#NLR proteins

[kamounlab]

Madhuprakash, J., Toghani, A., Contreras, M.P., Posbeyikian, A., Richardson, J., Kourelis, J., Bozkurt, T.O., Webster, M.W., and Kamoun, S. 2024. A disease resistance protein triggers oligomerization of its NLR helper into a hexameric resistosome to mediate innate immunity.

Check the threads by Madhu @JMadhuprakash and AmirAli @amiralito_ on the helper NLR inflammasome-like structure. Animation and much more by Michael @WebsterMichael0 #NLRbiology #OpenPlantNLR https://x.com/KamounLab/status/1803773797130018948

[sbgridconsortium]

Ancient mediators of innate immunity

Bacteria can become infected by bacteriophages and have developed a range of anti-phage immune pathways to counteract these infections. These pathways are often multi-gene systems encoding proteins that sense and inhibit virion production, and efforts to catalog anti-phage signaling systems in bacteria have revealed that some of these genes share homology with components of eukaryotic immune systems. This suggests that eukaryotes horizontally acquired some innate immune genes from bacteria.

Many components have been identified as homologous between bacteria and humans, including bacterial cyclic-oligonucleotide-based anti-phage signaling systems (CBASS) with human cGAS and STING, and bacterial Viperins and Gasdermins with human Viperin and Gasdermin D. However, SBGrid member Aaron Whiteley and other researchers have been searching for other potential components in bacterial anti-phage signaling systems which could be homologous to immune signaling elements in humans. The researchers demonstrate that bacteria express anti-phage proteins containing a NACHT module, which is an important element of the animal nucleotide-binding domain leucine-rich repeat containing gene family called NLRs. These NACHT proteins are widespread in bacteria and contain a C-terminal sensor, central NACHT module, and N-terminal effector component, acting against both DNA and RNA bacteriophages. 

Above: Previously reported structure of NLR family CARD domain-containing protein 4. CC BY SBGrid.

Importantly, they determined that mutations in human NLR which lead to stimulus-independent activation of downstream signaling also activate bacterial NACHT proteins, suggesting that the bacterial and human systems share similar signaling mechanisms. This work identifies NACHT module-containing proteins as ancient innate immune signaling elements and expands our knowledge of homology between bacterial anti-phage immune pathways and eukaryotic immune systems.

Read more about this work in Cell.

[talksthesuccess]

The Best Doctor in the World

The Best Doctor in the World 

Doctors are the elemental keystone for any country’s healthcare system. It is proved that an established education system in a country is always directly proportional to having qualified doctors. The profession of doctor is one of the noblest occupations in the world. Doctors are respected and considered as they have divine qualities.

The world is pleased to have the wonderful and best doctors. Now, who is the best doctor in the world? So here is the answer Patrick Soon-Shiong is reflected as one of the best doctors in the world. Let’s explore the life journey of the best doctor. 

Who is Patrick Soon-Shiong?

Patrick Soon-Shiong, M.D., a physician, surgeon, scientist, inventor, technologist, and philanthropist, has dedicated his career to the fundamental biology driving life-threatening diseases and transforming these discernments into medical innovations with global impact. 

Along with his devotion towards his career as a doctor, he also performs his duties as Chairman and Chief Executive Officer of NantWorks, a network of businesses with extensive knowledge of many complicated industries, including physics, data, AI, medical science, communications, and mobility.

Soon-Shiong’s Journey: From South Africa to Medical Innovation

Patrick Soon-Shiong was born on July 29, 1952, in Port Elizabeth, Union of South Africa to Chinese immigrant parents who migrated from China during the Japanese occupation in World War II. His parents were Hakka originally from Mexican District in Guangdong province. 

Eventually, Soon-Shiong received a bachelor’s degree in medicine (MBBCh) at the University of Witwatersrand and graduated with flying colors, the fourth in a group of 200 students. After that, he continued his education at the University of British Columbia, graduating with a master’s degree in 1979.

The American College of Surgeons, the Royal College of Physicians and Surgeons of Canada, and the American Association of Academic Surgery all gave him research grants.

Patrick Soon-Shiong: A trailblazing Surgeon 

A total of 30 years of Dr. Soon-Shiong’s working experience belongs to medical revolutions. All over Dr. Soon-Shiong’s career, he has introduced therapies for diabetes and cancer, recognized himself as the publisher of over 100 scientific papers, and has taken over 675 patients worldwide for innovative advancements passing over a multitude of fields of medicine, technology, and artificial intelligence. 

He was the one who performed UCLA’s first whole-organ pancreas transplant and the world’s first enclosed islet cell transplant in Type 1 diabetic patients he performed this transplant as an Assistant Professor at UCLA in 1993. 

He worked at NASA to further his studies in stem cells and nanotechnology during the early 1990s, conducting projects related to the space shuttle program. He was in charge of the California Nanosystems Institute (CNSI) while he was at UCLA.

To incorporate supercomputing data centers and to devise an augmented intelligence for genomic sequencing, NantOmics’ boss, Dr Soon-Shiong, bought the National LambdaRail (NLR) in 2011. This Layer 1 network was further developed by Dr. Soon-Shiong as MOX Networks in 2013. 

Patrick Soon-Shiong: Developer of Abraxane 

Dr. Soon-Shiong was also the developer of Abraxane, a very pioneered drug, in 1995. Abraxane was the first human protein (albumin) nanoparticle invented to activate a specific receptor on the blood vessels that supply the tumor and this drug was purposed to transform the tumor’s microenvironment and activate the immune system.

Abraxane has been used in different types of cancers inclusive of metastatic breast cancer since 2005, cell lung cancer since 2012, and pancreatic cancer since, 2013 as approved by the FDA.

Patrick Soon-Shong: Business Career 

Further on to his medical and scientific career, Soon-Shiong also emerged as an active businessman during the late 1990s, and since the early 2010s, he has grown as an investor. 

Already in the period from 1997 up to 2010, Dr. Soon-Shiong was identified as the founder, chairman, and chief executive officer of two public pharmaceutical companies namely Abraxis BioScience, Inc. (NASDAQ: ABII) and American Pharmaceutical Partners Inc. (NASDAQ: APPX) Vista, the parent company of APPX, have inked a biologic supply agreement.

In addition, Dr. Soon-Shiong is the creator of NantWorks, LLC, a business firm whose primary objective is to establish a next-generation pharmaceutical development network and transformative global health information for the safe communication of genetic and medical data, tackling issues of climate change, and changing the characteristics of new media.

The San Diego Union-Tribune and the Los Angeles Times were purchased by Dr. Soon-Shiong in 2018. The process was put into law through the 21st Century Cures Act, and the committee carrying out this policy is the Health Information Technology Advisory Committee, which has a primary duty to provide the government and President with advice on relevant health IT policies.

He was nominated to this committee too. Before this, Dr. Soon-Shiong was a member of Bank of America’s Global Advisory Board and co-chair of the CEO Council for Health and Innovation at the Bipartisan Policy Center.

Patrick Soon-Shiong: Awards and Achievements

Many reputable, prestigious, and honorable awards have been achieved by Soon-Shiong throughout his career. Some noticeable awards include the Ellis Island Medal of Honor, the National Medal of Technology and Innovation, the Horatio Alger Award, and the Ernst & Young Entrepreneur of the Year Award. He has also been acknowledged as one of the world’s billionaires and one of the most influential people in healthcare.  

