Reticulon 3 (RTN3) is an endoplasmic reticulum protein that has previously been shown to play roles in neurodegenerative diseases, but little is known about its function in the kidneys. The aim of the present study was to clarify the roles of RTN3 in chronic kidney disease (CKD) and kidney fibrosis. In this study, RTN3 levels were measured in kidney tissues from healthy controls and CKD or kidney fibrosis patients. An RTN3-null mouse model was generated to explore the pathophysiological roles of RTN3 in the kidneys. The underlying mechanisms were studied in primary proximal tubular epithelial cells and HEK293 cells in vitro. The results showed that (1) a reduction in RTN3 in mice induces CKD and kidney fibrosis; (2) decreased RTN3 expression is found in patients with CKD; (3) RTN3 plays critical roles in regulating collagen biosynthesis and mitochondrial function; and (4) mechanistically, RTN3 regulates these phenotypes by interacting with GC-Rich Promoter Binding Protein 1 (GPBP1), which activates the IGF2-JAK2-STAT3 pathway. Our study indicates that RTN3 might play crucial roles in CKD and kidney fibrosis and that a reduction in RTN3 in the kidneys might be a risk factor for CKD and kidney fibrosis. A protein (RTN3) known to be involved in neurodegenerative diseases may play a causative role in kidney fibrosis or scarring, and chronic kidney disease (CKD). An estimated 20% of CKD cases may have genetic causes and identifying the genes involved may help find better treatments. Ri-Qiang Yan at the University of Connecticut Health, Farmington, USA, and Rong Xian at Central South University, China, noticed that mice in which the gene coding for RTN3 was inactivated had kidney fibrosis. The researchers showed that RTN3 levels were also lower in kidney tissues of patients with CKD than in healthy individuals and that RTN3 levels were inversely proportional to disease progression. Further investigation showed that decreased RTN3 caused extra collagen deposition and misshapen mitochondria, the cellular powerhouses, in the kidney. These results identify a potential novel risk factor for CKD. It affects 10–15% of the population worldwide and is now recognized as the most rapidly increasing contributor to the global burden of disease. The costs related to CKD and end-stage renal disease  are an enormous burden for all healthcare systems around the world2. Usually, CKD does not cause symptoms until it reaches an advanced stage. At the middle and late stages of CKD, edema, fatigue, vomiting, loss of appetite, or confusion may develop3. In the clinic, the recommended diagnosis and testing procedures for CKD include blood pressure measurement, urine testing, and serum creatinine measurement2. Although many early-onset CKD cases were not previously considered to be of genetic origin, studies in recent years have discovered that approximately 20% of CKD cases may be associated with genetic factors4. Thus far, more than 200 candidate genes, including collagen-related genes, mitochondria-related genes, and ion channel-related genes, have been identified for 70% of CKD cases associated with genetic factors4.Chronic kidney disease (CKD) is a type of kidney disease in which there is gradual loss of kidney function over a period of months or years5. Biochemically, RTNs can shape the structure of the tubular endoplasmic reticulum (ER) due to the ω- (wedge-shaped) membrane topology in the N- and C-terminal domains6. Functionally, RTNs have been found to regulate neurite outgrowth and negatively modulate the activity of Alzheimer’s β-secretase, and they are pathologically linked to axonopathy in hereditary spastic paraplegias9. Interestingly, the functions of RTNs in human peripheral organs are still not clear. Previously, we found that increased RTN3 can lead to obesity and hypertriglyceridemia via interaction of RTN3 with heat shock protein family A (Hsp70) member 510. However, it has not been established whether RTN3 expression has any effects on kidney disease.The reticulon (RTN) protein family consists of RTN1 through RTN4 in mammals, and its members have a signature C-terminal RTN homolog domain (RHD)In this study, by employing hematoxylin and eosin (HE) staining, periodic acid-Schiff (PAS) staining, picrosirius red staining, and Masson staining, we noted kidney fibrosis, including glomerulosclerosis and tubulointerstitial fibrosis, in RTN3-knockout (RTN3-null) mice at 13 months of age. Serum and urine analyses revealed that the levels of creatinine (CR) and urea nitrogen (BUN) in serum and microalbumin (mALB) in urine were overtly increased in RTN3-null mice compared to WT mice at 13 months. We then collected 29 kidney samples from different stages of CKD and four kidney biopsy samples from healthy people (kidney contusion patients). Immunohistochemistry (IHC) found that ~37.5% of CKD patients showed low expression of RTN3 in the kidneys. Combined with PAS and HE staining, these analyses revealed that the expression of RTN3 was inversely proportional to CKD progression. Additional functional studies showed that both the glomerular basement membrane and mitochondria were misshaped in the kidneys of RTN3-null mice. Reducing the expression of RTN3 activated the IGF2-JAK2-STAT3 pathway by altering the localization of GC-rich promoter binding protein 1 (GPBP1), increasing the expression of collagen and disrupting the structure and function of mitochondria, which ultimately resulted in CKD and kidney fibrosis. Hence, our study indicates that RTN3 may play a crucial role in CKD and kidney fibrosis and that a reduction in RTN3 in the kidneys may be a risk factor for CKD and kidney fibrosis.11. C57BL/6J mice were purchased from the Chinese Academy of Sciences  and bred in the Department of Zoology, Central South University. Unilateral ureteral obstruction (UUO) was induced in male mice at 7–8 weeks of age. Briefly, after anesthetization with pentobarbital sodium, the left ureter of each mouse was obstructed by two-point ligation with silk. The control mice underwent a sham operation without ligation of the left ureter. The mice were euthanized 7 days after UUO, and the kidneys were harvested for subsequent analysis.RTN3-null mice were generated, and genotyping was performed as described previouslyg for 10 min; (5) the cell pellet was resuspended in medium selective for epithelial cell growth; and (6) the cells were seeded onto 1% gelatin-coated tissue culture plates and incubated at 37 °C with 5% CO2.Primary proximal tubular epithelial cells were isolated from mouse kidneys as follows: (1) the kidneys were decapsulated and bisected, and the medulla was removed; (2) the remaining cortices were finely chopped using a scalpel and digested in 1 mg/ml collagenase type-II at 37 °C for 10 min; (3) the kidney digest was passed through a series of brass sieves with progressively smaller mesh openings; (4) cells were collected from the 40 µm nylon mesh and spun at 150 × 2–controlled atmosphere in DMEM supplemented with 10% fetal bovine serum, 50 IU/mL penicillin, 50 mmol/L streptomycin, and glutamine.The HEK293 cell line was purchased from the Cell Bank of the Shanghai Institutes for Biological Sciences  and maintained at 37 °C in a humidified, 5% COThe study protocol was approved by the Review Board of Central South University in China.The studies involving human participants and animals were reviewed and approved by the Third Xiangya Hospital of Central South University Ethics Committee . Kidney tissues were collected from puncture or surgical specimens, and healthy kidney tissues were collected from patients with kidney contusions. All patients provided written informed consent.® RNA Mini Kit (12183025), and Maxima SYBR Green/ROX qPCR Master Mix (2×) (K0221) were purchased from Thermo Fisher Scientific. Antibodies against COL4A5 (ab231957) and a creatinine assay kit (ab204537) were purchased from Abcam. A Urea Nitrogen (BUN) Colorimetric Detection Kit (EIABUN) was purchased from Novo Biotechnology Co., Ltd. A mouse mALB ELISA Kit (JL26402) was purchased from Shanghai Jianglai Industrial Limited by Share Ltd. A Hematoxylin-Eosin/HE Staining Kit (G1120), Masson’s Trichrome Stain Kit (G1340), Picrosirius Red (Direct Red 80) Stain Kit (S8060), Glycogen Periodic Acid Schiff (PAS/Hematoxylin) Stain Kit (G1281), Broad Spectrum Immunohistochemistry Kit (SP0041), ATP (BC0300), Reactive Oxygen Species (ROS) Assay Kit (CA1410), Mitochondrial Membrane Potential Assay Kit with JC-1 (M8650), Dual-Lucy Assay Kit (D0010) and Nuclear Protein Extraction Kit (R0050) were purchased from Beijing Solarbio Science & Technology Co., Ltd.The RTN3 antibody was generated in the Yan laboratory. Antibodies against collagen type I (Cat No: 66761-1-Ig), collagen type III (Cat no: 22734-1-AP), GPBP1 (Cat No: 21622), and STAT3 (Cat No: 60199) were purchased from Proteintech Group, Inc. Antibodies against JAK2 (# 3230S), p-JAK2 (# 3771S), and p-STAT3 (# 9145S) were purchased from Cell Signaling Technology. Antibodies against IGF2 (sc-515805) and GAPDH (sc-47724) were purchased from Santa Cruz Biotechnology. Alexa Fluor 488 (A-11008), Alexa Fluor 568 (A-11011), DAPI (62247), a BCA Protein Assay and Analysis Kit (23227), a PureLinkParaformaldehyde-fixed kidney tissue was embedded in paraffin and sliced into 6-μm sections. The sections were stained with an HE staining kit, a Masson staining kit, and a PAS staining kit and examined by routine light microscopy. Staining was performed according to established protocols.Immunohistochemistry experiments were performed with a broad-spectrum immunohistochemistry kit . Kidney tissues from mice and humans were sectioned in the sagittal plane at a thickness of 10 μm with a cryostat after 4% paraformaldehyde fixation and optimal cutting temperature compound embedding.3VO4 to inhibit phosphatase. The homogenates were rotated for 30 min at 4 °C to ensure extraction of membrane proteins. After centrifugation at 15,000 × g for 120 min, the supernatants were collected, and the protein concentrations were measured with bicinchoninic acid protein assay reagent. Nuclear proteins were isolated with a nuclear protein extraction kit according to the manufacturer’s instructions. Equal amounts of protein lysates were resolved by 4–12% Bis-Tris NuPAGE gel electrophoresis followed by standard Western blotting with the antibodies specified above. The chemiluminescence signals were scanned, and the integrated density values were calculated with a chemiluminescence imaging system (Alpha Innotech).For Western blotting, kidney tissues or cells were homogenized on ice in 1% 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate extraction buffer containing complete protease inhibitors  and 0.1 mmol/L NaFor coimmunoprecipitation (co-IP), the kidney tissues of WT mice were lysed, and equal amounts of lysates (500 μg in 1 mL) were used for immunoprecipitation with Protein A + G beads  overnight. The extensively washed immunoprecipitates were resolved by 4–12% NuPage Bis-Tris gel electrophoresis followed by standard Western blotting with the antibodies specified above. The chemiluminescence signals were scanned, and the integrated density values were calculated with a chemiluminescence imaging system (Alpha Innotech).11. After dissection and fixation, each sample was sectioned to generate 70-nm-thick sections with an ultramicrotome  and stained with uranyl acetate and lead citrate. Images were acquired by TEM . Five samples were prepared for each experimental condition.For transmission electron microscopy (TEM), samples were prepared as previously described with modificationsFor confocal imaging, the cultured cells were fixed with 4% paraformaldehyde and treated with 0.5% Triton X-100. The cells were stained with relevant antibodies or using a JC-1 kit and examined under the Leica SP5 platform according to standard methods.Adenosine triphosphate (ATP) assays and reactive oxygen species (ROS) assays were performed using appropriate assay kits. Phosphomolybdic acid colorimetry was employed to detect the levels of ATP. The generation of ROS was determined by fluorometric analysis using 2,7-dichlorofluorescein diacetate. At least 3 independent repeats were made for each indicated cell line.