying mcr-1 from Klebsiella pneumoniae N816.

Figure 3 Structure of plasmid pCTX-M-55-N816 carrying blaCTX-M-55 from Klebsiella pneumoniae N816.

Figure 4 Structure of plasmid pNDM-5-N816 carrying blaNDM-5 from Klebsiella pneumoniae N816.

Carbapenems are the choice of treatment for infections caused by MDR K. pneumoniae. However, with the emergence of carbapenemase-producing bacteria, carbapenem resistance is increasing. The most common carbapenemase gene is blaKPC-2.16 Since the first discovery of the carbapenem resistance gene, blaNDM-1, in New Delhi, India,17 this gene and its multiple subtypes have been gradually discovered and reported worldwide. Moreover, the emergence of MBL-producing drug-resistant bacteria poses a great challenge for the treatment of drug-resistant bacterial infections. China reported a CRKP strain carrying the blaNDM-1 gene in 2013.18

Polymyxins have been used for many years in veterinary medicine, and nowadays, in human medicine, as a last resort for the treatment of MDR infections, especially CRKP. Thus, the increase in carbapenemase-producing Enterobacteriaceae has resulted in increased use of polymyxins with the inevitable risk of emerging polymyxin resistance.19,20 In this study, 107 CRKP isolates were tested for antimicrobial susceptibility; 15 (14.0%) of them were resistant to polymyxin B. The resistance rates of CRKP isolates to polymyxin B reported in Brazil and other European countries are 15.5% and 36%, respectively.20,21 The differences in antimicrobial resistance rates may be related to the different levels of antimicrobial usage in different countries.22

We found that 15 isolates were resistant to broad-spectrum antibiotics. Sequencing analysis showed that in addition to the blaKPC-2 gene, one or more other kinds of -lactamase genes (such as blaCTX-M, blaSHV, and blaTEM) were identified in these KPC-producing K. pneumoniae strains, with 53% (8/15) of the strains carrying the ESBL gene, blaCTX-M; this is consistent with previous reports.23 Consistent with other studies, the isolates were also more resistant to quinolones and trimethoprim/sulfamethoxazole. Quite often, plasmids carrying ESBL genes also carry other drug-resistant genes including quinolone and trimethoprim/sulfamethoxazole resistance genes.24

The drug susceptibility results of this study showed CRKP has a low resistance to amikacin, possibly because amikacin has only been used for a short time in this region or owing to the presence of restorative mutations. It may also be because of the aminoglycoside-modifying enzymes produced when amikacin is used to treat CRKP; the 16S rRNA gene targeted by amikacin is prone to mutations, resulting in a decrease in the activity of the enzyme to hydrolyze it.25 Although the resistance rate of CRKP to tigecycline is also low in this study, Its FDA approved uses include complicated skin/skin structure infections, complicated intra-abdominal infections, and community-acquired bacterial pneumonia, treatment of these infections limits its use.26 Studies have shown that polymyxin combined with amikacin has obvious synergistic and additive effects, and the MICs of this combination therapy are significantly lower than those of monotherapy.27 Polymyxin and amikacin may be sensitive to each other and as they target multiple proteins through different mechanisms to inhibit biofilm formation and increase membrane permeability, a synergistic effect to inhibit CRKP isolates is exhibited.28