Read More:- Taylor Swift: Redefining Pop Music One Album at a Time

[medicomunicare]

Le sindromi autoinfiammatorie: entità prima misteriose, ora hanno un agente responsabile e possibili soluzioni

Le malattie autoinfiammatorie sistemiche (SAID) colpiscono principalmente la risposta immunitaria innata e sono entità patologiche distinte, sebbene possano sovrapporsi in qualche modo alle malattie autoimmuni. Negli ultimi anni lo spettro dei SAID è stato ampliato. I recettori simili al dominio di oligomerizzazione nucleotidica (NOD) (NLR) sono un gruppo specializzato di proteine intracellulari…

[david-ojcius]

Free article: A subset of NLRs function to mitigate overzealous pro-inflammatory signaling produced by NF-κB activation. Under normal pathophysiologic conditions, proper signaling by these NLRs protect against potential autoimmune responses. These NLRs associate with several different proteins within both the canonical and noncanonical NF-κB signaling pathways to either prevent activation of the pathway or inhibit signal transduction. Inhibition of the NF-κB pathways ultimately dampens the production of pro-inflammatory cytokines and activation of other downstream pro-inflammatory signaling mechanisms. Dysregulation of these NLRs, including NLRC3, NLRX1, and NLRP12, have been reported in human inflammatory bowel disease (IBD) and colorectal cancer patients, suggesting the potential of these NLRs as biomarkers for disease detection. Mouse models deficient in these NLRs also have increased susceptibility to colitis and colitis-associated colorectal cancer. While current standard of care for IBD patients and FDA-approved therapeutics function to remedy symptoms associated with IBD and chronic inflammation, these negative regulatory NLRs have yet to be explored as potential drug targets. In this review, we describe a comprehensive overview of recent studies that have evaluated the role of NLRC3, NLRX1, and NLRP12 in IBD and colitis-associated colorectal cancer.

[sufficientlylargen]

Oooh, nice! I forgot about acronyms with numbers like that!

I have since learned that a nested acronym is called a "macronym", and the wikipedia article for that suggests "RARS" and "VITAL" as possible contenders:

VITAL -> "VHDL Initiative Towards ASIC Libraries" VHDL -> "VHSIC Hardware Description Language" VHSIC -> "Very High Speed Integrated Circuit" ASIC -> "Application-specific integrated circuit"

VITAL -> "Very High Speed Integrated Circuit Initiative Towards Application-specific integrated circuit Libraries"

Ratio: 93/5 = 18.6 (not good enough)

RARS -> "Regional ATOVS Retransmission Service" ATOVS -> "Advanced TOVS" TOVS -> "TIROS operational vertical sounder" TIROS -> "Television infrared observational satellite"

So RARS -> "Regional Advanced Television Infrared Observational Satellite Operational Vertical Sounder Retransmission Service"

Ratio: 103/4 = 25.75 (better)

There are also apparently a lot of symbols in protein naming, so e.g. the NACHT protein domain technically has a whopping ratio of 39.8, but some of the expansions aren't actually acronyms or initialisms, like HET being short for the "heterokaryon incompatibility protein" domain:

NACHT -> "NAIP, C2TA, HET-E, TEP1" NAIP -> "NLR family apoptosis inhibitory protein" NLR -> "NOD-like receptor" NOD-like -> "nucleotide-binding oligomerization domain-like" C2TA -> "class 2 major histocompatibility complex transactivator" HET -> "Heterokaryon incompatibility protein" (the -E is just a classifier, AFAICT) TEP1 -> "Telomerase protein component 1"

NACHT -> "Nucleotide-binding oligomerization domain-like receptor family apoptosis inhibitory protein, class 2 major histocompatibility complex transactivator, Heterokaryon incompatibility protein E, Telomerase protein component 1"

Ratio: 199/5 = 39.8

But it's not entirely fair to even call NACHT an acronym, since it's not actually short for "NAIP, C2TA, HET-E, TEP1", those are just common examples of things in that domain.

XHR stands for "XML HTTP Request", where XML is the "eXtensible Markup Language" and HTTP is the "Hypertext Transfer Protocol", so the full expansion of XHR is "extensible markup language hypertext transfer protocol request", so those 3 letters expand to 56 letters (62 with spaces), and this got me wondering, there must be acronyms or initialisms with an even greater ratio of "expanded length"/"unexpanded length", but apart from recursive acronyms I can't think of longer examples.

Can anyone else think of any?

[kreuzaderny]

Prokaryotic innate immunity through pattern recognition of conserved viral proteins

The innate immune systems of animals, plants, and fungi universally use nucleotide binding oligomerization domain–like receptors (NLRs) of the STAND superfamily to detect molecular patterns common to pathogens. Gao et al. show that NLR-based immune pattern recognition is also prevalent in bacteria and archaea, something that was not known before. In particular, the authors characterized four families of NLR-like genes, finding that they are specific sensors for two highly conserved bacteriophage proteins. Upon binding to the target, these NLRs activate diverse effector domains, including nucleases, to prevent phage propagation. These findings demonstrate that pattern recognition of pathogen-specific proteins is a common mechanism of immunity across all domains of life.

[kamounlab]

Mauricio P. Contreras, Hsuan Pai et al.

The nucleotide-binding domain of NRC-dependent disease resistance proteins is sufficient to activate downstream helper NLR oligomerization and immune signaling.

Conceptual image by Andy Posbe @adnroide

[venkatesh-kolli]

Data variables-PCS-Covid-19 and pregnancy

The study incorporated Prospective Cohort study design. For the Prospective Cohort study, the explanatory variables, which are the exposures, would be severity levels of symptoms. These levels are separated into four subgroups of cases as asymptomatic, mild, moderate, severe(extreme). These levels can be defined based on measurements of clinical indications of Covid-19 such as leukocyte, neutrophil‐to‐lymphocyte ratio (NLR), neutrophil, lymphocyte, platelet, D‐dimer, C‐reactive protein. The response variables would be maternal and infant mortality rates, premature birth time, fetal birth weight, maternal morbidity, maternal hospitalizations during pregnancy. The maternal mortality cases in this study should only include indirect obstetric maternal mortality cases with underlying disease being exposure to Covid-19 for primary outcome. The secondary outcomes are measured based on ICD-10 codes for Pregnancy, childbirth and the puerperium provided by CDC. The explanatory variables would be distinct values rather than continuous values. All the response variables would be binary values except for premature birth time and fetal birth weight which would be continuous values.

[david-ojcius]

Gut microbiota influence host immunity and metabolism during obesity. Bacterial sensors of the innate immune system relay signals from specific bacterial components (i.e., postbiotics) that can have opposing outcomes on host metabolic inflammation. NOD-like receptors (NLRs) such as Nod1 and Nod2 both recruit receptor-interacting protein kinase 2 (RIPK2) but have opposite effects on blood glucose control. Nod1 connects bacterial cell wall-derived signals to metabolic inflammation and insulin resistance, whereas Nod2 can promote immune tolerance, insulin sensitivity, and better blood glucose control during obesity. NLR family pyrin domain containing (NLRP) inflammasomes can also generate divergent metabolic outcomes. NLRP1 protects against obesity and metabolic inflammation potentially because of a bias toward IL-18 regulation, whereas NLRP3 appears to have a bias toward IL-1β-mediated metabolic inflammation and insulin resistance. Targeting specific postbiotics that improve immunometabolism is a key goal. The Nod2 ligand, muramyl dipeptide (MDP) is a short-acting insulin sensitizer during obesity or during inflammatory lipopolysaccharide (LPS) stress. LPS with underacylated lipid-A antagonizes TLR4 and counteracts the metabolic effects of inflammatory LPS. Providing underacylated LPS derived from Rhodobacter sphaeroides improved insulin sensitivity in obese mice. Therefore, certain types of LPS can generate metabolically beneficial metabolic endotoxemia. Engaging protective adaptive immunoglobulin immune responses can also improve blood glucose during obesity. A bacterial vaccine approach using an extract of the entire bacterial community in the upper gut promotes protective adaptive immune response and long-lasting improvements in blood glucose control. A key future goal is to identify and combine postbiotics that cooperate to improve blood glucose control.