® RNA Mini Kit. The Novogene Bioinformatics Institute  conducted the main part of the RNA-seq and bioinformatics analysis. Real-time PCR validation was carried out in a Fast 7500 Real-Time PCR System (Applied Biosystems) using Maxima SYBR Green/ROX qPCR Master Mix (2×). Mass spectrometric analysis was also conducted at the Novogene Bioinformatics Institute .Total RNA was extracted from mouse kidney tissues with a PureLink(−ΔΔCt) method was used to compare the mRNA expression between the affected individuals and the controls. Each assay was performed in five independent tests.cDNA was synthesized from a total of 1 μg of RNA using a RevertAid First Strand cDNA Synthesis Kit  with oligo(dT) primers. Real-time PCR was carried out in a Fast 7500 Real-Time PCR System (Applied Biosystems) using Maxima SYBR Green/ROX qPCR Master Mix (2×) . The 2The CDS of GPBP1 was cloned into pcDNA3.1 by PCR, and the promoter of IGF2 was cloned into a luciferase plasmid (Luc promoter (IGF2)). Both plasmids were cotransfected into HEK293 cells according to the instructions of the Dual-Lucy Assay Kit. Luciferase activity was detected on a SpectraMax L microplate system.t tests based on ANOVA were used for 2-group comparisons. For real-time PCR analysis, we used the ΔΔCT method. Differences were considered statistically significant at P < 0.05, with significance indicated in the figures as *P < 0.05, **P < 0.01, and ***P < 0.001 (ns represents no significant difference).The data were subjected to statistical analysis with Graph-Pad Prism 5 (GraphPad Software) and plotted with AI Illustrator (Adobe). The results represent the mean ± SEM from at least three independent experiments, as indicated in the figure legends. Two-tailed Student’s 11. We have also reported a specific isoform (RTN3-C) in mouse kidneys11. Hence, we hypothesized that RTN3 may play a crucial role in the development of the kidneys. We collected kidneys from RTN3-null and WT mice maintained on standard chow at three different ages for histology. HE, picrosirius red and PAS staining indicated that, compared to WT mice, RTN3-null mice presented with overt glomerulosclerosis at 8 months of age and developed hyalinosis at 13 months  and three healthy controls (kidney contusion patients). Western blot analysis of kidney tissues showed that the RTN3 levels in CKD patients (four tissues) were much lower than those in healthy controls (two tissues) Fig. . IHC anaWe then selected three biopsy samples with low expression of RTN3 that represented different stages of CKD, as estimated by PAS and Masson staining. Combined with the IHC results, the findings revealed that the lower the expression of RTN3 was, the more advanced the CKD was Fig. . The finMasson staining of RTN3-null mice revealed collagen aggregation in the glomerulus and tubulointerstitium. We then prepared the renal cortices of 13-month-old RTN3-null mice and WT mice for whole-mRNA sequencing. The results suggested that the expression of collagen type I, collagen type III, and collagen type IV was increased dramatically in RTN3-null mice Fig. . KEGG pa13. Real-time PCR and Western blot analysis revealed that the expression of MFN2 and OPA1 was increased in RTN3-null mice compared to WT mice, but the level of FIS1 was not changed  was dramatically increased in the kidneys of RTN3-null mice compared to WT mice Fig. . IGF2 islls Fig. . Activat21. The interaction of GPBP1 with the GC-rich promoter of IGF2 was assessed by dual-luciferase reporter assay in vitro . In addition, upregulation of IGF2 has been proven to lead to Perlman syndrome, a disease related to kidney injury, in mouse nephron progenitor cells15. In proximal tubule cells, increased IGF levels promote fibronectin expression through a pathway involving Nox-dependent ROS generation and Akt signaling32. In our study, the expression of IGF2 was dramatically increased in RTN3-null mice with CKD, which indicated that kidney injury might have occurred via the IGF2 pathway.Insulin-like growth factors (IGF-1 and IGF-2) are necessary for normal growth and development35. Podocyte-specific JAK2 overexpression has been found to accelerate disease progression in a DKD mouse model36, and a STAT3 inhibitor (S3I-201) can attenuate fibrosis and inflammation in UUO kidneys37. In a CKD mouse model, activation of the JAK2-STAT3 pathway has been found to induce oxidative stress and aggravate CKD38. In acute kidney injury and DKD, activation of the JAK2-STAT3 pathway also promotes the immune inflammatory response and induces apoptosis33. Furthermore, administration of JAK2 inhibitors, including baricitinib and Huang Gan formula, effectively improves kidney function in CKD and DKD, which indicates that JAK2 inhibitors might be useful as new therapies for CKD and DKD39.JAK2-STAT3 activation has been detected in several types of kidney disease, such as acute kidney injury, CKD and DKD40. Disruption of mitochondrial structure and function may promote the oxidative stress response42. In our study, we detected disruption of mitochondria in RTN3-null mouse kidneys. Previous studies have revealed that activation of the JAK2-STAT3 pathway may induce mitochondrial dysfunction and oxidative stress by disrupting the balance between BCL2 and BAX44. A specific JAK2 inhibitor can attenuate TNF-α-induced oxidative stress in renal tubular epithelial cells45. Hence, the mitochondrial dysfunction in RTN3-null mouse kidneys described in our current study may also have occurred via the JAK2-STAT3 pathway.Oxidative stress is harmful to cells due to excessive generation of ROS, which has been proven to lead to CKDIn summary, our study suggests that the RTN3-null mouse model might be an ideal model for research on CKD and kidney fibrosis because the model recapitulates features of CKD. Reduced RTN3 expression is a potential risk factor for glomerulosclerosis and tubulointerstitium in the kidney because it promotes collagen synthesis and aggregation and impairs mitochondrial structure and function, partly through alteration of the localization of GPBP1 and activation of the IGF2-JAK2-STAT3 pathway. Hence, our findings shed light on the importance of the relationship between ER proteins and CKD/kidney fibrosis in humans and animals. Collectively, our data suggest that RTN3 is a key molecule in the kidneys.Supplemental Materials


Elements of inflammation are found in almost all chronic ocular surface disease, such as dry eye disease. The chronicity of such inflammatory disease speaks to the dysregulation of innate and adaptive immunity. There has been a rising interest in omega-3 fatty acids to attenuate inflammation. While many cell-based (in vitro) studies verify the anti-inflammatory effects of omega-3, different human trials report discordant outcomes after supplementation. This may be due to underlying inter-individual differences in inflammatory cytokine metabolism ), in which genetic differences might play a role, such as polymorphisms in the lymphotoxin alpha (LT-α) gene. Inherent TNF-α production affects omega-3 response and is also associated with LT-α genotype. Therefore, LT-α genotype might predict omega-3 response. Using the NIH dbSNP, we analyzed the relative frequency of LT-α polymorphisms among various ethnicities, each weighted by the genotype’s probability of positive response. While the probability of response for unknown LT-α genotypes are 50%, there is greater distinction in response rates between various genotypes. Hence, there is value in genetic testing to prognosticate an individual’s response to omega-3. Dry eye disease (DED), characterized by tear film dysfunction, is a disease of the ocular surface characterized by a loss of homeostasis of the tear film . DED is Regardless of the initial precipitating factor, the “final common pathway” in the pathogenesis of DED is localized stress on the corneal surface epithelium , which can lead to the release of inflammatory mediators, progressing to globalized inflammation along the ocular surface, making for an “inflammatory soup” of pro-inflammatory cytokines, lipid mediators, and an influx of proinflammatory cells, which in turn cause further ocular surface damage ,3.Such processes potentiate chronic inflammation and are driven by the immune response, which involve both the ocular mucosa, and in many cases, a systemic immune response. These processes are modelled as the “immune response arc” ,6.In DED, antigens at a tissue site are phagocytosed by antigen-presenting cells (APCs) such as macrophages and dendritic cells, in which the antigen is cleaved and combined with a unique major histocompatibility complex (MHC) class II peptide  to form a peptide-MHC complex on their cell surface membrane. These APCs then migrate to lymph nodes and use their peptide-MHC complex to bind to specific receptors on naïve B and T cells, activating them to undergo clonal expansion and differentiation to form effector T cells (CD4+ and CD8+ T cells) and effector B cells (antibody-secreting plasma cells). CD4+ T helper (Th) cells play important roles in chronic inflammation, with each subset secreting a unique repertoire of cytokines which regulate the immune response and may act synergistically or antagonistically to each other, of which Th1 cells participate in cell-mediated and delayed hypersensitivity reactions via secretion of cytokines such as tumor necrosis factor-alpha (TNF-α) & interferon-gamma (IFN-y) . Th2 celIn health, any inflammation is usually acute, self-limiting, and self-resolving. However, failure of resolution of existing acute inflammation can cause continuous chronic inflammation . ElementDesiccating stress induces secretion of IL-1, TNF-α, and IL-6 by ocular surface tissues, which promote migration of ocular resident APCs to the regional draining lymph nodes (LNs) . Tear fiTh1 cells secrete IFN-y, which upregulates the production of IL-9, CXCL10, and CAMs, facilitating recruitment of more inflammatory cells to the ocular surface as well as TNF-α to supplement those from innate immune cells ,16. Th17JAK2 polymorphisms with Behcet’s disease, TKY2 polymorphisms with Crohn’s, systemic lupus erythematosus (SLE), STAT3 with psoriasis, STAT4 with rheumatoid arthritis (RA), and STAT6 with atopy and asthma [These aforementioned inflammatory processes are regulated by both genetic and environmental factors and, hence, must be considered as potential therapeutic targets in the treatment strategy for DED. In particular, genetic