Among the 15 polymyxin B-resistant CRKP isolates, most of the strains carried the blaKPC-2 gene, which is primarily responsible for carbapenem resistance, and this is consistent with our previous report.16 Among these strains, we only detected one isolate positive for mcr-1 gene. This strain was isolated from a blood culture specimen of a 71-year-old male patient and was resistant to multiple drugs, including polymyxin B, but not amikacin. This isolate also had blaCTX-M-55, blaNDM-5 and blaSHV-27 genes. Our experiments and sequencing results show that these mcr-1, blaCTX-M-55, and blaNDM-5 genes do not appear to be on the same plasmid, and the blaSHV-27 gene was found on the bacterial chromosome. Consistent with the above experimental results, the MICs of the corresponding antibiotic of the conjugants and transformants were altered accordingly (Table 2). The other 14 isolates did not harbor the mcr-1 gene. Other drug resistance mechanisms may be related to specific mutations within the genes encoding LPS-modifying enzymes, resulting in increased levels of the intrinsic regulator RamA and hyperproduction of CPS.29,30 However, this needs further testing. Unlike reports in other countries where the mcr-1 and blaNDM-5 genes were found to coexist in E. coli,9,10 this is the first time that K. pneumoniae has been reported to harbor both mcr-1, blaNDM5, and blaCTX-M-55 genes. What is even more worrying is that the plasmids in which these three genes are located have the ability to transfer horizontally. Thus, the bacteria may develop more serious drug resistance and lead to a state where there will be no treatment options. However, PFGE and MLST results of this isolate indicated that it had a different homology from the other 14 polymyxin B-resistant K. pneumoniae isolates, indicating that this type of bacteria did not have an outbreak. This isolate is not part of the most common ST (ST11) in China,16 but is a rare ST485 isolate. There is no report of an outbreak caused by K. pneumoniae ST485 at home and abroad. The patient had no history of foreign travel in the inquiry; thus, we infer that the occurrence of this isolate is a rare phenomenon in this area, but continuous monitoring to prevent the spread of this type of bacteria, which may cause more serious drug resistance, is warranted.

It has long been believed that polymyxin is mediated by chromosomes.19 Until 2015, Chinese scholars reported that the plasmid-mediated polymyxin resistance gene mcr-1 was found in Enterobacteriaceae isolated from humans and animals.3 Since then, people have a new understanding of the mechanism of polymyxin resistance. The emergence of a new type of drug resistance mechanism immediately set off a research boom among microbiologists worldwide. After sequencing and analysis of the plasmid obtained in this study, it was found that the isolate contained three plasmids of different sizes (33, 46, and 76 kb), which carried the mcr-1, blaCTX-M-55, and blaNDM-5 genes, which also verified the results of our previous conjugation, transformation, and drug susceptibility experiments. Further analysis of the data obtained using sequencing revealed that the similarity between the plasmid carrying mcr-1 and the plasmid pKP15450-MCR-1 was 98.77%. The mcr-1 gene at the starting point of the plasmid is approximately 3826 to 5451 bp, which encodes 541 amino acids. Analysis of its upstream and downstream genes showed that there are no common insert elements, indicating that the mcr-1 gene on this type of plasmid is more prone to horizontal transfer. The plasmid carrying the blaCTX-M-55 gene has a similarity of 99.96% with the plasmid pKP32558-4. The start site of the blaCTX-M-55 gene is approximately 2052 to 2927 bp, which encodes 291 amino acids and has an inserted transposon IS431 around the gene. The above two plasmids can be successfully joined to the same strain at the same time, indicating that they are compatible with each other. The plasmid carrying the blaNDM-5 gene has a similarity of 98.4% with the plasmid pNDM5-LDR. The start site of the blaNDM-5 gene is approximately 5783 to 6595 bp, which encodes 270 amino acids and has an inserted transposon, IS1086 and IS5H, upstream of the gene. The IS1086 and IS5H sequences indicate that the plasmid can transfer horizontally and confirm the results of previous experimental research. The resistance gene expression and transferability of this isolate have been further verified, which may lead to serious drug resistance.

In conclusion, the present study demonstrated for the first time the coexistence of mcr-1, blaNDM5, and blaCTX-M-55 in a K. pneumoniae ST485 isolate. Therefore, treatment strategies and monitoring should be implemented to limit the widespread of isolates containing mcr-1, blaNDM5, and blaCTX-M-55.

As the Klebsiella pneumoniae clinical isolate in this study was part of the routine hospital laboratory procedure, we have confirmed that the isolate has no identifiable patient data, the Second Affiliated Hospital of Nanchang University Medical Research Ethics Committee exempted this research for review.

This study was supported by a grant from the Department of Science and Technology of Jiangxi Province (20181BBG70030), the Jiangxi Natural Science Foundation (No.20181BAB205066), Science and Technology Plan of Jiangxi Provincial Health Commission (NO.20195211).

