However, it remains unclear which signaling transduction pathway(s) of TNFR2 is required for the stimulation of Tregs. Previously, it was shown that the interaction of TNF–TNFR2 resulted in the activation of a number of signaling pathways, including p38 MAPK, NF-κB, in T cells. We thus examined the role of p38 MAPK and NF-κB in TNF-mediated activation of Tregs, by using specific small molecule inhibitors. The results show that treatment with specific p38 MAPK inhibitor SB203580, rather than NF-κB inhibitors , abrogated TNF-induced expansion of Tregs in vitro. Furthermore, upregulation of TNFR2 and Foxp3 expression in Tregs by TNF was also markedly inhibited by SB203580. The proliferative expansion and the upregulation of TNFR2 expression on Tregs in LPS-treated mice were mediated by TNF–TNFR2 interaction, as shown by our previous study. The expansion of Tregs in LPS-treated mice were also markedly inhibited by in vivo treatment with SB203580. Taken together, our data clearly indicate that the activation of p38 MAPK is attributable to TNF/TNFR2-mediated activation and proliferative expansion of Tregs. Our results also suggest that targeting of p38 MAPK by pharmacological agent may represent a novel strategy to up- or downregulation of Treg activity for therapeutic purposes.There is now compelling evidence that tumor necrosis factor (TNF) preferentially activates and expands CD4 Furtherin vitro and in vivo studies  by TNF–TNFR2 interaction.The biological functions of TNF are transduced by two receptors, TNFR1 (p55) and TNFR2 (p75) . In cont studies –29. It w studies . It has  studies , 32. Thuin vitro and in vivo experimental settings. The results showed that SB203580 potently inhibited TNF-induced proliferative expansion of Tregs. Furthermore, other stimulatory effects of TNF on Tregs, such as upregulation of TNFR2 and Foxp3 expression were also abrogated by SB203580. Therefore, p38 MAPK represents a major component of signaling pathway of TNFR2 in the activation of Tregs.In this study, we investigated the effect of SB203580, a p38 MAPK-specific inhibitor, on the expansion of Tregs induced by the interaction of TNF–TNFR2 in both in vitro effect of p38 MAPK-specific inhibitor SB203580 . The proportion of Foxp3+ Tregs in the cultured CD4+ T cells was also markedly reduced by SB203580 treatment, with a percent inhibition of 24.9–47.05% . Furthermore, the absolute number of Tregs in each well was markedly reduced . In contrast, treatment with two NF-κB inhibitors  failed + T cells were cultured with IL-2, with or without TNF. The cells were treated with SB203580 (1–25 µM). As shown in Figure p < 0.01–0.001), with a percent inhibition of 32.3–62.6% . Thus, inhibition of p38 MAPK with SB203580 can inhibit surface expression of TNFR2 on TNF-treated Tregs.The surface expression levels of TNFR2 are correlated with immunosuppressive function of Tregs , 6. Previn vitro and in vivo settings . Similarly, the proportion of Foxp3+ cells in CD4 T cells present in peripheral blood and lymph nodes following intraperitoneal LPS injection was also increased compared with control mice , which is consistent with our previous report (p < 0.01). Treatment with single dose of SB203580  immediately after LPS treatment completely inhibited LPS-induced expansion of Tregs . CD152 (CTLA4) is a characteristic marker and an effector molecule of Tregs. Expression of CD152 in Tregs was upregulated by LPS-treatment , and the elevation of CD152 expression in LPS-treated mice was completely abrogated by SB203580 treatment  indicated that both p38 MAPK pathway and NF-κB pathway in Tregs were markedly activated after TNF stimulation . Recent evidence also showed that TNFR2-specific TNF-variant scTNF(143N/145R) treatment markedly activated p38 MAPK and NF-κB in purified human Tregs  C57BL/6J (8–12 weeks old) were provided by the Animal Facility of University of Macau. The animal study protocol was approved by Animal Research Ethics Committee of University of Macau. Antibodies purchased from BD Pharmingen  consisted of PerCP-Cy5.5 anti-mouse CD3 (145-2C11), PE anti-mouse CD4 (GK1.5), PE anti-mouse CD120b/TNFR2 (TR75-89), PerCP-Cy5.5 anti-mouse CD25 (PC61), PE anti-mouse CD152 (UC10-4F10-11). Antibodies purchased from eBioscience include PE-Cy7 anti-mouse CD4 (GK1.5) and APC anti-mouse/rat Foxp3 staining set (FJK-16s). Functional grade purified hamster anti-mouse CD3ε (145-2C11), Functional grade purified hamster anti-mouse CD28 (37.51), recombinant mouse IL-2 and TNF were obtained from BD Pharmingen. Bay 11-7082 (Cat#: B5556), and Lipopolysaccharides (rough strains) from Salmonella (LPS) (Cat#: L9764) was purchased from Sigma-Aldrich. Sulfasalazine (Cat#: S1576) and SB203580 (Cat#: S1076) was obtained from Selleckchem. LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit  was ordered from Thermo Fisher Scientific.