[your-dietician]

Neutrophil-to-Lymphocyte ratio in diabetic neuropathy

New Post has been published on https://tattlepress.com/health/diabetes/neutrophil-to-lymphocyte-ratio-in-diabetic-neuropathy/

Neutrophil-to-Lymphocyte ratio in diabetic neuropathy

Introduction

As defective insulin secretion or impaired biological function, chronic hyperglycemia can cause damage to various tissues and systems, especially eyes, kidneys, blood vessels and nerves.1 Most diabetes patients can be divided into two types. Type 1 diabetes mellitus (T1DM), due to the absolute lack of insulin secretion, can usually be identified by serological evidence and genetic markers of islet autoimmunity. Abnormal inflammation and immune responses are associated with the development of T1DM. Recent study have shown that innate immunity and inflammatory mediators play an important and wide-ranging roles, possibly inhibiting β-cell function,2 promoting subsequent apoptotic processes, and leading to insulin resistance in surrounding tissues.

Type 2 diabetes mellitus (T2DM), the more common type, accounts for 90~95% of diabetes, due to the insulin resistance (IR) and inadequate compensatory secretory response. Factors that contribute to impaired glucose tolerance (IGT) and IR include genetic factors, environmental factors, age, obesity, and inflammation. Activation of adipose tissue may lead to the release of inflammatory cytokines associated with IR, such as TNF-α, leptin, IL-6, resistin, monocyte chemotactic protein-1 (MCP-1), angiotensin, endolipids, retinol-binding protein-4, and serum amyloid A (SAA).1,2 Inflammatory factors induce and maintain the inflammatory response and inflammatory damage during the development of diabetes.

As one of the complications of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) usually develops insidiously and gradually. It can manifest as pain, numbness, tingling, weakness, and balance disorders, leading to ulcers, gangrene, and even amputation. Some patients are asymptomatic at an early stage, which may lead to neglect of the disease. Therefore, early detection and treatment of DPN play an important role in improving disease prognosis and life quality.3 The development of DPN is related to metabolic disorders, such as oxidative stress, increased polyol flux, accumulation of glycosylated end products and lipid changes, and other metabolic abnormalities.4 To date, some hemogram derived inflammatory markers and related metabolites have been found to be associated with diabetes mellitus. Mean platelet volume (MPV) can provide important information on the course and prognosis in many inflammatory conditions.5 Red blood cell distribution width (RDW) is associated with cardiovascular disease, sepsis, and tumors.6–8 Besides, recent studies have shown that NLR and RLR can be used as systemic marker in some inflammatory conditions including cardiovascular disease, metabolic syndrome and malignancies.9–11 Both of them are novel, available, and inexpensive marker of Inflammatory status. Against this background, we aimed to study the association between the occurrence of DPN and related indicators in patients with type 1 or type 2 diabetes, and patients without DPN were followed up to investigate the predictive value of these indicators on newly diagnosed DPN.

Materials and Methods

Subjects

In this study, 225 consecutively hospitalized patients were recruited from August 31, 2018 to October 1, 2019 at the First Affiliated Hospital of University of Science and Technology of China. The inclusion criteria included a diagnosis of diabetes with or without the symptoms and signs of DPN. Diabetes was diagnosed using the revised American diabetes association standards, including fasting plasma glucose [FPG] ≥7.0mmol/L[126mg/dL] and/or postprandial 2h glucose value ≥11.1 mmol/L [200mg/dL].12 Exclusion criteria were as follows: a) a history of multiple nerves due to other causes, such as hereditary, alcoholic, metabolic, inflammatory, and toxic factors; b) a history of tumor radiotherapy and chemotherapy; c) skin damage or swelling which can interfere with nerve conduction; d) active infection and using of medicine affecting the white blood cell counts; e) Complicated hematogenous disease or rheumatic disease; f) a prior history of leg or ankle fractures or surgery. Patients without DPN were followed up in the following 18 months, and patients with new-developed DPN were counted. This study gained approval by the Chinese Clinical Trial Registry’s ethics committee, and informed consent was obtained from all enrolled patients. This study’s clinical registration number is ChiCTR1900026629. This study was conducted in accordance with the Declaration of Helsinki. Due to the limited number of ethical review staff in our hospital, we had to queue for a long time, so we chose to get approval from the Ethics Committee of the Chinese Clinical Trial Registry.

Data Collection

We collected data on patient characteristics (eg, age, type of diabetes, disease course, medical history, height, weight) and inflammatory indicators (eg, levels of NLR and PLR) using the hospital’s electronic medical record system. The included subjects were divided into four different groups, including T1DM with DPN group (T1DPN group), T1DM without DPN group (T1DM group), T2DM with DPN group (T2DPN group), T2DM without DPN group (T2DM group). The diagnostic criteria for DPN were based on the Toronto Expert Consensus.13 Professional doctors (Meichao Chen and Yuanbo Wu) verified the data.

Statistical Analysis

Statistical software SPSS, version 20.0, was used to analyze the collected data. Continuous data and normally distributed data were expressed as the mean ± standard deviation using the Student’s t-test for intergroup comparisons, whereas non-normally distributed data were expressed as the median (1/4, 3/4) using the Mann–Whitney U-test. Categorical variables were expressed as counts (%) using the χ2 test for comparisons. The influence of related indicators levels were assessed using binary logistic regression analysis with significant factors. Results were expressed as adjusted odds ratios (OR) with the corresponding 95% confidence intervals (CI). Receiver operating characteristic (ROC) curves were drawn, cut-off values were determined. The cut-off values and their corresponding sensitivity and specificity were determined using the Youden index. Drew the corresponding box diagram for the indicator with the maximum AUC. P values less than 0.05 were considered statistically significant.

Results

General Data

A total of 70 patients with type 1 diabetes were recruited, including 48 patients with DPN and 22 patients without DPN. For type 2 diabetes, 155 patients were recruited, including 74 patients with DPN and 81 patients without DPN.

Data were compared between the two groups in terms of general patient characteristics (eg, age, disease course), inflammatory indicators (eg, levels of NLR and PLR).

For type 1 diabetes, age was statistically different between the two groups. The age of patients with DPN was generally higher than that of patients without DPN (36.31 ± 15.64 years and 28.32 ± 12.79 years, respectively). For type 2 diabetes, age, disease course and systolic blood pressure were statistically different between the two groups. The age of patients with DPN was generally higher than that of patients without DPN (61.92 ± 11.22 years and 55.90 ± 11.34 years, respectively). The disease course of patients with DPN was longer than that of patients without DPN (11.00 (5.00, 19.50) years and 6.00 (2.00, 10.00) years, respectively). The systolic blood pressure of patients with DPN was higher than that of patients without DPN (144.07 ± 20.60 years and 136.47 ± 17.12 mmHg, respectively). The baseline characteristics of hospitalized patients are shown in Table 1.

Table 1 Baseline Patient Characteristics and Laboratory Results

For type 1 diabetes, levels of platelet counts, indirect bilirubin, total cholesterol, NLR and PLR were all statistically different between the two patient groups, and for type 2 diabetes, indirect bilirubin, triglyceride and NLR were all statistically different between the two patient groups. Notably, the inflammatory indices of patients with DPN were generally higher than those of patients without DPN, as shown in Table 1.

Predictive Value of NLR, PLR and I-BIL

Between DPN group and DM group, ROC curves were drawn for NLR, PLR and I-BIL. The AUCs and cut-off values were calculated according to their specificity and sensitivity as predictive factors.

The AUC of PLR levels was 0.753 (95% CI 0.635–0.871); the sensitivity was 70.80% and the specificity was 77.30% for predicting DPN in type 1 diabetes when the cut-off level of PLR was 97.880. The cut-off NLR level was set at 2.485, with a sensitivity of 38.00% and a specificity of 79.00%, for predicting disease severity, and an AUC of 0.602 (95% CI 0.513~0.691), as shown in Figure 1 and Table 2.