All authors contributed to data analysis, drafting or revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

The authors declare that they have no conflict of interest.

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More:
Emergence of the Coexistence of mcr-1, blaNDM-5, and blaCTX-M-55 in Kl | IDR - Dove Medical Press

Dublin, Aug. 27, 2021 (GLOBE NEWSWIRE) -- The "Regenerative Medicine Market by Product, by Material, by Application - Global Opportunity Analysis and Industry Forecast, 2021 - 2030" report has been added to ResearchAndMarkets.com's offering.

The global regenerative medicine market is expected to reach USD 172.15 billion by 2030 from USD 13.96 billion in 2020, at a CAGR of 28.9%. Regenerative Medicine are used to regenerate, repair, replace or restore tissues and organs damaged by diseases or due to natural ageing. These medicines help in the restoration of normal cell functions and are widely used to treat various degenerative disorders such as cardiovascular disorders, orthopedic disorders and others.

The rising demand for organ transplantation and increasing awareness about the use of regenerative medicinal therapies in organ transplantation along with implementation of the 21st Century Cures Act, a U.S. law enacted by the 114th United States Congress in December 2016 are creating growth opportunities in the market. However, high cost of treatment and stringent government regulations are expected to hinder the market growth.

The global regenerative medicine market is segmented based on product type, material, application, and geography. Based on product type, the market is classified into cell therapy, gene therapy, tissue engineering, and small molecule & biologic. Depending on material, it is categorized into synthetic material, biologically derived material, genetically engineered material, and pharmaceutical. Synthetic material is further divided into biodegradable synthetic polymer, scaffold, artificial vascular graft material, and hydrogel material. Biologically derived material is further bifurcated into collagen and xenogenic material. Genetically engineered material is further segmented into deoxyribonucleic acid, transfection vector, genetically manipulated cell, three-dimensional polymer technology, transgenic, fibroblast, neural stem cell, and gene-activated matrices. Pharmaceutical is further divided into small molecule and biologic. By application, it is categorized into cardiovascular, oncology, dermatology, musculoskeletal, wound healing, ophthalmology, neurology, and others. Geographically, it is analyzed across four regions, i.e., North America, Europe, Asia-Pacific, and RoW.

The key players operating in the global regenerative medicine market include Integra Lifesciences Corporation, AbbVie Inc., Merck KGaA, Medtronic, Thermo Fisher Scientific Inc., Smith+Nephew, Becton, Dickinson and Company, Baxter International Inc, Cook Biotech, and Organogenesis Inc., among others.

Key Topics Covered:

1. Introduction

2. Regenerative Medicine Market - Executive Summary

3. Porter's Five Force Model Analysis

4. Market Overview4.1. Market Definition and Scope4.2. Market Dynamics

5. Global Regenerative Medicine Market, by Product Type5.1. Overview5.2. Cell Therapy5.3. Gene Therapy5.4. Tissue Engineering5.5. Small Molecules & Biologics

6. Global Regenerative Medicine Market, by Material6.1. Overview6.2. Synthetic Materials6.3. Biologically Derived Materials6.4. Genetically Engineered Materials6.5. Pharmaceuticals

7. Global Regenerative Medicine Market, by Application7.1. Overview7.2. Cardiovascular7.3. Oncology7.4. Dermatology7.5. Musculoskeletal7.6. Wound Healing7.7. Opthalomolgy7.8. Neurology7.9. Others

8. Global Regenerative Medicine Market, by Region8.1. Overview8.2. North America8.3. Europe8.4. Asia-Pacific8.5. Rest of World

9. Company Profile9.1. Integra Lifesciences Corporation9.2. Abbvie Inc.9.3. Merck Kgaa9.4. Medtronic plc9.5. Thermo Fisher Scientific Inc.9.6. Smith+Nephew9.7. Becton, Dickinson and Company9.8. Baxter International Inc9.9. Cook Biotech9.10. Organogenesis Inc

For more information about this report visit https://www.researchandmarkets.com/r/pl6r1p

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Worldwide Regenerative Medicine Industry to 2030 - Featuring AbbVie, Medtronic and Thermo Fisher Scientific Among Others - GlobeNewswire