factors may affect immune response and, hence, autoimmunity, manifesting in the form of autoimmune disease. However, even in the absence of such autoimmune disease, genetic factors play a role in the immune response in dry eye disease. For instance, polymorphisms in IL-10 (anti-inflammatory) lower its mRNA expression and, hence, the risk of chronic gastritis in Asians . Furtherd asthma ,25,26,27Omega-3 fatty acids have been advocated as a treatment for ocular surface inflammatory disease such as DED. Their use as anti-inflammatory compounds dates back to the 18th century when cod liver oil was used in the Manchester Infirmary to treat rheumatism and other chronic inflammatory conditions like tuberculosis . StudiesAfter absorption in the gastrointestinal tract, these fatty acids compete for metabolism by the same intracellular enzymes (cyclooxygenase (COX) and lipoxygenase (LOX)) to form a variety of lipid mediators of inflammation (eicosanoids), which have various effects on modulating innate and adaptive immune cells ,33,34. SOver the years, omega-3 in DED has been the subject of numerous studies and meta-analyses ,40,41,42Not only do omega-3 fatty acids lead to a shift in eicosanoid profile, but they also modulate innate—and to a certain extent—adaptive immunity, and these have been reviewed separately . For exaTNF-α, IL-1B, and IL-6 gene expression [TNF-α expression in monocytes and macrophages, which are the predominant synthesizers of TNF-α [Furthermore, omega-3 PUFAs may downregulate inflammatory gene expression. For example, cells pre-treated with DHA or EPA can downregulate pression ,49,50. Iof TNF-α ,51. A feof TNF-α ,53. Syntof TNF-α . As shown above, the bioactive metabolites of omega-3 fatty acids play crucial roles in modulating innate and adaptive immunity. As shown, the downstream metabolites of omega-3s are dominated by anti-inflammatory oxylipins, which dampen the production of cytokines by the aforementioned immune cells, of which cytokines from the tumor necrosis factor (TNF) superfamily are extensively implicated in both innate and adaptive immunity. TNF-α is produced in all nucleated cells, including the epithelial cells of the cornea and conjunctiva , as wellApart from bolstering innate immunity, there is emerging evidence that chronic TNF-α stimulation may potentiate adaptive immune responses by promoting the survival of effector T cells at sites of inflammation, as well as suppressing cell lineages which inhibit cytokine production, such as Treg cells ,70. Lymphotoxin-alpha (LT-α) is another cytokine of the TNF superfamily. LT-α is structurally similar to TNF-α and also binds to the TNF1 and TNF2 receptors . It is pMuch of the differences in response to omega-3 supplementation between individuals might be due to differences in baseline levels of these omega-3 fatty acids. Wilder’s law of initial value states that the level of response must take into account the initial levels of that substance . Levels TNF-α and LT-α have been the most extensively studied due to their involvement in both innate and adaptive immunity. For instance, polymorphisms in TNF-α show an association with inflammatory connective tissue disease like SLE, RA, and ankylosing spondylitis (AS), and other inflammatory disease like Celiac disease [Genetic factors may also account for the discrepancies in the anti-inflammatory response to omega-3. This is because single nucleotide polymorphisms (SNPs) in gene coding for inflammatory cytokines can influence the severity and extent of inflammation, of which polymorphisms in  disease ,84. Furt disease . TNF-α gene. TNF-α is part of an MHC gene cluster located on chromosome 6, adjacent to LT-α and LT-β; furthermore, TNF-α and LT-α share the same transcriptional orientation [TNF-α promoter region may affect the affinity of RNA polymerase for the promoter and, hence, the frequency of transcription initiation [TNF-α promoter SNPs on TNF-α production is not robust. For example, a meta-analysis on a variety of patients  across a variety of TNF-α promoter SNPs  points to the absence of any association between TNF-α promoter SNPs and TNF-α production [The regulation of TNF-α production is through many epigenetic mechanisms, none of which has a monopoly on TNF-α production. Germane to the discussion is an understanding of the location of the entation . At the itiation ,88. Howeoduction . LT-α alleles, TNF-α alleles, and even other alleles part of the same gene cluster, such as HLA-DR and HLA-DQ, to form extended haplotypes [LT-α are potentially inherited together as part of an extended haplotype with other genetic polymorphisms, which may affect TNF-α secretion. For example, certain HLA-DR subtypes have been correlated with an increased cytokine secretion capacity from monocytes [Intriguingly, polymorphisms in other genes nearby (LT-α) can also affect TNF-α production . This miplotypes . In suchonocytes . All theAt the post-translational level of regulation, soluble TNF receptors may sequester TNF and, hence, attenuate its activity . AdditioPart of the anti-inflammatory effect from omega-3 comes from suppression of TNF-α production. However, if inherent TNF-α production is already low to begin with, any attempts to suppress TNF-α might not further benefit. Conversely, patients with inherently high TNF-α production will be more sensitive to the TNF-α lowering effects of omega-3. This hypothesis was evaluated for the first time by Grimble et al. in a study of 111 healthy subjects . To do sTNF-α , as well as Lymphotoxin A (LT-α+252G and LT-α+252A a.k.a. TNFB*1 and TNFB*2) genes, giving rise to three alleles at each loci [LT-α locus was inversely correlated with inherent TNF-α production. Therefore, the LT-α genotype (but not the TNF-α genotype) can affect the inherent TNF-α. These findings regarding LT-α are shown in Next, Grimble et al. sought to investigate if inherent TNF-α production could be associated with TNF-α or LT-α genotypes. These genotypes arise due to polymorphisms in the ach loci . In theiLT-α genotype is associated with baseline TNF-α, and that the baseline TNF-α tertile predicts direction of omega-3 response, we can deduce that the LT-α genotype can potentially predict omega-3 response. Based on the above information, the calculated probability of a particular genotype responding favorably to omega-3 supplementation is the frequency of each tertile in that genotype  in a given ethnic population. To do so, we obtained the allelic frequencies for each polymorphism from the refSNP database for each ethnic population . We then used the Hardy–Weidenburg Equilibrium to estimate the proportion of each genotype  in each ethnic population, as described below :HeterozyThese probabilities, referred to as P(unknown), are shown in As shown, P(unknown) does not vary much between ethnicities and hovers around 50%, making the probability of favorable response to omega-3 about 50%. However, response rates differ greatly between the various genotypes B1/B1, B1/B2 and B2/B2, as shown in While genetic testing may be costly, the cost of omega-3 taken as a supplement over a lifetime justifies its cost, since it allows each patient to make an informed decision on the efficacy of omega-3 supplementation. Furthermore, since this is testing for a single SNP, the cost is not expected to be high. TNF-α promoter polymorphisms on TNF-α production is clear, the mechanisms behind LT-α polymorphisms influencing TNF-α production is less well elucidated. One possible mechanism is that due to the extremely close proximity of the LT-α and TNF-α gene loci, these LT-α polymorphisms may be in linkage disequilibrium with other unidentified susceptibility polymorphisms responsible for the differences in baseline TNF-α [While the effect (while discordant) of ne TNF-α . LT-α might affect the formation of the transcription initiation complex at the TNF-α promoter, again, due to their proximity. The TNF-α promoter is just around 2 kilobases downstream of the LT-α polymorphism in question [TNF-α and, hence, its levels. However, these need to be verified with future rigorous transcriptomics and metabolomics experiments. Alternatively, polymorphism in question ,100. TheLT-α may also affect differences in the expression of tear cytokines which drive DED pathogenesis, although the underlying mechanisms are highly complex. On the one hand, LT-α polymorphisms correlate with inherent TNF-α levels, which itself increases pro-inflammatory cytokines IL-6, IFN-y, and IL-1b and suppresses immunoregulatory TGF-β and IL-10 on the ocular surface [LT-α polymorphisms correlate with plasma LT-α levels, which vary widely among DED patients, and, after a certain LT-α threshold (>700 pg/mL), LT-α positively correlates with increases in both pro-inflammatory  and anti-inflammatory  cytokines, as revealed in a study by Chen et al. in 2020 [The polymorphisms in  surface ,66. On t in 2020 . EvidentIntriguingly, the same study by Chen et al.  found noLT-α genotyping prior to starting omega-3 in clinical trials. This is so that patients with similar LT-α genotype can be analyzed together so that effects of any variables being tested  are not confounded by genetic factors which also affect the individual’s response to omega-3. Otherwise, the results might be heterogenous with no clear direction of effect, which is the case with the DREAM study and the DREAM extension study. The aforementioned results also emphasize the need for 2), may also be measured in the tears of DED patients. This is because high levels of PGE2 suggest an underlying imbalance of pro-inflammatory lipids, which may be counterbalanced by omega-3 supplementation. This can be achieved using mass spectrometry as described previously [The levels of lipid mediators of inflammation, such as prostaglandin E2  not only increase inherent TNF-α levels [The polymorphisms in with DED ,104. Thiα levels  but alsoα levels ,106, botα levels . This shCurrent meta-analyses are plagued by data heterogeneity due to discordant results from different trials, such that there is no clear direction of effect. For instance, the latest meta-analysis in 2022 of eight studies found no improvement to corneal staining, tear breakup time, or Schirmer’s test due to substantial statistical heterogeneity throughout all outcome measures , leadingCytokines in patients with systemic diseases with DED manifestation may also influence the response to omega-3, although no studies have been in these specific patient populations. For example, Sjogren’s DED patients have higher inherent TNF-α levels than their non-Sjogren’s DED counterparts , which mIn conclusion, the polymorphisms in LT-α can prognosticate the direction of response to omega-3 supplementation as proxied by changes in TNF-α production. Greater inherent levels of TNF-α production is predictive for favorable response to omega-3 supplementation. While the calculated probability of favorable and therapeutic response to omega-3 ranges around 50% for all ethnicities, there is greater distinction in response rates between various genotypes. This emphasizes the need for LT-α genotyping before starting omega-3 in both long-term treatment and in clinical trials. There may be other cytokines which act on or independently of TNF-α to affect the response to omega-3 supplementation and merit further investigation.