Table 2 ROC Curve Area and Cut-Off Values of NLP PLR and I-Bil for the Diagnosis of DPN

Figure 1 The ROC curves of predicting whether diabetic patients combined with DPN. Subfigure (A) is the receiver operating characteristic analysis (ROC) for NLR, PLR and I-BIL to predict DPN in T1DM. Subfigure (B) is the ROC curve of NLR and I-BIL for predicting whether T2DM is combined with DPN.

Comparison of NLR and PLR in Different Groups

The NLR level in T2DM with DPN group was statistically higher than that of T2DM without DPN group, T1DM with DPN group and T1DM without DPN group. While the PLR level in the T1DM with DPN group was significantly higher than that of T1DM without DPN group, T2DM with DPN group and T2DM without DPN group, as shown in Figures 2 and 3.

Figure 2 The comparison of the PLR and NLR values of DPN for 2 types of diabetes. Subfigure (A) is the comparison of the PLR value between the T1DM with DPN group and T1DM without DPN group. Subfigure (B) is the comparison of the NLR value between the T2DM with DPN group and T2DM without DPN group.

Figure 3 Bar chart of NLR and PLR values for each group. Subfigure (A) is a bar diagram for four sets of PLR values. Subfigure (B) reacts NLR values for four groups; P < 0.05 was marked as “*”, and P < 0.01 as “**”.

Follow-Up Results

General Data

After 18 months of follow-up for diabetes patients without DPN, 9 patients of type 1 diabetes were newly diagnosed with DPN, while 14 patients of type 2 diabetes were newly diagnosed with DPN. We analyzed the data between patients with and without newly diagnosed DPN. The type of diabetes mellitus was a significant factor for the new onset of DPN, and the BWI of patients without DPN was higher than that of patients with newly diagnosed DPN. The results are shown in Table 3.

Table 3 Baseline Patient Characteristics and Laboratory Results on Follow-Up Subjects

For the relative indicator, levels of NLR, PLR, NEUT (%), NC (109/L), LYMPH (%), LC (109/L), TC (mmol/L), TG (mmol/L) and LDL-C (mmol/L) were all statistically different between the two patient groups. For the inflammatory indicators, levels of PLR and NLR were all statistically higher in the group with newly diagnosed DPN. For lipid metabolism-related indexes, TC (mmol/L), TG (mmol/L) and LDL-C (mmol/L) were significantly lower in the group with newly diagnosed DPN. The results are shown in Table 3.

Regression Model

After adjusting for above recorded confounders such as BWI, TC, TG, LDL-C, type of diabetes and NLR were associated with the new diagnosis of DPN in multivariate binary logistic regression analysis. The adjusted OR were 0.091 (95% CI, 0.010–0.799) and 0.060 (95% CI, 0.014–0.258; p ≤ 0.001), respectively. The results are shown in Table 4.

Table 4 Binary Logistic Regression Analysis for Newly Diagnosed DPN in Follow-Up Subjects

Discussion

The neuropathy of diabetes is the most common neurological disorder in the world, and its prevalence increases with the extension of diabetes.14 It affects about half of people with diabetes, affecting their sensorimotor function. And the early stages of DPN can be asymptomatic, resulting in delaying diagnosis. Seeking an effective and convenient screening method can improve the screening efficiency.

Multiple factors contribute to the occurrence of DPN, including endothelial injury, microvascular dysfunction, metabolic disorders, oxidative stress, abnormal cytokines and immune factors, among which inflammatory injury plays an important role. Chronic hyperglycemia can lead to microcirculation disorders. A series of vascular pathological changes can occur, such as vascular endothelial cell proliferation, microvascular basement membrane thickening and hyaline degeneration, which leads to direct narrowing of lumen. The increase of blood viscosity and the disturbance of blood flow aggravate the reduction of blood supply to local tissues. This process leads to ischemia and hypoxia of nerve tissues, stimulating the increase of cytokines, and aggravating inflammatory damage. Besides, hyperglycemia leads to damage through several major, well-characterized biochemical pathways, including activation of the polyol pathway, increased levels of advanced glycation end products (AGEs) and their receptors, activation of protein kinase C (PKC),15 mitogen-activated protein kinase (MAPK), and inducible nitric oxide synthase.16 These biochemical processes can produce oxidative mediators and inflammatory mediators, resulting in local or systemic tissue damage. Abnormal lipid metabolism is also one of the important influencing factors. Adipocytes are important components for inducing and maintaining the inflammatory response. In general, inflammation injury, activated and maintained by various pathways, plays an important role in the development of diabetes mellitus and its complications.

Neutrophil-to-lymphocyte ratio (NLR) represents the balance of neutrophils and lymphocytes in vivo. Neutrophils are closely related to inflammatory responses, and lymphocytes reflect immune regulatory pathways.17,18 They can reflect systemic inflammation,19,20 as well as innate immune responses (mediated by neutrophils) and adaptive immune responses (mediated by lymphocytes).21 The nonspecific inflammatory response caused by hyperglycemia may lead to changes in peripheral blood cell levels, which may explain the abnormal NLR values. Association between inflammatory conditions and elevated NLR has been well-established.22 The reason NLR is reported as a novel marker is that it is very stable compared with the absolute count, which can be altered by various physical, physiological and pathological factors.23 Some clinical studies have proposed that NLR value is related to DM and its complications. Duman et al‘s study has demonstrated that NLR was strongly correlated with age, fasting plasma glucose and HbA1c.24 A Japanese study showed that NLR might be a potential factor for evaluating diabetic patients with a higher degree of albuminuria,25 suggesting that NLR may predict the existence of microvascular complications.26

In diabetic patients, abnormal insulin action may lead to increased platelet adhesion.

At the same time, hyperglycemia also accelerate platelet metabolism and production, exacerbating the imbalance between coagulation and anticoagulation in vivo. This process may play an important role in atherogenesis, thrombosis and microcirculation disturbance.27 PLR is reported to be a prognostic marker of inflammation for many types of cardiovascular disease, including peripheral arterial disease (PAD) and hypertension.28,29 PLR is also reported to have predictive effect about diabetes mellitus and diabetic complications in recent years. A cross-sectional study from Japan demonstrated that PLR can be a marker for high risk diabetic foot and diabetic foot ulcer in patients with type 2 diabetes.30 Besides, Duan et al’s study demonstrated that the PLR was associated with proteinuria and prognosis in diabetic kidney disease (DKD) patients.31

Bilirubin is a product of heme degradation, and recent studies have reported the beneficial effects of elevated serum bilirubin on cardiovascular health and its antioxidant properties at physiological concentrations.32 Research has demonstrated that bilirubin has anti-inflammatory properties in vitro and in vivo. Bilirubin releases eNOS by inhibiting protein kinase C and NAD(P)H oxidase pathways that produce oxidants, and inhibits the peroxidation of lipids and lipoproteins, thereby reducing ROS and protecting nerves from damage.33,34 DPN is associated with inflammatory responses, so bilirubin may have beneficial effects. Kim et al35 demonstrated a significant correlation between low serum bilirubin levels and DPN.

In our study, NLR and PLR were significantly increased in DPN in patients with type 1 diabetes. Through the ROC curve, the area under the curve of PLR was the largest. When the cut-off value was 97.880, the sensitivity is 70.80% and the specificity was 77.30%. PLR could be used to predict whether type 1 diabetes patients were associated with peripheral neuropathy. As for indirect bilirubin, this indicator is negatively correlated with DPN, which is consistent with the research results of Kim et al. According to the antioxidant and anti-inflammatory properties of bilirubin, this is in line with the expected results. For patients with type 2 diabetes, NLR was significantly higher in the DPN group. According to the ROC curve, when the cut-off value is 2.485, the sensitivity is 38.00% and the specificity was 79.00%. NLR may be an independent risk factor for T2DM with DPN, as demonstrated by Siying Liu et al, Xu et al.36,37

Through the analysis of the results of follow-up, we found that the newly diagnosed DPN was related to the type of diabetes, BWI, inflammatory indexes, and lipid metabolism-related indexes. And the result of logistic regression analysis confirmed that the type of diabetes and NLR level were powerful indicators of risk of developing newly diagnosed DPN after adjusted other variables. Compared with type 2 diabetes, patients with type 1 diabetes have a higher risk. While NLR value could be an effective index to predict DPN in the future.