+ T cells were purified from lymphocytes by using CD4 (L3T4) microbeads  and MS column (Miltenyi Biotec). MACS-Purified CD4+ cells were labeled with CFSE and cells (5 × 104 cells/well) were cultured in a 96-well plate, then stimulated with IL-2 or IL-2 plus TNF, in the presence or absence of SB203580 (1–25 µM) for 3 days. Proliferation of Tregs was assessed by CFSE dilution assay, and the proportion of Foxp3+ cells in CD4+ subset and TNFR2 expression on Tregs were analyzed with FACS. In some experiments, FACS-sorted CD4+CD25+ cells  were stimulated with plate-bound anti-CD3ε Ab (10 µg/mL) and soluble anti-CD28 Ab (2 µg/mL) in the presence of TNF (10 ng/mL) or medium alone, with or without 25 µM SB203580, for 3 days. Expression of Foxp3 and TNFR2 were analyzed by FACS.Mouse lymphocytes were harvested from spleens, axillary lymph nodes, inguinal lymph nodes, and mesenteric lymph nodes. CD42O). After 24 and 72 h, mice were sacrificed. The spleens, lymph nodes at axillary, inguinal, and mesenteric regions, and blood were harvested for FACS analysis.C57BL/6J mice were injected intraperitoneally (i.p.) with 200 µg of LPS in 0.2 mL PBS. Some mice were treated with SB203580  immediately after LPS treatment. SB203580 were dissolved in a stable solvent system .+CD25+ T cells were stimulated with TNF (100 ng/mL), with or without selected inhibitors  for 30 min. The cells were homogenized in RIPA buffer containing a cocktail of proteinase and phosphatase inhibitors. Protein samples were separated on a SDS-PAGE gradient gel  and transferred to PVDF membranes. The blots were blocked with 5% BSA for 1 h and incubated with phospho-p38 antibody  and phospho-NF-κB p65 antibody  overnight at 4°C. The blots were then incubated in HRP-conjugated secondary antibody  for 1 h at room temperature, developed in ECL solution (Thermo Fisher Scientific) for 1 min, and exposed by G-Box imager. The blots were then incubated in stripping buffer (Thermo Fisher Scientific) at 37°C for 15 min and reprobing with IκBα antibody  or p38 antibody  or NF-κB p65 antibody  or GAPDH antibody .MACS-purified CD4t test using GraphPad Prism 6.0. Comparisons of more than two groups of data were analyzed by one-way ANOVA by using GraphPad Prism 6.0 .Comparisons of two groups of data were analyzed by This study was carried out in accordance with the recommendations of approved guidelines of Animal Research Ethics Committee, University of Macau. The protocol was approved by the Animal Research Ethics Committee of University of Macau.TH, SL, SC, JY, and XW performed the experiments. TH, ZB, and XC designed the experiments and wrote the manuscript. All authors agree to the submission of the manuscript.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.


The clinical importance of heart failure with preserved ejection fraction (HFpEF) has recently become apparent. HFpEF refers to heart failure (HF) symptoms with normal or near-normal cardiac function on echocardiography. Common clinical features of HFpEF include diastolic dysfunction, reduced compliance, and ventricular hypokinesia. HFpEF differs from the better-known HF with reduced ejection fraction (HFrEF). Despite having a “preserved ejection fraction,” patients with HFpEF have symptoms such as shortness of breath, excessive tiredness, and limited exercise capability. Furthermore, the mortality rate and cumulative survival rate are as severe in HFpEF as they are in HFrEF. While beta-blockers and renin-angiotensin-aldosterone system modulators can improve the survival rate in HFrEF, no known therapeutic agents show similar effectiveness in HFpEF. Researchers have examined molecular events in the development of HFpEF using small and middle-sized animal models. This review discusses HFpEF with regard to etiology and clinical features and introduces the use of mouse and other animal models of human HFpEF. The condition known as heart failure with preserved ejection fraction (HFpEF), in which the fraction of blood pumped from the heart by each heartbeat remains near normal is an increasing cause of illness and death. Available treatments are unsatisfactory. Heart functions also appears normal or near normal in echocardiography imaging, but patients suffer some typical symptoms of heart failure including shortness of breath, excessive tiredness, and reduced ability to exercise. Gwang Hyeon Eom and Somy Yoon at Chonnam National University in Gwangju, South Korea, review understanding of the causes and clinical features of HFpEF, and the development of animal models to aid research into the condition. Studies in rodents are revealing molecular level cellular changes that may be involved in causing HFpEF. The insights gained will hopefully lead to more effective treatments. During diastole, the ventricle rapidly increases in volume, with an abrupt decline in intracardiac pressure2. When ventricular pressure becomes equal to atrial pressure, the valve between the atrium and ventricle opens. At that moment, blood from a large vein fills the ventricle in early diastole; then, forceful contraction of the atrium moves a small amount of blood into the ventricle. Systole follows diastole. After filling, the ventricle begins to contract. When the contractile pressure exceeds that in a large artery, blood in the ventricle is pushed into the artery. Normal performance in both diastole and systole is required for normal cardiac function2.The heart