Heart failure with preserved ejection fraction (HFpEF) is a syndrome with a heterogeneous cluster of causes, including non-resolving inflammation, endothelial dysfunction, and multi-organ defects. The present study’s objective was to identify novel predictors of HFpEF.The study analyzed the Multi-Ethnic Study of Atherosclerosis (MESA) to assess the association of specific markers of inflammation with new onset of HFpEF . The study included men and women 45 to 84 years of age without cardiovascular disease at baseline. The primary outcome was the multivariate association of the hypothesized markers of inflammation with new-onset of HFpEF versus participants without new-onset heart failure. Participants with missing data were excluded.p = 0.009) but not new-onset HFrEF. In multivariate analysis, MMP3, large LDL-C, and medium HDL-C were not associated with HFpEF or HFrEF.The present analysis included 6814 participants, 53% female, with a mean age of 62 years. Among the entire cohort, HFpEF was diagnosed in 151 (2.2%) participants and heart failure with reduced ejection fraction (HFrEF) was diagnosed in 146 (2.1%) participants. Participants were followed for the outcome of heart failure for a median 13.9 years. Baseline IL-2 was available for 2861 participants. The multivariate analysis included 2792 participants. Of these, 2668 did not develop heart failure, 62 developed HFpEF, 47 developed HFrEF, and 15 developed unclassified heart failure. In the multivariate regression model, IL-2 was associated with new-onset HFpEF (OR, 1.00058; 95% confidence interval, 1.00014 to 1.00102, These findings portend IL-2 as an important component of suboptimal inflammation in the pathogenesis of HFpEF.The online version contains supplementary material available at 10.1186/s12872-022-02656-z. Heart failure with reduced ejection fraction (HFrEF) is principally an end-stage of atherosclerosis, while heart failure with preserved ejection fraction (HFpEF) is a syndrome with a heterogeneous cluster of causes, including non-resolving inflammation, endothelial dysfunction, and multi-organ defects . DespiteAcute inflammation plays a key role in host defense in response to myriad other conditions, including infection covid-19) and injury . However and injuRather than simply predicting risk for new onset of HFpEF, the objective of the present study was to identify candidate biomarkers of suboptimal or chronic inflammation potentially suitable for trials of targeted therapy in the prevention of HFpEF. To achieve this objective, we assessed data from the large, prospective cohort study to test biomarker associations with new-onset of HFpEF, new-onset of HFrEF versus no heart failure. We hypothesized that interleukin-2 (IL-2), matrix metalloproteinase 3 (MMP3), large low-density lipoprotein cholesterol (LDL-C), and medium high-density lipoprotein cholesterol (HDL-C) would be independently associated with new-onset HFpEF.The present analysis used data from the Multi-Ethnic Study of Atherosclerosis (MESA) to assess novel biomarkers of dysregulated and chronic inflammation . MESA inThe primary outcome of the present study was the multivariate association of four novel biomarkers of inflammatory dysregulation  with new-onset HFpEF. Associations were tested versus patients without new-onset heart failure to assess the impact on new-onset HFpEF. The biomarkers were also assessed for association with HFrEF and unclassified heart failure to determine if they were specific for HFpEF versus heart failure generally. Univariate and multivariate multinomial logistic regression was used to assess the associations between biomarkers and the outcomes of interest and were summarized as odds ratio (OR) with 95% confidence interval (CI). Models tested the relationship between predictor variables and membership of four groups  were missing a value for the number of alcohol drinks per week, the only predictor variable with more than 100 missing data points. The number of alcohol drinks per week was not associated with any outcomes of interest; therefore, the model was re-run without alcohol drinks per week variable. There was no change in significant findings, and the quality of the model was improved. Therefore, a model without the number of alcohol drinks per week is reported herein.The MESA was started in 2000 with first publication of original MESA study after the informed consent was obtained . The preThe present analysis included 6814 participants, 53% female, with a mean age of 62 years (Table p = 0.01) and new-onset HFrEF . The multivariate analysis included 2792 participants, among which 2668 did not develop heart failure, 62 developed HFpEF, 47 developed HFrEF, and 15 developed heart failure, which could not be classified. In the multivariate regression model, IL-2 remained associated with new-onset of HFpEF (p = 0.009) but not new-onset of HFrEF (p = 0.34) . In line with this, multiple variables in the model were significantly associated with HFpEF .Univariate analyses for each variable in the models are reported in the Additional file p = 1.000; Deviance, P = 1.000) when MMP3 (n = 970), large LDL-C (n = 6602), and medium HDL-C (n = 6602) were assessed. In univariate analysis MMP3 was not associated with new-onset HFpEF  or new-onset HFrEF . However, large LDL-C was associated with HFpEF  and HFrEF . Medium HDL-C was only associated with HFrEF; HFpEF  and HFrEF . However, MMP3, large LDL-C, and medium HDL-C were not associated with HFpEF or HFrEF in multivariate analysis  and suboptimal inflammation of coronary microvascular disease (harmful). Future research needs to address IL-2, chronic inflammation, and failure of acute inflammation to resolve concerning the prevention and treatment of HFpEF.Additional file 1. Appendix 1: Univariate analyses.


Etanercept (ETA) is a TNF inhibitor widely used in treatment of autoimmune inflammatory diseases. However, the effects of TNF-α inhibition in the establishment of CKD have not been fully elucidated. We evaluate the effects of TNF inhibition by ETA in adenine- (Ad-) induced CKD in rats.  Chronic kidney disease (CKD) constitutes a chronic inflammatory state associated with an increase in inflammatory mediators and profibrotic molecules such as tumor necrosis factor-α levels and biochemical parameters for renal function were evaluated. Histopathological changes in the kidney were assessed using H&E and Masson's trichrome staining and also immunostaining for tubular cells.  Rats were divided into three groups: control, renal injury model, and model plus ETA ; sc). Renal injury was induced by Ad administration . Serum TNF-α levels was observed from 2 w of Ad administration and remained elevated up to 4 w. Treatment with ETA partially reduced kidney damage but was very effective to blocking serum TNF-α.  Ad administration produced a renal functional decline, tubular atrophy, interstitial inflammation, and fibrosis for 2 w, followed by renal anemia, several renal dysfunctions, tubular atrophy, and fibrosis at 4 w. A significant increase in serum TNF-α, this strategy was partially effective in preventing Ad-induced CKD. Although inhibition of TNF by ETA was very effective in reducing serum TNF- CKD conin (CRP) . Inflammin (CRP) .α is an inflammatory cytokine produced mainly by immune cells such as macrophages/monocytes, and its biological activity depends on binding to its receptors [α and TNFRs in renal dysfunction has been described previously [TNF-eceptors . There aeviously , 6.TNF inhibitors (TNFi) are used clinically to counterbalance the high TNF levels in several autoimmune rheumatic diseases such as rheumatoid arthritis (RA) , 8. Inteα to its cell surface receptor [α inhibition on CKD progression is not fully elucidated.Etanercept (ETA) is one of the registered TNFi (DrugBank accession DB00005). It is a fusion protein that is composed of the extracellular ligand-binding portion of human p75 TNFR linked to the Fc portion of human IgG1 and acts as a “TNF decoy receptor” by inhibiting the binding of soluble TNF-receptor . Some streceptor –14. HoweThe chronic adenine (Ad) diet model results in renal dysfunction. Ad is metabolized to 2,8-dihydroxy-adenine which forms crystalline casts within the renal tubules. This induces progressive kidney damage characterized by tubular atrophy accompanied by chronic interstitial inflammation and fibrosis –17.In this study, we employed the adenine-induced chronic kidney disease (Ad-CKD) model to produce rapid-onset kidney disease in rats to assess the effects of ETA on TNF inhibition and its impact on progressive kidney failure.ad libitum. Animal procedures were approved by the Institutional Animal Care and Use Committee (CIATEJ protocol number 2019-009A).Thirty male Wistar rats weighing approximately 200 g were purchased from Bioterio Morelos. Rats were maintained under SPF conditions with a room temperature of 22–26°C and humidity of 50–60% under a 12 h light/dark cycle, with access to standard food and water n = 10 per group): control, kidney damage (Ad), and kidney damage plus etanercept (Ad+ETA). The control group received orogastric administration of water and a subcutaneous (sc) injection of normal saline, both vehicles of Ad and ETA, respectively. Kidney damage was carried out by orogastric administration of Ad  at a dose of 100 mg/kg daily for 4 weeks, with a cutoff preliminary at 2 weeks [Rats were randomly divided into three groups , urine creatinine (UCr), serum urea (sUr), and blood uremic nitrogen (BUN) by means of dry chemistry, in order to evaluate the renal function. Glomerular filtration rate (GFR) was determined using the levels of sCr, UCr, urinary flow, and average kidney weight, using the formula: ((uVol (ml/min) × UCr)/sCr)/average kidney weight (g) .μm) with a microtome and then stained with hematoxylin and eosin (H&E) or Masson's trichrome following standard protocols. Determination of inflammatory infiltrate, tubular damage, and fibrosis was performed by a double-blind semiquantitative analysis. We observed 23 slides, with 2 tissues in each one and 15 fields at 10x and 40x approach for each one. Then, we assessed a score of percentage of damage, inflammation, and fibrosis. The scale considered 0-4 scores or levels according to percentage, where 0 represents the absence or no damage, 1 represents the little presence or minor damage (<25%), 2 represents the presence or damage in approximately the middle of the field (25-50%), 3 represents the presence or damage in approximately two-thirds of field (50-75%), and 4 represents the presence or damage in the entire field (>75%) [Kidney samples were fixed in 4% paraformaldehyde; one part was dehydrated and embedded in paraffin. Tissues were sectioned (5 d (>75%) , 20.