There are some limitations in this study. For the T2DM with DPN group, it has a higher level of NLR compared with the other three groups, while the T1DM with DPN group has a higher level of PLR compared with the other three groups. The relationship between the inflammatory mechanism of diabetic peripheral neuropathy and different types of diabetes is worthy of further study. In addition, there are limitations in sample size, single center, and lack of long-term clinical observation.

Our results show that the T1DM patients who has a higher level of PLR is more likely to develop into DPN, while T2DM patients who has a higher level of NLR is more likely to develop into DPN. NLR and PLR could be used as predictors to help clinicians screening for DPN in different types of diabetes. In this study, we also found that type 1 diabetes is more likely to develop DPN in the future. For type 1 diabetes, if patients who were without DPN had higher NLR level, the risk of developing DPN in the future will be greatly increased.

Data Sharing Statement

We will share the relevant data of the paper on the website of Chinese Clinical Trial Registry within six months to one year after the paper is published.

Acknowledgment

This study was supported by the Fundamental Research Funds for the Central Universities, No. WK9110000036 (to YBW); Natural Science Foundation of Anhui Province, China, No. 1608085MH209 (to YBW); Fundamental Research Funds for the Central Universities, No. WK9110000114 (to JW).

Disclosure

The authors report no conflicts of interest in this work.

References

1. Fukuhara A, Matsuda M, Nishizawa M, et al. Visfatin: a protein secreted by visceral fat that mimics the effects of insulin. Science. 2005;307(5708):426–430. doi:10.1126/science.1097243

2. Steppan CM, Bailey ST, Bhat S, et al. The hormone resistin links obesity to diabetes. Nature. 2001;409(6818):307–312. doi:10.1038/35053000

3. Pop-Busui R, Boulton AJM, Feldman EL, et al. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–154. doi:10.2337/dc16-2042

4. King GL. The role of inflammatory cytokines in diabetes and its complications. J Periodontol. 2008;79(8s):1527–1534. doi:10.1902/jop.2008.080246

5. Korniluk A, Koper-Lenkiewicz OM, Kamińska J, Kemona H, Dymicka-Piekarska V. Mean Platelet Volume (MPV): new perspectives for an old marker in the course and prognosis of inflammatory conditions. Mediators Inflamm. 2019;9213074:2019.

6. Poz D, De Falco E, Pisano C, et al. Diagnostic and prognostic relevance of red blood cell distribution width for vascular aging and cardiovascular diseases. Rejuvenation Res. 2019;22(2):146–162. doi:10.1089/rej.2018.2094

7. Kim YC, Song JE, Kim EJ, et al. A simple scoring system using the red blood cell distribution width, delta neutrophil index, and platelet count to predict mortality in patients with severe sepsis and septic shock. J Intensive Care Med. 2019;34(2):133–139. doi:10.1177/0885066618787448

8. Montagnana M, Danese E. Red cell distribution width and cancer. Ann Trans Med. 2016;4(20):399. doi:10.21037/atm.2016.10.50

9. Afari ME, Bhat T. Neutrophil to lymphocyte ratio (NLR) and cardiovascular diseases: an update. Expert Rev Cardiovasc Ther. 2016;14(5):573–577. doi:10.1586/14779072.2016.1154788

10. Buyukkaya E, Karakaş MF, Karakaş E, et al. Correlation of neutrophil to lymphocyte ratio with the presence and severity of metabolic syndrome. Clin Appl Thromb/Hemost. 2012.

11. Diem S, Schmid S, Krapf M, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176–181. doi:10.1016/j.lungcan.2017.07.024

12. American Diabetes Association. 2. classification and diagnosis of diabetes: standards of medical care in Diabetes—2018. Diabetes Care. 2018;41(Supplement 1):S13–s27. doi:10.2337/dc18-S002

13. Tesfaye S, Boulton AJM, Dyck PJ, et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care. 2010;33(10):2285–2293. doi:10.2337/dc10-1303

14. Iqbal Z, Azmi S, Yadav R, et al. Diabetic peripheral neuropathy: epidemiology, diagnosis, and pharmacotherapy. Clin Ther. 2018;40(6):828–849. doi:10.1016/j.clinthera.2018.04.001

15. Xia P, Kramer RM, King GL. Identification of the mechanism for the inhibition of Na+, K(+)-adenosine triphosphatase by hyperglycemia involving activation of protein kinase C and cytosolic phospholipase A2. J Clin Invest. 1995;96(2):733–740. doi:10.1172/JCI118117

16. Vareniuk I, Pavlov IA, Obrosova IG. Inducible nitric oxide synthase gene deficiency counteracts multiple manifestations of peripheral neuropathy in a streptozotocin-induced mouse model of diabetes. Diabetologia. 2008;51(11):2126–2133. doi:10.1007/s00125-008-1136-3

17. El Kebir D, Filep JG. Targeting neutrophil apoptosis for enhancing the resolution of inflammation. Cells. 2013;2(2):330–348. doi:10.3390/cells2020330

18. Rajakariar R, Lawrence T, Bystrom J, et al. Novel biphasic role for lymphocytes revealed during resolving inflammation. Blood. 2008;111(8):4184–4192. doi:10.1182/blood-2007-08-108936

19. Gibson PH, Cuthbertson BH, Croal BL, et al. Usefulness of neutrophil/lymphocyte ratio as predictor of new-onset atrial fibrillation after coronary artery bypass grafting. Am J Cardiol. 2010;105(2):186–191. doi:10.1016/j.amjcard.2009.09.007

20. Tamhane UU, Aneja S, Montgomery D, et al. Association between admission neutrophil to lymphocyte ratio and outcomes in patients with acute coronary syndrome. Am J Cardiol. 2008;102(6):653–657. doi:10.1016/j.amjcard.2008.05.006

21. Sawant AC, Adhikari P, Narra SR, et al. Neutrophil to lymphocyte ratio predicts short- and long-term mortality following revascularization therapy for ST elevation myocardial infarction. Cardiol J. 2014;21(5):500–508. doi:10.5603/CJ.a2013.0148

22. Moore MM, Chua W, Charles KA, Clarke SJ. Inflammation and cancer: causes and consequences. Clin Pharmacol Ther. 2010;87(4):504–508. doi:10.1038/clpt.2009.254

23. Azab B, Daoud J, Naeem FB, et al. Neutrophil-to-lymphocyte ratio as a predictor of worsening renal function in diabetic patients (3-year follow-up study). Ren Fail. 2012;34(5):571–576. doi:10.3109/0886022X.2012.668741

24. Duman TT, Aktas G, Atak BM, et al. Neutrophil to lymphocyte ratio as an indicative of diabetic control level in type 2 diabetes mellitus. Afr Health Sci. 2019;19(1):1602–1606. doi:10.4314/ahs.v19i1.35

25. Kawamoto R, Ninomiya D, Kikuchi A, et al. Association of neutrophil-to-lymphocyte ratio with early renal dysfunction and albuminuria among diabetic patients. Int Urol Nephrol. 2019;51(3):483–490. doi:10.1007/s11255-018-02065-2

26. Fawwad A, Butt AM, Siddiqui IA, et al. Neutrophil-to-lymphocyte ratio and microvascular complications in subjects with type 2 diabetes: pakistan’s perspective. Turk J Med Sci. 2018;48:157–161. doi:10.3906/sag-1706-141

27. Jabeen F, Fawwad A, Rizvi HA, Alvi F. Role of platelet indices, glycemic control and hs-CRP in pathogenesis of vascular complications in type-2 diabetic patients. Pak J Med Sci. 2013;29(1):152–156. doi:10.12669/pjms.291.2592

28. Thomas G, Pichler M, Belaj K, et al. Platelet-to-lymphocyte ratio: a novel marker for critical limb ischemia in peripheral arterial occlusive disease patients. PLoS One. 2013;8(7):e67688. doi:10.1371/journal.pone.0067688

29. Sunbul M, Gerin F, Durmus E, et al. Neutrophil to lymphocyte and platelet to lymphocyte ratio in patients with dipper versus non-dipper hypertension. Clin Exper Hypertens. 2014;36(4):217–221. doi:10.3109/10641963.2013.804547

30. Mineoka Y, Ishii M, Hashimoto Y, et al. Platelet to lymphocyte ratio correlates with diabetic foot risk and foot ulcer in patients with type 2 diabetes. Endocr J. 2019;66(10):905–913. doi:10.1507/endocrj.EJ18-0477

31. Duan S, Sun L, Zhang C, et al. Association of platelet-to-lymphocyte ratio with kidney clinicopathologic features and renal outcomes in patients with diabetic kidney disease. Int Immunopharmacol. 2021;93:107413. doi:10.1016/j.intimp.2021.107413

32. Stocker R, Yamamoto Y, McDonagh AF, Glazer AN, Ames BN. Bilirubin is an antioxidant of possible physiological importance. Science. 1987;235(4792):1043–1046. doi:10.1126/science.3029864

33. Kwak JY, Takeshige K, Cheung BS, Minakami S. Bilirubin inhibits the activation of superoxide-producing NADPH oxidase in a neutrophil cell-free system. Biochim Biophys Acta. 1991;1076(3):369–373. doi:10.1016/0167-4838(91)90478-I

34. Son SM. Reactive oxygen and nitrogen species in pathogenesis of vascular complications of diabetes. Diabetes Metab J. 2012;36(3):190–198. doi:10.4093/dmj.2012.36.3.190

35. Kim ES, Lee SW, Mo EY, Moon SD, Han JH. Inverse association between serum total bilirubin levels and diabetic peripheral neuropathy in patients with type 2 diabetes. Endocrine. 2015;50(2):405–412. doi:10.1007/s12020-015-0583-0

36. Liu S, Zheng H, Zhu X, et al. Neutrophil-to-lymphocyte ratio is associated with diabetic peripheral neuropathy in type 2 diabetes patients. Diabetes Res Clin Pract. 2017;130:90–97. doi:10.1016/j.diabres.2017.05.008

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[drthindhomeo]

COVID and CBC | Dr. Thind’s Homeopathy

What is CBC? What is the relation between COVID and CBC? Why is the test done? Everything around this, that you should know, in this post and of course our doctors always there to help you.

What is CBC?

The complete blood count (CBC) is a group of tests that evaluate the cells that circulate in the blood which includes red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs). The CBC can evaluate the overall health of a person and detect a variety of diseases and conditions, such as infections, anemia, and leukemia, etc. The Blood cells are produced and then, they mature in the bone marrow primarily and under normal circumstances are released into the bloodstream as required.

What is the function of RBC’s, WBC’s, and Platelets?

The three types of cells that can be evaluated by the CBC include:

1) RED BLOOD CELLS: Red blood cells are also known as erythrocytes, are produced in the bone marrow and then released into the bloodstream when they mature. They contain hemoglobin. Hemoglobin is a protein that transports oxygen throughout the body. Many conditions can affect RBCs, such an example is Anemia which results from low red blood cell counts and low hemoglobin. Other diseases can also lead to anemia, so additional tests are often required to determine the cause.

2)WHITE BLOOD CELLS: White blood cells are also known as leukocytes. These are the cells that exist in the blood, the lymphatic system, and tissues and are an important part of the body’s natural defense (immune) system. These cells help in protecting against infections and also have a role in inflammation and allergic reactions. There are five different types of WBCs and each type has a different function. They include neutrophils, lymphocytes, basophils, eosinophils, and monocytes.

3)PLATELETS: Platelets are also known as thrombocytes. Platelets are the tiny cell fragments that circulate in the blood and are essential for normal blood clotting. When there is an injury and the bleeding begins, platelets help to stop the bleeding by adhering to the injury site and clumping together to form a temporary plug. They also release chemical signals that attract and help to promote the clumping of additional platelets and eventually become part of a stable blood clot at the site of the injury that remains in place until the injury heals. Therefore, platelets help to prevent the body from bleeding and bruising easily. This test is usually performed to check for a blood infection.

Which tests are included in a Complete blood count test?

A standard CBC includes: 1)Red blood cell (RBC) tests: Red blood cell tests further include: a) Red blood cell (RBC) count b)Hemoglobin c)Hematocrit measures the percentage of your total blood volume that consists of red blood cells. d)Red blood cell indices also provide information on the physical features of the RBCs: * Mean corpuscular volume (MCV) is a calculated measurement of the average size of the red blood cells. * Mean corpuscular hemoglobin (MCH) is a calculated measurement of the average amount of hemoglobin inside the red blood cells. * Mean corpuscular hemoglobin concentration (MCHC) is a calculated measurement of the average concentration of hemoglobin inside the red blood cells.

2) White blood cell (WBC) tests: White blood cell tests further include: a)White blood cell (WBC) count b) White blood cell differential: The WBC differential identifies and counts the number of the five types of white blood cells present, that are-neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

3) Platelet tests: Platelet tests further include: a) Platelet count b) Mean platelet volume (MPV) may be reported with a CBC. MPV is a calculated measurement of the average size of platelets.

How does Covid 19 affect the Complete blood count?

COVID-19 is a systemic infection with a significant impact on the hematopoietic system and hemostasis. Hematopoietic changes show decreased hemoglobin levels. The mean corpuscular volume (MCV) was also lower in adult COVID-19 patients and the mean corpuscular hemoglobin concentration (MCHC) was significantly higher in COVID-19 patients as compared to healthy individuals. This is most likely due to a decrease in hemoglobin levels. White blood cell numbers seem to be normal or decreased in COVID-19 patients and to increase with disease progression with some severe cases having leukocytosis. The neutrophil-lymphocyte-ratio (NLR) seems to be increased in patients with severe COVID-19. Lymphopenia may be considered as a cardinal laboratory finding, with prognostic potential. The platelet-lymphocyte-ratio (PLR) has also been considered as a parameter that indicates the severity of the infection. Platelets are normal or decreased in non-severe patients and significantly decreased in severe patients. Blood hypercoagulability is seen as common among hospitalized COVID-19 patients. In conclusion, hemocytometric changes, especially the presence of lymphopenia and increased neutrophil-lymphocyte-ratio, in patients infected with the SARS-CoV-2 virus may help in diagnosing and predicting disease progression of COVID-19.

This article was originally published on www.drthindhomeopathy.com

[biomedgrid]

Biomed Grid | A General Outlook at the Pathogenesis of COPD

Introduction

Chronic obstructive pulmonary disease (COPD) is a pathological disorder characterized by deregulated chronic inflammation of the airways and persistent airflow obstruction [1,2], which lead to emphysematous destruction of lung tissue and deterioration of the pulmonary function [3]. The characteristics of COPD include infiltration of neutrophils, macrophages, B and T lymphocytes, and dendritic cells that release inflammatory cytokines, proteases, and growth factors responsible for the structural changes in the lung. It also comprises mucociliary dysfunction, apoptosis, and structural changes in the airways causing emphysema, and extrapulmonary systemic effects [4], with a calculated prevalence of 10% in over forty years old adults. Clinical features of COPD include persistent pulmonary inflammation, obstructive bronchiolitis, chronic bronchitis, emphysema and loss of alveolar tissue. Conventional therapies work only as palliative treatments. Respiratory infections increase of cardiovascular risk, lung cancer, pulmonary hypertension, and depression are some of the most common complications caused by this disorder. COPD is a significant leading cause of deaths worldwide, and by 2020, it is expected to be the third leading cause of deaths worldwide [5]. Cigarette smoking is the major etiologic and risk factor for COPD [6]. Aging is considered a critical factor in the development of COPD [7]. It’s been shown that the incidence of COPD increases with aging, with a peak observed in patients aged 65-74 years [8,9].