μm) with a cryostat for immunofluorescence. Tissues were stained with antibody for Thy-1 (CDw90)  at a dilution of 1 : 250 overnight at 4°C, washed, and then incubated with a secondary antibody, Alexa Fluor® 488 . Nuclei were counterstained with DAPI . Images were obtained with confocal microscopy  at a 40x magnification; 3 cortical fields were analyzed per sample.Another section of the fixed kidneys was cryoprotected  overnight at 4°C and then was embedded in Tissue-Tek (O.C.T. Compound Sakura) and snap frozen at -25°C. Frozen tissues were sectioned . Results were analyzed by linear regression adjustment, using the standard concentrations of TNF-α and the absorbance given at 450 nm.Serum levels of TNF-P value ≤ 0.05 were considered. Results are represented as the mean ± standard error (SEM).Statistical analysis was performed using GraphPad Prism version 5 . One-way ANOVA was used followed by Dunn's multiple comparison posttest. The statistically significant differences of the results with a α in serum. Ad administration significantly increased serum TNF-α levels from the second week (570 pg/ml), and they remained high until the fourth week of Ad administration (587 pg/ml) compared to the control (104 pg/ml). Treatment with ETA (Ad+ETA) significantly decreased TNF-α concentrations from week 2 (137 pg/ml) to week 4 (125 pg/ml), compared to the Ad groups without treatment shown in To demonstrate the TNF inhibition by ETA, we performed an ELISA assay to quantify the levels of TNF-First, the kidney appearance was observed. As shown in Next, hematological and biochemical analyses were performed to evaluate renal anemia and kidney function. Chronic kidney damage depletes the number of EPO-producing cells in the kidney; therefore, hemoglobin, hematocrit, and erythrocyte indexes descend. Ad significantly decreased hematic parameters at 4 weeks; however, treatment with ETA did not have an impact on these . MoreoveTreatment with ETA partially reduces kidney damage prior to the establishment of CKD. Adenine administration produces a persistent inflammation and a progressive scarring evolution to CKD. We found a large accumulation of inflammatory infiltrate in kidney tissue from the second week with a score 3 (62%) and reaching to score 4 (79%) at the fourth week in groups with Ad. The administration of ETA partially decreased the percentage of inflammatory infiltrate by approximately 10% at second week (54%) but only in 3% at 4 weeks (76%), shown in α is a potent proinflammatory cytokine and important mediator of inflammatory tissue damage. High serum levels of TNF-α are positively correlated with the severity of kidney injury [α concentration up to four times from week 2 and remained elevated until week 4, confirming the elevation of this cytokine in the Ad-CKD model. Similar data has been previously reported, where elevated levels of this cytokine were approximately 3 times more in the Ad group compared to the control [α from the second week up to the fourth week. With these data, we demonstrated an efficient TNF-α inhibition by ETA. This finding is consistent with the action mechanism of ETA that inhibits binding of TNF to cell surface TNFRs, rendering biologically inactive TNF.TNF-y injury . In our  control . TreatmeMoreover, in our study, we found a progressive deterioration of kidney function induced by Ad. According to previous studies , 21, renIn addition, as previously mentioned, it is well known that Ad administration induced renal toxicity associated with tubular injury and produced persistent inflammation and a progressive scarring evolution to CKD , 19, 22.α inhibitors have shown protective effects in the models of acute kidney injury (AKI) such as ischemia/reperfusion (I/R) injury [α blockers in the models of chronic kidney damage [α blockers could be more effective in the models of acute damage or using higher doses. Besides that, several factors participate in the pathophysiology of CKD: initiation , transition , and establishment  where multiple profibrogenic molecules participate [Although, some studies showed that treatments with TNF-) injury , 24, buty damage , 26. Recy damage . As prevticipate , 28.α levels and the severity of adenine-induced tubular damage prior to the establishment of the CKD model. However, inhibition of TNF-α is not sufficient to avoid the progression of kidney damage to fibrosis. Actually, it has been widely reported that establishment of fibrosis is a complex phenomenon that ultimately depends on several proinflammatory/anti-inflammatory balance, as well as tissue remodeling molecules , including many other regulators, involving a highly orchestrated event that determines tissue homeostasis, as well as the initiation of inflammation, reinforcement, and perpetuation. In addition, other factors like time of exposition, concentration, and elimination of the nephrotoxic etiological agent compromise the efficiency to carry out the removal of the extracellular matrix. Therefore, although inhibition of TNF by ETA is effective in blocking TNF-α, this strategy was partially effective in preventing Ad-induced CKD.Treatment with ETA reduces circulating TNF-


However, the mechanisms through which ferroptosis initiates the development and progression of HFpEF have not been established. This review explains the possible correlations between HFpEF and ferroptosis and provides a reliable theoretical basis for future studies on its mechanism.Ferroptosis is a recently identified cell death characterized by an excessive accumulation of iron-dependent reactive oxygen species (ROS) and lipid peroxides. Intracellular iron overload can not only cause damage to macrophages, endothelial cells, and cardiomyocytes through responses such as lipid peroxidation, oxidative stress, and inflammation, but can also affect cardiomyocyte Ca Ferroptosis, a unique form of cell death that differs from necrosis and apoptosis, was first proposed in 2012 [Different from heart failure with reduced ejection fraction (HFrEF), heart failure with preserved ejection fraction (HFpEF) is a multisystemic syndrome, with most patients having a history of metabolic disorders such as obesity, diabetes, hypertension, and hyperlipidaemia ,4. SeverThe initiation and execution of ferroptosis is a highly regulated process. Although the specific mechanism of iron death has not yet been clarified, with the continuous expansion of research, several key ways to mediate ferroptosis have been identified, including LPO, amino acid metabolism, and iron metabolism .3+ is bound to transferrin and pumped into the cell via the transferrin receptor1 (TFR1) and subsequently reduced to Fe2+ by intracytoplasmic iron oxide reductase3 (STEAP3). Then, Fe2+ is released into a labile iron pool in the cytoplasm from the endosome mediated by divalent metal transporter protein 1 (DMT1). Excess Fe2+ can activate both the non-enzymatic [2+ is excreted from cells through the iron export protein ferroportin (FPN1) and then converted to Fe3+ with low redox activity by ceruloplasmin (CP). Iron homeostasis is regulated by iron import, transport, storage, and export processes. The addition of bioavailable forms of iron, such as ferric ammonium citrate, ferric citrate, ferric chloride hexahydrate, and iron bound to transferrin, sensitizes cells to develop ferroptosis [Iron is required for both ferroptosis execution and the accumulation of lipid peroxide. Extracellular Fenzymatic  and enzynzymatic  pathwaysroptosis . On the roptosis . A studyroptosis .−) and peroxynitrite (ONOO−) are two particularly chemically reactive species of activated oxygen that can induce LPO. The OH- formed by the Fenton reaction from intracellular free Fe2+ can build lipid peroxides directly with PUFAs in membrane phospholipids and attack the cytosolic membrane, causing iron death morphological changes [− can be produced by nitrosative stress [LPO triggered by ROS attack on membrane polyunsaturated fatty acids (PUFAs) is the direct cause of ferroptosis. LPO products can destabilize the phospholipid bilayer and eventually cause the disintegration of the cell membrane . ROS hyd changes . The ONOe stress . In addie stress . Non-enze stress ,17. Derie stress ,19,20.−) in the cell membrane takes in one molecule of cystine and excretes one molecule of glutamate in a 1:1 ratio. Cystine enters the cell and then is reduced to cysteine, which is involved in the synthesis of glutathione (GSH). GPX4 (an important antioxidant enzyme) uses GSH as a substrate to scavenge lipid peroxidation and reduce oxidative stress. Drugs that reduce intracellular cysteine and GSH by targeting the system Xc−, such as erastin, or drugs that directly inhibit GPX4, such as RSL3, have been shown to induce ferroptosis [Amino acid metabolism is involved in the regulation of ferroptosis by LPO. Both cystine  and glutroptosis ,22. Glutroptosis . Furtherroptosis .Over the past decade, the understanding of HFpEF has progressed from a basic diastolic malfunction of the LV to a syndrome caused by multiple system diseases. Obesity, diabetes, hypertension, and hyperlipidemia are all diseases that can cause and keep the body in a chronic inflammatory state, which is considered to be the initial event of diastolic dysfunction in HFpEF. There was a significant increase in plasma levels of CRP in patients with HFpEF with an increasing number of comorbidities . In addi2O2 levels, and endothelial nitric oxide synthase (eNOS) uncoupling [2O2 levels were significantly elevated in both human and rat samples of HFpEF [In a systemic pro-inflammatory state, inflammatory cytokines induce the upregulation of ROS-producing NADPH oxidase (NOX), leading to high oxidative stress, increased Hcoupling ,32. H2O2of HFpEF . eNOS isof HFpEF . In addiof HFpEF .According to the comorbidity–inflammation paradigm, vascular endothelium damage is a key initiating event of myocardial inflammation in HFpEF. Iron overload causes endothelial dysfunction , which iMacrophages resident in myocardial tissue play a central role in iron homeostasis, as they recycle iron by phagocytosing senescent and damaged red blood cells and return most of the iron to circulation . In hearOn the other hand, cardiac iron-deposition-induced fibrosis has been reported in some iron overload animal models, despite the fact that iron excess reduced M2 macrophages ,48. Ishi2+) signaling. The main pathway for iron uptake by the myocardium is dependent on the interaction of Fe3+-bound transferrin (TF) with the transferrin receptor 1 (TFR1)  [2+-induced slowing of LTCC current inactivation by Fe2+ leads to increased Ca2+ entry and overload [Systolic cardiac contraction and diastolic relaxation depend on the excitation–contraction coupling (ECC) of cardiomyocytes, which requires proper calcium (Ca1 (TFR1) . In addi1 (DMT1) . Serum i1 (DMT1) ,52. Rece1 (DMT1) . LTCCs a1 (DMT1) . Fe2+-inoverload . In miceoverload .2+ treatment are currently poorly defined due to the limited availability of cardiac tissue from patients with HFpEF. Nevertheless, there is compelling evidence that impaired calcium homeostasis in patients with HFpEF runs through different periods and stages, from compensatory hypertrophy to clinical symptoms, and is manifested as increased myocardial cell diastolic calcium concentration or abnormal sarcoplasmic reticulum calcium uptake followed by incomplete myocardial relaxation [2+ release of systolic RyR2 increased in HFpEF models, and this increased Ca2+ release can be attributed to an increased sarcoplasmic reticulum (SR) Ca2+ load during Ca2+ influx through LTCCs [2+ leakage mediated by RyR2 and decreased activity of SERCA2a responsible for calcium reuptake was reported in HFpEF rats [Changes in HFpEF-related Calaxation ,56. As sgh LTCCs ,58,59. FpEF rats ,61.2+-handling proteins, including RyR2 [As a result, increased levels of NTBI may cause cardiac iron overload as well as calcium overload via LTCCs, impairing the ECC of HFpEF cardiomyocytes. Research showed that increased iron levels and LPO mediate ferroptotic cell death, which can also be induced by unregulated calcium levels . Li et aing RyR2  and SERCing RyR2 , are paring RyR2 .−, which is formed by the rapid reaction of NO with superoxide (O2−) radicals, is a powerful inducer of LPO. Excess NO-induced accumulation of lipid peroxides promotes ferroptosis. Further study revealed a high NO-driven dysregulation of the Xbp1s–FoxO1 axis as a pivotal mechanism in the pathogenesis of cardiometabolic HFpEF, FoxO1 depletion, as well as over-expression of the Xbp1s (a spliced form of the X-box-binding protein 1) arm of the UPR (unfolded protein response) in cardiomyocytes; each ameliorates the HFpEF phenotype (diastolic dysfunction and exercise tolerance) in mice and reduces myocardial lipid accumulation [2O2 and LPO end product malondialdehyde (MDA) were observed in obesity-related HFpEF rats [Obesity and metabolic syndrome often coincide with disturbances in lipid metabolism. The occurrence and development of HFpEF, therefore, is closely related to LPO according to the comorbidity–inflammation paradigm. It was previously thought that decreased nitric oxide (NO) availability contributed to the pathophysiology of HFpEF , but recmulation . Excitinmulation . In addipEF rats , with a pEF rats . It has pEF rats . FurtherpEF rats , furtherIn recent years, the role of ferroptosis in cardiovascular disease has received increasing attention. Cells undergoing ferroptotic cell death are characterized by the accumulation of lipid peroxide, metabolism of PUFA, excess iron, and cannot be rescued by inhibitors of apoptosis or other cell death processes . InflammRecent research found that GPX4 was reduced in HFpEF mice with obesity and impaired glucose tolerance, and imeglimin treatment not only improved their abnormal systemic glucose metabolism and visceral obesity, but also suppressed the upregulation of iNOS and restored expression of Xbp1s and GPX4 . In addi2+), and regenerates some endogenous antioxidants such as glutathione. Discontinuous treatment with ALA in HFpEF rats prevented the development of cardiac hypertrophy and both attenuated and delayed the onset of diastolic dysfunction through anti-obesity effects, anti-inflammatory effects, and reducing lipid metabolism disorders (raising the GSH/GSSG ratio and lowering circulating MDA levels) [2O2, 3-nitrotyrosine, glutathione, and LPO) in the cytosol and mitochondria of cardiomyocytes in mice and humans with HFpEF, and thus improved endothelial and myocardial function [Alpha-lipoic acid (ALA) is an ideal antioxidant that scavenges ROS, chelates divalent transition metals (including Fe levels) . As the function ,79,80. Tfunction  showed tfunction ,83. Thisfunction ; thus, t2O2-induced AMPK/mTOR/ERK-dependent mechanisms, demonstrating its antioxidant therapeutic potential against HFpEF. Among them, AMP-dependent protein kinase (AMPK) and rapamycin (mTOR) can inhibit ferroptosis through different pathways. Energy stress activates AMPK, which suppresses PUFA and other fatty acid production, at least in part by phosphorylating and inactivating acetyl coenzyme A carboxylase (ACC), inhibiting ferroptosis [Lipocalin (APN) is an adipokine that can regulate cardiac remodeling by interacting with multiple intracellular signaling pathways . Recentlroptosis . Bayeva roptosis  discoverroptosis , which sAccording to the current paradigm of HFpEF development, coronary microvascular endothelial inflammation and dysfunction is the initial and critical mechanism for cardiac remodeling. Hypertensive cardiac remodeling is characterized by hypertrophy, fibrosis, and inflammation . EffectiAppropriate treatment of HFpEF is considered one of the largest unmet needs in the cardiovascular field because of its high prevalence, adverse outcomes, and lack of approved beneficial therapies ,94. ReceClinical studies showed that Fth1 was positively correlated with amino-terminal pro-B-type natriuretic peptide (NT-proBNP) and the ratio of mitral peak velocity of early filling to early diastolic mitral annular velocity (E/e’) ,97,98. AThe regulatory mechanisms of myocardial cell death are of great research value and transformational significance for heart failure because the myocardium is non-renewable. Ferroptosis and its regulatory processes are closely related to the development of HFpEF and deserve further attention. Second, the incidence of ferroptosis includes the expression and control of multiple genes, its signaling pathways are complicated, and the processes have not been fully revealed. More in-depth investigations are expected to uncover the currently unknown molecular processes of ferroptosis and provide more scientific evidence for targeting ferroptosis in HFpEF.


Since the role of Ca2+ influx in TNF-α-mediated inflammation remains poorly understood, we explored the role of L-type voltage-gated Ca2+ (CaV) channels in TNF-α-induced CCL-2 and IL-6 secretion from human pulmonary endothelial cells. The CaV channel blocker, Nifedipine, decreased both CCL-2 and IL-6 secretion, suggesting that a fraction of CaV channels is open at the significantly depolarized resting Em of human microvascular pulmonary endothelial cells (−6 ± 1.9 mV), as shown by whole-cell patch-clamp measurements. To further explore the role of CaV channels in cytokine secretion, we demonstrated that the beneficial effects of Nifedipine could also be achieved by Em hyperpolarization via the pharmacological activation of large conductance K+ (BK) channels with NS1619, which elicited a similar decrease in CCL-2 but not IL-6 secretion. Using functional gene enrichment analysis tools, we predicted and validated that known Ca2+-dependent kinases, JNK-1/2 and p38, are the most likely pathways to mediate the decrease in CCL-2 secretion.Elevated TNF-α levels in serum and broncho-alveolar lavage fluid of acute lung injury patients correlate with mortality rates. We hypothesized that pharmacological plasma membrane potential (Em) hyperpolarization protects against TNF-α-induced CCL-2 and IL-6 secretion from human pulmonary endothelial cells through inhibition of inflammatory Ca TNF-α is a key regulatory molecule in the development of both direct  and indirect  phenotypes of acute lung injury (ALI) ,2. In in2+ (iCa2+) levels [2+ flux across the alveolar–capillary interface remains poorly understood. To complicate matters, in alveolar endothelial and epithelial cells, both Ca2+-dependent and -independent secretory mechanisms have been observed. Specifically, in vascular endothelial cells, some investigators found an increase in iCa2+ levels following TNF-α treatment [2+ concentrations [2+ influx-dependent process [2+ concentrations, whereas the secretion of CCL-2 (MCP-1) relies on the mobilization of iCa2+ stores but not extracellular Ca2+ levels [The binding of TNF-α to its main receptor, TNF-α receptor 1 (TNFR1), activates multiple inflammatory signaling cascades simultaneously, which ultimately promote inflammatory mediator secretion, barrier dysfunction, and cell apoptosis [) levels ,6, but dreatment ,8, whiletrations . Recent  process , but the+ levels  in these+ levels ,13.–capillary interface is crucial for the design of new therapeutic strategies against ALI. Specifically, we face an urgent need to clarify (i) the role of L-type voltage-gated Ca2+ (CaV) channels in regulating Ca2+ influx and inflammatory cytokine secretion during ALI, (ii) the importance of resting plasma membrane potential (Em) alterations in regulating alveolar iCa2+ homeostasis, and (iii) the protein–protein interaction (PPI) networks that translate Em and iCa2+ alterations into inflammatory cytokine secretion. Therefore, this study was designed to (a) determine the role of CaV channels in TNF-α-induced CCL-2 and IL-6 secretion from human pulmonary endothelial cells, two key inflammatory cytokines involved in the development of ALI [2+ influx-dependent inflammatory cytokine secretion via pharmacological Em hyperpolarization, and (c) establish the intracellular signaling mechanisms and PPI networks that confer such protection. We hypothesized that pharmacological Em hyperpolarization protects against TNF-α-induced CCL-2 and IL-6 secretion from human pulmonary endothelial cells through the inhibition of inflammatory Ca2+-dependent MAPK pathways.These knowledge gaps highlight that a more detailed understanding of the electro-chemical signaling mechanisms occurring at the alveolart of ALI ,13, (b) 2+ (CaV) channels. Alternatively, a similar protective effect on CCL-2 secretion can be achieved by inhibiting CaV activity via large conductance K+ (BK) channel-mediated Em hyperpolarization in human pulmonary endothelial cells, which results in the downstream disruption of known Ca2+-dependent JNK-1/2 and p38 MAPK signaling pathways.Our results in cultured and primary human pulmonary endothelial cells show that TNF-α stimulation increases inflammatory CCL-2 and IL-6 secretion from these cells, which can be inhibited by blocking L-type voltage-gated Ca2+-dependent in many cell types, but little is known about the role of endothelial Ca2+ influx during TNF-α-induced inflammation in the lung. Initial dose–response experiments revealed that 5 ng/mL and 50 ng/mL TNF-α evoked a similar increase in CCL-2 and IL-6 secretion from cultured human pulmonary microvascular endothelial cells . To a much lesser degree, Nifedipine also decreased baseline CCL-2 and IL-6 secretion from unstimulated endothelial cells .TNF-α-induced secretory processes are Caal cells  and, theSince studies on rat alveolar epithelial and calf pulmonary artery endothelial cells reported a significant depolarized Em ,15, we mV channels in the regulation of CCL-2 and IL-6 secretion from TNF-α-stimulated cultured endothelial HULEC5a is suggested. We next investigated whether Em hyperpolarization via large conductance K+ (BK) channel activation could be used as an alternative approach to counteract CaV-dependent cytokine secretion. First, we used high-throughput, fluorescence-based FLIPR assays to demonstrate that the BK channel opener, NS1619 (30 µM), potently hyperpolarized the endothelial Em in both untreated and TNF-α-stimulated cultured human pulmonary microvascular endothelial cells  networks involved in the observed decrease in TNF-α-induced CCL-2 secretion following BK channel activation (NS619). This powerful, free-of-charge platform is one of the most popular approaches for analyzing the associations of proteins and genes obtained from proteomics and other high-throughput technologies, and it contains information from more than 14,000 organisms.V channels, CCL-2, and BK channels. The first step, after setting the confidence of the interaction level to “high” , was for the STRING model to create a predictive PPI network containing the following top nine interaction partners: CCR-2, TNFRSF-1B, BIRC-2, TRAF-2, TNFRSF-1A, RIPK-1, TRADD, FADD, IKBKG (V channels), (ii) cytokines (including CCL-2 and its receptor CCR-2), and (iii) members of the TNF-α pathway . In this model, the cytokine and TNF-α pathway sub-clusters interacted with each other via the proinflammatory CCL-2/TNF-α axis and their respective receptors, CCR-2 and TNFRSF-1B  proteins from D, IKBKG A. The seD, IKBKG B: (i) io2+-dependent inflammatory pathways in numerous published ALI models [V channels which are at the intersection of all three pathways , and are primarily connected with the source protein TNF-α: IKBKG, TRADD, and TRAF2  pathways in untreated and TNF-α-stimulated cultured human pulmonary microvascular endothelial cells . We foun2+-dependent MAPK pathways JNK-1/2- and p38, we blocked these pathways with pharmacological inhibitors and measured CCL-2 secretion. We found that the inhibition of JNK-1/2 (JNK inhibitor VIII) and p38 (SB203580) decreased TNF-α-induced CCL-2 release by 73% (from 4386 ± 128 to 1189 ± 344 pg/mL) and 69% (from 4386 ± 128 to 1381± 280 pg/mL), respectively. The combination of NS1619 with the JNK inhibitor VIII or SB203580 decreased TNF-α-stimulated CCL-2 secretion by 96 % (from 4386 ± 128 to 140 ± 15 pg/mL) and by 97%  affected by NS1619 treatment. This database contains 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes using two main parameters; the “Count in gene set” is a parameter used in functional gene enrichment analysis and is a powerful analytical method for interpreting gene expression data. It ranks all genes in a data set, then calculates an enrichment score for each gene set . The second parameter, “False discovery rate (FDR)”, is based on a statistical approach that is used in high-throughput experiments to correct for random events that falsely appear significant (low FDR = high probability of involvement of a given pathway in the network). As input variables (source proteins) for this model, we entered NS1619 as a chemical, and BK channels (KCNMA1), CaV channels (CACNA1), TNF-α, CCL-2, JNK-1/2, and p38 as experimentally validated source proteins , protein-chemical interaction analysis (STITCH), and experimental phosphorylation and protein expression studies provides strong evidence that the BK channel activation-mediated decrease in TNF-α-induced CCL-2 secretion is caused by the inhibition of Ca–capillary interface during ALI, which makes it challenging to develop new targeted therapeutic approaches against this devastating disease. In particular, the role of Ca2+ influx in inflammatory mediator secretion from lung resident  and immune cells in this highly complex inflammatory environment is poorly understood. In pulmonary endothelial and epithelial cells, the main structural components of the alveolar–capillary interface [2+-dependent and -independent secretory mechanisms have been observed. Previous studies from our lab revealed that the TNF-α-induced secretion of the inflammatory cytokine IL-6 from cultured alveolar epithelial cells occurred independently of intra- and extracellular Ca2+ concentrations. Inflammatory CCL-2 (MCP-1) secretion, on the other hand, relies on the mobilization of intracellular Ca2+ (iCa2+) stores but not extracellular Ca2+ levels in cultured alveolar epithelial cells [2+ levels following