The pathogenesis of COPD remains largely unclear. It is known that emphysema is characterized by the presence of humoral and cellular autoimmune responses against elastin [10], and bronchial smooth muscle cell hyperplasia [11], showing evidence for a role of autoimmunity in COPD pathogenesis, with a prominent role of inflammation. Dysregulated inflammation, dysfunction in airway smooth muscle (ASM), imbalance in the proteolysis/ antiproteolysis equilibrium and in the repair process, and different epigenetic mechanisms –including DNA methylation, decreased levels of histone deacetylases and reduced microRNAs levels– are mechanisms that contribute to COPD pathophysiology [12]. Inflammatory cells influence cell destruction, hyperplasia of smooth muscle cells, and subepithelial fibrosis seen in COPD. It has been hypothesized that inflammatory cells infiltrate the bronchial mucosa and lung parenchyma in COPD lungs, affecting the airway destruction and remodeling by secreting enzymes and inflammatory cytokines or by indirect interference regulating other cellular functions [13], promoting tissue damage and reconstruction. However, the mechanism of participation of different inflammatory mediators is not completely known. Currently, no good biomarkers of remodeling are available, and imaging techniques are not sensitive enough to directly visualize the remodeling changes in the airways.

Cytokines have a controversial and sometimes opposite role in COPD; the elimination of a single one may further deregulate the inflammatory process. Cells and mediators of immunity, and reactive oxygen species (ROS) also contribute to inflammation [14]. Innate and adaptive immune systems are involved in the development of chronic inflammation leading to COPD. The role of innate immunity in COPD pathogenesis has been implicated in the induction and acute exacerbation [15,16], with Interleukin (IL) 1-like cytokines increased in COPD patients suggesting the role of inflammasomes –intracellular multiproteines complexes that activates proinflammatory caspases– in the pathogenesis of COPD that lead to the production of IL-1β and IL-18 in response to pathogenesis [15,16]. Innate immune cells recognize microbial pathogens or damage-associated molecular patterns or recognition of innate immune cells, which activate the inflammasome through several families of pattern recognition receptors (PRRs) expressed in innate immune cells, including Toll-like receptors (TLRs), nucleotide-binding domain leucine-rich repeat-containing receptors (NLRs), C-type lectin receptors, and RIG-I-like receptors [15,16]. TLRs are expressed on alveolar macrophages, lymphocytes, dendritic cells, and bronchial epithelial cells [15,16].

COPD exacerbations are defined as sustained worsening of a patient’s condition associated with respiratory –dyspnea and productive cough– and non-respiratory –fatigue and malaise– symptoms [17] beyond normal day-to-day variations that is acute in onset. Several biomarkers of inflammation can help to identify exacerbations most likely to respond to oral corticosteroids and antibiotics, and patients with a frequent exacerbation phenotype, which requires a preventative treatment [18]. While mild exacerbations can be managed with inhaled bronchodilators, for worsening symptoms, while moderate exacerbations need treatment with antibiotics and/or corticosteroids, and severe exacerbations require hospitalization [17]. The heterogeneity in COPD clinical manifestations, outcomes, and responses to treatment [19] is useful to classify COPD into specific phenotypes. Many exacerbations of COPD involve bacterial or viral respiratory infections [20]. Accelerated cell senescence and insufficient autophagy –significantly deregulated in the cells from COPD patients– increase the accumulation of damaged cells [21]. Cellular senescence has been widely implicated in the pathogenesis of COPD, presumably by impairing cell repopulation and by aberrant cytokine secretion in the senescence-associated secretory phenotype. Autophagy is a process of lysosomal self-degradation that maintains a homeostatic balance between the synthesis, degradation, and recycling of cellular proteins.

Peripheral blood eosinophil count was shown to be a valid biomarker for sputum eosinophil-associated exacerbations [22]. Patients with COPD and evidence of eosinophilic airway inflammation respond well to corticosteroid therapy [23]. C-reactive protein (CRP) is another potentially useful biomarker for predicting which exacerbations may benefit from antibiotic therapy and for selecting those that might resolve without antibiotic intervention [24]. Procalcitonin may not be sufficiently sensitive for use as a biomarker for response to antibiotics in patients with COPD exacerbations [24]. Therefore, the use of CRP as a biomarker could help to avoid unnecessary use of antibiotic therapy that can lead to adverse effects and development of bacterial resistance [25]. In addition to bronchodilators, macrolide azithromycin, taken daily for 1 year in addition to usual therapy, demonstrated a significant reduction in the risk of exacerbations in patients with COPD at increased risk of exacerbation [26]. Furthermore, there is evidence that pneumococcal and annual influenza vaccinations reduce the risk of exacerbation and hospitalization in patients with COPD [27-29]. Other mechanisms further upregulated during exacerbations –triggered by infectious pathogens, air pollution or second-hand smoke– include amplification of inflammatory process.

Conclusion

Inflammation plays a key role in the development of COPD. Other mechanisms such as senescence, autophagy or repair process are dysregulated in COPD. Despite all the current knowledge, there is still a long way to go to further unveil the pathogenesis of COPD, and find useful biomarkers for diagnosis, response to treatments and outcome.

[kamounlab]

Ten things we learned in 2010-2019 (aside from everything else)

He who has studied himself is his own master. –Sri Lankan proverb.

By the time this gets posted, you’ll probably be sick and tired of all those retrospective articles looking back at the 2010-2019 decade. I feel your pain. But hey, we’re still early in the new decade and I have a good reason for writing this. This last decade has been such an exhilarating period of exploration and discovery for me, my team and my collaborators that I just can’t resist the urge to write this post. The decade took us through unexpected research paths that I would have never imagined ten years ago. As I’m drafting these words during my holidays break in Sri Lanka—in between tasting the local milk rice curries and soaking the soft Indian ocean December sunshine—I’m reflecting on the local proverb above and I’m using it as my lame excuse to offer you yet another list of decadal achievements.

Please note that this is my personal highly biased perspective on ten things we have learned in 2010-2019. This list is by no means meant to be comprehensive review of advances in our research field but rather a reflection of my own personal take on the scientific topics we investigate.

2010. Two-speed genomes, everywhere? What started as a loose metaphor inspired from economics went sort of viral at some point in this decade, sometimes to comical effects (one speed genome anyone?). To those who struggle with metaphors, the idea is that there is an uneven distribution in the rates of gene evolution across the genome, not that there are precisely two-rates of gene evolution. The term actually dates back to 2009 press releases associated with the Haas et al. paper on the genome of Phytophthora infestans. We noted at the time that it was a catchy term that does illustrate the point we were making, and I really liked the French translation into the poetic “génome à deux vitesses”. By 2010, we were confident enough that we formally used the term in the Raffaele et al. paper on genome evolution after host jumps. What was even more exciting about the “two-speed genome” concept is that it turned out to apply not just to Phytophthora genomes but also to many other plant pathogens. Ten years later, we start the new decade with a paper on the two-speed genome architecture of the blast fungus Magnaporthe oryzae.

2011. WY fold—commonalities amid diversity. Our collaboration with Mark Banfield started yielding its fruits with the 2011 Boutemy et al. paper. This and related papers by the Staskawicz and Shirasu Labs in the US and Japan, respectively, marked the discovery of the WY-fold of oomycete effectors. This has now expanded into the LWY-foldand an effector of the tobacco blue mold pathogen Peronospora tabacina has 18 WY units. The finding that pathogen effectors share structural features despite limited primary sequence similarity has also extended to other filamentous pathogens, for example the MAX-fold of M. oryzae effectors. This is very useful because it improves prediction of effector genes from pathogen genomes and sets the stage for effectoromics.