TNF-α treatment [2+ concentrations [2+ influx-dependent NF-kB-1 activation and subsequent proinflammatory IL-1β release [Very little is known about the electrochemical signaling mechanisms underlying the inflammatory processes occurring at the alveolarnterface ,21, bothal cells ,22. In vreatment ,12, whiltrations . In immo release , but the2+ influx mechanisms at the alveolar–capillary interface is scarce, and therefore, in this study, we focused on human pulmonary endothelial cells. Previous reports showed the expression of transient receptor potential (TRP) channels in lung endothelial [V channels in the lungs and their potential contribution to the regulation of iCa2+ concentrations is limited. One study, using rat lung slices, found the expression of R-, T-, and N-type CaV channels in smooth muscle cells and nerve fibers and L-type (CaV 1.2 and 1.3) channels in lung epithelial but not endothelial cells [V channels in inflammatory CCL-2 and IL-6 secretion  channels, and we found no changes in BK channel expression in TNF-α-treated endothelial cells  and mechanical ventilation-induced ALI models [V inhibition with Nifedipine did not affect the CCL-2 levels in human pulmonary endothelial cells, highlighting important differences in the role of CaV channels between TNF-α- and LPS-mediated inflammation.Given the relatively depolarized Em values measured in our human pulmonary microvascular endothelial cells (−6 ± 1.9 mV), which are consistent with data in the literature on mammalian endothelial cells , we nextrization . We founecretion B,C. ThesI models . An earlI models . TherefoI models . HoweverV channels, and the cytokine CCL-2 in our inflammation model, we used the STRING database. This powerful and free-of-charge web-based resource contains both gene and protein information from numerous sources, including experimental data, computational prediction methods, and public text collections [2+-dependent MAPK, JNK-1/2, and p38 as the most likely signaling pathways responsible for the observed reduction in CCL-2 secretion, since the blocking of these pathways with pharmacological inhibitors reduced endothelial CCL-2 secretion  networks that integrate TNF-α, BK, Calections . This prlections . Based olections , BK chanlections ,39,40. Oecretion G. RIG-1  process , to the tivation ,43. On tnd TRAF2 D. These –target relationships. STITCH connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs [2+-dependent process [While the STRING/KEGG databases are useful tools for predicting protein–protein interaction networks and related signaling pathways, for the last reported set of experiments, we employed the STITCH database  to clari00 drugs . Using t00 drugs , which i00 drugs . Therefo process ,48,49, tThis is the first report proposing endothelial BK channels as potential therapeutic targets to counteract TNF-α-induced inflammation in the lung. In other organ systems, the modulation of these channels has already sparked clinical interest . NS1619 The limitations of our study include the need for the validation of the reported anti-inflammatory effects in vivo. It is also important to recognize that, in addition to the plasma membrane, BK channels can also be found on nuclear  and mitoV channels and the role of Em hyperpolarization in TNF-α-induced inflammatory mediator secretion from human pulmonary endothelial cells via Ca2+-dependent JNK-1/2 and p38 pathways. These findings lay the groundwork for future studies targeting these pathways and signaling molecules as potential new approaches against TNF-α-induced endothelial inflammation.In conclusion, our data show for the first time the importance of CaHuman pulmonary microvascular endothelial cells  were purchased from ATCC  and cultured in an MCDB131 medium  supplemented with 10% FBS , 1% Penicillin/Streptomycin , 10 mM L-Glutamine , 10 ng/mL Epidermal Growth Factor , and 1 µg/mL hydrocortisone . Primary human pulmonary artery endothelial cells (HPAEC) were purchased from Lifeline Cell Technology  and cultured in an endothelial cell medium , supplemented with 5% fetal bovine serum (FBS), 1% endothelial cell growth supplement, and 1% antibiotic solution (Penicillin/Streptomycin) . Primary human alveolar epithelial cells (HPAEpiC) were purchased from ScienCell  and cultured in a human epithelial cell medium , supplemented with the epithelial cell medium supplement kit . HULEC5a cells were used at a passage number <20, and HPAEC or HPAEpiC at a passage number <15. Separate passages from a given lot were considered biological replicates.E. coli-derived), NS1619 , Nifedipine , JNK inhibitor VIII , SB203580  or equimolar vehicle controls for 24 hrs. To avoid any potential cell toxicity related to TNF-α, in a pilot dose–response experiments, we treated HULEC5a cells with 5 and 50 ng/mL TNF-α for 24 hrs, measured CCL-2 and IL-6 concentrations in cell supernatants, and assessed cell viability .CCL-2 and IL-6 concentrations in cell culture supernatants were quantified by ELISA  after the treatment of cells with 5 ng/mL or 25 ng/mL TNF-α  Membrane Potential Assay kits  ,2,3. TheTo determine the resting plasma membrane potential (Em), we measured baseline Em values using the whole-cell patch clamp technique ,65.KCNMA1 (BK-α1), or GAPDH (Applied Biosystems).The total RNA was isolated from the control and TNF-α-treated (5 ng/mL for 24 h) HULEC5a and HPAEpiC cells, using a Qiagen RNeasy Mini Kit , 1–8 μg RNA were reverse transcribed with a High Capacity cDNA Reverse Transcription kit , and 2 μL cDNA were amplified by semi-quantitative Real-Time PCR (TaqMan) with primers for specific V channels, TNF-α, and CCL-2, we employed a search tool for the retrieval of interacting genes/proteins . STRING integrates all known and predicted associations between proteins, including both physical interactions and functional associations. Furthermore, STRING collects and scores evidence from a number of sources such as (i) automated text mining from the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context, and (iv) the systematic transfers of interaction evidence from one organism to another. We developed a predicting strategy that included 3 main steps: first, we defined which downstream networks were regulated by BK and CaV channels. For this purpose, our experimentally validated source proteins  were entered into STRING as source proteins. By setting the confidence of the interaction cutoff level to “high” , STRING analysis revealed an additional top 9 downstream proteins connected to each other and/or to the experimentally validated initial cluster consisting of BK channels, CaV channels, TNF-α, CCL-2. Second, we performed “Cluster analysis” using the KMEANS clustering algorithm. This step defines the number of sub-clusters  within the network predicted in step 1. Understanding the way these sub-clusters are connected forms the basis for step 3, where we used the Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis in STRING to narrow down the candidate pathways within the network to the top 3. This is the most critical step in defining the most likely downstream signaling pathways employed by BK and CaV channels during TNF-α-induced inflammation. Those proteins that are common to all 3 different pathways are most likely responsible for the observed BK and CaV channel-mediated inhibition in CCL-2 secretion [To predict the PPI networks underlying the interactions between BK channels, L-type Caecretion ,37.To validate the activation of our top 3 predicted signaling pathways , we used an ELISA-based RIG-1-like receptor (RLR) kit , an NF-kB/p65  detection kit , a multiplex immunoassay MAP kinase phosphoprotein  kit, and an electrochemiluminescent platform , respectively ,66.V channels, TNF-α, CCL-2, JNK-1/2, and p38) and the chemical NS1619 as entry points into STITCH . This database integrates information about interactions from metabolic pathways, crystal structures, binding experiments, and drug–target relationships. Inferred information from phenotypic effects, text mining, and chemical structure similarity is used to predict relations between chemicals and proteins [To explore the interactions between the chemical NS1619 and target proteins, we input our experimentally defined protein queries  are poorly understood, and no targeted approaches exist that improve patient outcomes. TNF-α levels are consistently elevated in ALI patients, but the role of Ca2+ influx in TNF-α-mediated cytokine secretion remains a matter of intense discussion. Using a TNF-α-induced inflammatory model, we discovered that inflammatory CCL-2 and IL-6 secretion form of human pulmonary endothelial cells relies on voltage-gated Ca2+ (CaV) channel activity. We identified cell membrane potential hyperpolarization via the activation of large conductance K+ (BK) channels as a new alternative approach to inhibit CCL-2 secretion, which occurs via the BK-mediated downstream inhibition of Ca2+-dependent JNK-1/2 and p38 signaling. These findings represent a potentially new targeted approach against TNF-α-induced inflammation in the lung.The inflammatory mechanisms that cause damage to the alveolar


N = 120 papers, we map the current research landscape on gender specific biases present in ML and AI assisted automated systems. We further point out the aspects of ML/AI gender biases research that are less explored and require more attention. Mainly we focus on the lack of user studies and inclusivity in this field of study. We also shed some light into the gender bias issue as experienced by the algorithm designers. In conclusion, in this paper we provide a holistic view of the breadth of studies conducted in the field of exploring, detecting and mitigating gender biases in ML and AI systems and, a future direction for the studies to take in order to provide a fair and accessible ML and AI systems to all users.Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI) algorithms present promising solutions to a variety of technological and non-technological issues. Although, industry leaders are rapidly adopting these systems for anything from marketing to national defense operations, these systems are not without flaws. Recently, many of these systems are found to inherit and propagate gender and racial biases that disadvantages the minority population. In this paper, we analyze academic publications in the area of gender biases in ML and AI algorithms thus outlining different themes, mitigation and detection methods explored through research in this topic. Through a detailed analysis of  Algorithmic fairness has been a topic of interest in the academia for the past decade. Due to widespread application of Machine Learning (ML) and Artificial Intelligence (AI) assisted systems and complicated process of implementation, it is often difficult to flesh out the details of an ML system and demonstrate if the automated decisions made by these systems are fair or not. Although, the fairness discussion within the realm of ML and AI is a recent development, discrimination has roots within human society. The unfair treatment toward the minority is well-documented in the data we have created over time. These historical biases trickle into the automated systems through the data. ML/AI systems trained on these data are able to pick the implicit biases exercised over the years, which we might not see at first glance. For example, advertisement algorithm showing more high-paying technical jobs to men than women.Although there are a variety of biases present in the automated decision-making systems based on attributes like gender, race, sex, age, and others, in this paper our focus in on gender biases. Broadly in the context of ML and AI implementation, a model is gender biased if the model's performance and/or output is biased against a faction of population based on their gender. For example, in the research conducted by Buolamwini and Gebru we see that the facial recognition systems were highly inaccurate (more than 30%) when it comes to classifying the faces of women of color. In this ground-breaking paper, the authors further demonstrated that the model was most accurate for people who identified as male and of white skin tone  is an effective format to provide a unified view of several aspects of gender biases in different forms and stages of ML and AI application. Furthermore, a detailed analysis of academic publications will help researchers and ML/AI engineers to understand the gaps in research that needs more attention. In short, this paper aims to aid the ongoing research into gender bias in ML/AI systems by providing following key contribution:Provide an overview different themes and topics explored in the ML/AI gender bias research papers: There are many interesting and innovative concepts presented by different authors published in the gender bias research field. These ideas bring novel solutions and perspective to the issue of gender fairness within ML and AI communities. Hence, it is valuable to create a holistic account of these ideas as they can inspire further conversation and actions to prevent gender bias.Discuss different bias detection and mitigation methods proposed in these papers for different ML/AI algorithms: There are many gender-bias detection and mitigation methods proposed in the literature but there is little wide-spread application of these methods. Since many of these methods are provided within the context of specific type of algorithm, these solutions could remain hidden from researchers working on a separate set of algorithms. So, it is important to gather these concepts and recount these ideas as they can result in creating more solutions to a similar problem in a different ML/AI application. Additionally, an overview of all these proposed methods gives us a clue into algorithms and solutions that are most studied and others that are not.Shed light into the less studied aspects of ML/AI gender bias research and provide an argument for the need of more attention to these less explored topics: The ethical and legal aspect of these gender bias issues are seldom discussed. Similarly, we need more studies that collaborate with user community and field experts to get a good grasp on their perspective of gender bias in ML/AI systems.Here, we would like to mention that there are prior literature reviews which conduct in-depth analysis of fairness in automated systems and provided detailed overview of bias mitigation and detection methods. For example, Caton et al. review different biases detected in automated systems based on sensitive attributes like gender, marital status, race, and disabilities , searching in Google Scholar required no prior registration. We limited our search to the publications from year 2010 up to 2021. We added this year restrictions because we believed any research prior to 2010, will not reflect the current developments in the field. From the search we gathered 192 papers for the review.Once we had all the papers, we manually went through the papers to further refine the corpus. We created following exclusion criteria to refine the papers we had collected:We excluded the paper if the paper was not written in English given the primary evaluation done in the same language.We removed the paper if the full text of the paper was inaccessible, behind a paywall or had loading errors. We contacted the authors in that case, and we kept the paper in our list if we obtained those papers.We excluded the paper if the paper was incomplete or retracted, or not published on peer-reviewed journals and/or conferences.We put a time constraint in the inclusion of the papers and analyzed those papers published on or after 2017. We intentionally put this criterion to evaluate the recently published work in the last 6 years.When we looked into the papers during this excluding exercise, we found that most of the papers published prior to 2017 were not relevant to the topic at hand: gender bias in automated decision-making systems. Also, few papers that were published prior to 2017 and had relevant information had additional or updated work published after 2017, hence we arrived at the final exclusion criteria listed above.We implemented the exclusion criteria in three phases of screening. First, we reviewed just the title, keywords, and abstracts of the paper. Then, we reviewed the full text of the papers and created a codebook based on the paper's focus. Finally, we analyzed the methods and implications of the papers in a detailed manner to arrive at the final review corpus. We removed a total of 19 papers from our results based on the criteria mentioned, thus resulting in 173 papers. Next, we excluded papers that showed up twice which further reduced the number of papers in the corpus to 154.We further screened the remainder of 154 papers based on their titles and abstracts. In this step, we carefully reviewed the collection of papers to makes sure they had relevant keywords which includes words like “gender”, “bias/biases”, “machine learning”, and/or “artificial intelligence”, within the titles and abstracts. Based on the presence of these keywords, we classified papers into two categories: “relevant”, “some relevancy”, and “irrelevant”. During this processing we excluded more papers that fell into irrelevant category, resulting in total corpus count of 149.In this phase, we conducted a quick review of the full text of the 149 papers mainly focusing on the study methodology, implications, algorithms explored, and solutions proposed. In this step we removed 6 papers from the corpus because the full-text version of these papers were inaccessible or behind a paywall. We also further excluded 23 papers because they did not directly discuss about gender biases in the automated decision-making systems. Thus, we ended up with a total of 120 papers as summarized by the diagram .After the screening, we conducted a detailed review of 120 publications focusing on the algorithms explored in the paper, methodology followed, and solutions proposed to detect and/or mitigate gender biases in the automated decision-making systems. We also further analyzed the papers based on the publication year to trace the research trend on this topic over the years. As demonstrated in the Forty-eight out of 120 papers collected for this study discussed about the presence of gender bias in a variety of ML and AI applications. The diverse fields studied in these papers show the varied applications automated decision-making systems have and, it helps us realize the severity of the gender fairness issue. It also demonstrates that unintentional bias can have drastic effects on minority populations.Natural Language Processing (NLP) systems and the presence of gender biases in these systems. Researchers studied NLP algorithms like Word Embeddings, Coreference Resolution and Global Vector of Word Representation (GloVe). In these papers authors discuss the presence of inherent bias in the human languages which is then codified into the ML and AI NLP operations through the data. Here, NLP operations refers to functions like embeddings, coreference resolution, dialogue generation, machine translation, text parsing, sentiment analysis, hate speech detection, and so on  Buolamwini and Gebru  algorithms. Authors studied bias in music recommender systems  to mitigate gender biases in Natural Language Processing (NLP) models , it is a bias detection framework that takes in training dataset, a set of sensitive attributes in the dataset like race, gender, age etc. and classifiers to be used for the computation. Using these inputs DENOUNCER is able to conduct a fairness detection and present the true vs. the predicted value, hence making it plain for the model creators if their models are biased. For example, if a user wants to check if race is a fair classifier for criminal recidivism prediction. They can use DENOUNCER to select a dataset, COMPAS, and elect a classifier  and run the prediction. The DENOUNCER would run different prediction algorithms and compare the outcome of the prediction  to real outcome  and conduct fairness evaluation of the outcome. Thus, the result will reflect if the classifier selected scored high in fairness evaluation or not  to understand the interaction of gender-bias in the results and user's perception of it. ASI is a scoring system in which participants are measured for two types of sexism: Hostile Sexism (HS) which views minority gender in negative light and Benevolent Sexism (BS) which views minority gender in less negative albeit through stereotypical lenses. In their study, the researchers show users a grid of images and ask the users to guess the query used in the search engine which might have resulted in those images. Then the researchers reveal the actual query used and ask the participants to compare their answer with the query. These questions along with the ASI score helped the researchers to arrive at the conclusion that participants who scored higher in the ASI scale, i.e., displayed sexist tendencies, tended to not see any gender-bias in the biased search result images. This study conducted on 280 participants across US, UK and India reveals that a biased search engine perpetuates gender stereotypes and sexism , Gender Generalization (90% agreement), and abusive language (80% agreement) as three significant sub-types of gender bias , it is imperative to understand if this machine translation tool is gender biased. The authors conducted a quantitative analysis of Google Translate using gender-neutral languages supported by the system, which included 14 languages including Malay, Estonian, Finnish, Hungarian, Bengali, Swahili, Chinese, and others. Using statistical translation tools, they show that Google Translate is gender-biased toward male defaults (tends to default to he/him/his pronouns more frequently) without any reason. It assigns she/her/hers pronouns when adjectives like Another case study looks into the Automated Deception Detection tool and tries to see if the prediction provided by the software is stereotyping non-verbal behaviors (NVB) cues given by people. Crockett et al. utilize raw video data collected from 32 participants to test if there is a statistically significant gender effect on the deception detection system. Through this, they find a gender effect in NVB cues generated by people, which means we cannot use a system trained on female data to detect deception on male participants and vice-versa  we might have missed some papers which addressed and analyzed the impact of machine learning biases. We aim to address our limitation in the future by looking into more literature published in recent times and in different languages to get a better view of the current research landscape focusing on gender bias in automated decision-making systems. We also aim to conduct case studies and user studies to further extend our work in gender bias in ML/AI systems.ML/AI assisted systems have seamlessly integrated into our lives, quietly manipulating the items we buy, the entertainment we see and the doctors we visit. In our study, we reviewed n = 120 academic literature published on gender bias in automated decision-making systems. Through the literature review we identify the different areas explored in the research that have demonstrated presence of gender bias. We review different methods of study utilized in the papers to understand and analyze the state of gender bias in automated systems. We also detail the bias detection and mitigation methods proposed by the researchers. Finally, we highlight the areas that require more focus in the future research to further push the conversation of gender bias in ML/AI assisted systems. In conclusion, we believe the research into gender-bias in ML/AI assisted system should provide the definition and identification of a gender-biased system. Also, researchers should promote algorithmic auditing and interdisciplinary approach to design and develop ML/AI systems. Researchers should also conduct more user studies to bring the users perception into a biased system.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.SS has collected the papers analyzed and conducted the analysis as well as reporting of the data. SD has worked on the study ideation, analysis, design, and parts of writing the work. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.Any opinions, findings, and conclusions or recommendations expressed in this material are solely those of the authors and do not necessarily reflect the views of the University of Denver.