2011. The haustorial interface—where it all happens? I’m a big fan of this Bozkurt et al. paper because it was very challenging for me to get outside my comfort zone into the murky world of plant cell biology (where many people seem reluctant to quantify their observations…). Kudos to Tolga Bozkurt and Sebastian Schornack for leading the way and taking me through this journey. As often, the effectors gave us the first clue and the discovery that some P. infestans effectors accumulate at the haustorial interface (perihaustorial) turned out to be a starting point for many cool projects. Thanks in part to a nudge from an anonymous reviewer who was dissing the novelty of studying effectors that suppress PAMP-triggered immunity (the “it has all been done with Pseudomonas syringae” type of reviewer), we decided to focus on perihaustorial effectors. This resulted, in many important findings, notably the discovery of the ATG8-binding effector PexRD54 and that the host autophagy machinery is diverted to the haustorial interface during infection by P. infestans. This also led us to study plant ATG8 proteins and how they have specialized throughout evolution.

2013. Genome editing made easy. Ten years ago, geneticists were dreaming about gene editing. What if there was a tool that would allow facile gene editing. TALENs popped up first in 2009 but, in our hands, applying them turned out to be anything but simple. Vlad Nekrasov noted that the AvrBs3 backbone of standard TALEN constructs wouldn’t generate transgenic tomatoes because they elicit Bs4-mediated cell death. That frustration was one motivation in early 2013 to ditch the TALEN work and focus on the newly reported CRISP/Cas9 system. That was a wise decision and Vlad got CRISPR/Cas9 to work in what seemed like weeks. The rest is history with Vlad’s CRISPR/Cas9 plasmids have been distributed >500 times via Addgene. Vlad, in collaboration with Detlef Weigel’s lab, went on to engineer the transgene-free powdery mildew resistant mutant Tomelo in less than a year. This work ended up being highlighted by the BBC as one of “four good things that happened in 2016″.

2013. Field pathogenomics—just sequence it! It was the ash dieback outbreak that gave us our first opportunity to combine sequencing of field collected tissue with open science and crowdsourcing to mount a rapid response to plant health emergencies. Back then it did feel like plant pathology was lagging behind in immediately applying genome sequencing to emerging plant pathogens. Diane Saunders, Kentaro Yoshida and Dan MacLean managed to put OpenAshDieback together and release a draft of the pathogen’s genome just weeks after the outbreak was detected in Norfolk. Diane then applied the approach to yellow rusts and we later used field pathogenomics to identify the origin of the pathogen that caused the 2016 wheat blast outbreak in Bangladesh. That project kicked off a very inspiring collaboration with Tofazzal Islam and Nick Talbot and further strengthened my dedication to advocate for open science. It also changed the research direction of my lab, especially after the BLASTOFF project was funded by the ERC.

2013. Going back to the past to better prepare for the future. It’s not every day that you get lampooned by the Colbert Report. Stephen Colbert was correct, it wasn’t the 1b haplotype of P. infestans that triggered the Irish famine disaster, it was HERB-1. Our collaboration with Hernan Burbano, Detlef Weigel and several others on sequencing P. infestans genomes from 19th century herbarium samples, received incredible media coverage. With Hernan having recently started a new position at UCL, you can expect more pathogen aDNA projects in the future. Stay tuned.

2014. Effector adaptation after jumping hosts. There are literally dozens and dozens of examples of rapid evolutionary adaptations in plant-pathogen interactions in which the precise mutation is known. It's no big deal to find a new one these days. But almost all of these are AVR effectors that overcome host resistance. What Suomeng Dong and others documented is an effector that has adapted to a new target after switching hosts. Suomeng showed that the protease inhibitor effector EPIC1 has undergone biochemical specialization on the protease of its new host. This paper builds up on work dating back to the 2000-2009 decade by PhD students Miaoying Tian who discovered the protease inhibitor effectors of Phytophthora and Jing Song who further studied the EPICs. It was also the point when we decided to center the lab around the theme of evolutionary molecular-plant microbe interactions or #EvoMPMI as it’s known on Twitter.

2015. The beauty of a protein complex structure. Stella Cesari and her colleagues deserve much credit for articulating the NLR integrated decoy concept, although some of us prefer to use the more agnostic term integrated domain (NLR-ID). I’m thrilled to have been the matchmaker who helped link up the amazing work of Ryohei Terauchi on rice blast effectors and R genes with the structural biology magic of Mark Banfield. This resulted in bringing an unprecedented level of detail to Harold Flor's gene-for-gene model with Abbas Maqbool solving the structure of M. oryzae AVR-PikD in complex with the integrated HMA domain of the rice immune receptor Pik-D. Mark and his team went on to publish a series of trail blazing follow-up papers on how to exploit this knowledge to engineer new disease resistance specificities (De la Concepcion et al. 2018, 2019; Varden et al. 2018).

2017. Do NLRs work in pairs—it’s more complicated! In what was initially a follow-up study to the AVRblb2 project of Bozkurt et al., Chih-hang Wu, Ahmed Abd-El-Haliem and Jack Vossen “accidental” discovery that NRC4 is necessary for Rpi-blb2 ended up having some very unexpected ramifications. Chih-hang’s PhD took quite a turn when he followed up on a suggestion by Khaoula Belhaj to silence multiple NRC paralogs and uveil a complicated NLR network. He went on to his most insightful discovery that the NRC network is phylogenetically structured and has expanded over 100 million years ago (Mya) from an NLR pair to a network that makes up to half of the NLRs of asterid plants. All this cool stuff ended up taking over my research program by storm, with Team NRC making up half of my lab. It also led to the fascinating research question of how NLRs have evolved from singletons to pairs to networks. Meanwhile, Chih-hang is starting his new lab at Academia Sinica in January 2020.  

2019. The coming of age of the plant resistosome. Courtesy of Jijie Chai, Jian-Min Zhou and their collaborators, 2019 brought us a full-length NLR structure some 25 years after their discovery in the early 1990s. But these landmark papers by Wang et al. (2019a, 2019b) had much more than that. They showed that they could activate the ZAR1 resistosome in vitro by flooding it with ATP. This results in the “death switch”, a conformational change that generates a funnel-shaped structure that is proposed to insert into the plasma membrane and cause cell death. Beyond this extraordinary breakthrough, we had good reasons to celebrate—as we did in this video. The ZAR1 death switch model immediately explained some two-year old results that Hiroaki Adachi and Adeline Harant had produced with our own NRC4. This led Aki to discover the functionally conserved N-terminal MADA motif of NLR proteins that defines the N-terminus of NRC4, ZAR1 and at least one fifth of CC-type NLRs. We predict that a ZAR1 type conformational “death switch” is a common activation mechanism for CC-NLRs. What a way to end the decade. IT'S A MADA, MADA, MADA, MADA WORLD!

Conclusion. Over the last decade, the research topics in my lab have drifted from a focus on Phytophthoragenome and effector biology to new interests such as M. oryzae and NLR biology. I heard that several colleagues find this puzzling. Some of the drift can be explained by a tendency to follow Peter Medawar’s maxim of “science is the art of the soluble”. Another reason is an obsession with Keplerian thinking—unexpected findings are opportunities to explore new research avenues and shouldn’t be dismissed because they don’t fit the current theory. Also, some of the projects moved on to greener pastures when postdocs took on independent positions at other institutions and it didn’t make any sense for me to continue working on those topics. This said, there is probably more to this willingness to jettison projects and switch to new ones. I should think deeper about this. After all, “he who has studied himself…”

Acknowledgements. I’m deeply grateful to past and present lab members and collaborators for their many contributions, several of which are not described here. I want to particularly thank Joe Win for his across the board involvement in pretty much most of the projects described above. Thanks also to the funders, particularly the Gatsby Charitable, BBSRC and ERC.

To cite: Kamoun, S. Ten things we learned in 2010-2019 (aside from everything else). Zenodo. http://doi.org/10.5281/zenodo.3613856