supplies oxygen and nutrients to the body through the blood circulation. Rhythmic cardiac motion is required to achieve this function. Cardiac activity can roughly be divided into two phases: diastole (or relaxation) and systole (or contraction)3 and is exemplified by systolic HF4. The major characteristic of systolic HF is decreased contractile function4. A patient with HF symptoms and an ejection fraction <40% on echocardiography is diagnosed with systolic HF. Systolic HF is known as HF with reduced ejection fraction (HFrEF)4. The most common cause of HFrEF is a loss of effective left ventricular myocardium due to myocardial infarction, which is an ischemic event5. Reduced circulation triggers compensatory mechanisms6. Decreased perfusion in the kidney results in activation of the renin-angiotensin-aldosterone system (RAAS)7. At the same time, increased residual blood in the chamber at end-systole stresses the ventricular wall; this promotes neurohormonal activation and leads to eccentric heart remodeling8. Because of its central role in the development of HFrEF, targeted therapy against the RAAS  is effective in HFrEF patients4. Beta-adrenoceptor regulators are standard therapies for HFrEF, as these agents significantly reduce cardiac burden and automaticity4.Heart failure (HF) implies an inadequate blood supplyMany patients with typical HF symptoms, such as shortness of breath, limited exercise capacity, or cough, have a normal ejection fraction (>50%) on echocardiography, indicating that systolic function is normal. These patients are diagnosed with HF with preserved ejection fraction (HFpEF). HFrEF refers to systolic HF, but HFpEF does not imply diastolic HF.9. HFpEF is more common in women, while HFrEF is more common in men10. While many patients have been categorized as having “HFpEF,” their individual characteristics are heterogeneous11. Big data analysis of the HFpEF cohort has revealed several risk factors. Shah et al. identified the clinical features of HFpEF: 68% were women, 90% had hypertension, 70% had obesity, 62% had hyperlipidemia, and 52% had diabetes mellitus (DM)12. Another research group further supported this finding. Kao et al. clarified the phenotypes of HFpEF: 59% were women, 100% had DM, 84% had hyperlipidemia, and 75% had obesity (>30 kg/m2 body mass index) and hypertension13. Both studies consistently confirmed common cardiometabolic features: women with hypertension, obesity, DM, or hyperlipidemia 4. Large-scale clinical trials have been instituted to identify effective drugs based on the risk factors for HFpEF23, some of which are ongoing.Pharmacological treatment for HFrEF is well established. Beta-adrenoceptor blockers, RAAS inhibitors, and mineralocorticoid receptor blockers can reduce morbidity and mortality in HFrEF patients24. However, aberrant activation of the neurohormonal system worsens cardiac remodeling in HFrEF, implying that β-adrenergic blockage is beneficial25. The β-blockers allow the heart to rest through a significant reduction in cardiac load. Various studies using β-blockers have been conducted using different patient enrollment criteria. However, the overall outcome of β-blocker trials revealed no survival benefit for HFpEF patients22. HFmrEF patients showed better survival than HFpEF patients with β-blocker treatment20.The beta (β)-adrenergic system is primarily located in the heart (β1) and smooth muscle (β2). When endogenous catecholamines activate the β1 signaling cascade, the heart contracts more rapidly and forcefully26. When the baroreceptors in the carotid sinus detect falling blood pressure or when the filtration flow rate in the kidney stimulates the macula densa, the homeostatic system in the body activates the RAAS27. The main active component of the RAAS is angiotensin II. Hence, the administration of an angiotensin-converting enzyme inhibitor (ACEi) to block the generation of the active form of angiotensin from angiotensin I, or the use of an angiotensin receptor blocker (ARB) or AT1 receptor blocker, can inhibit HF progression28. Although ACEi/ARB agents are effective in HFrEF, clear evidence was not observed in HFpEF22.The RAAS controls both blood pressure and the circulating volume of blood by regulating vascular tone and sodium reabsorption, respectively29. Pfeffer et al. reported that aldosterone antagonists could be beneficial for HFpEF in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) clinical trial in the United States30. Beldhuis et al. warned that the prescription of an aldosterone antagonist should be avoided in HFpEF with decreased renal function31. However, cautious interpretation of the TOPCAT study is required. Other than in patients in the United States, aldosterone antagonists have shown no benefits. Furthermore, no positive outcome was found in the analysis of summary data from multiple facilities30. More detailed and long-term studies involving different countries or races are needed.Aldosterone, an endogenous mineralocorticoid hormone, accelerates myocardial remodeling, especially after an ischemic event. Mineralocorticoid receptor blockers alleviate myocardial fibrosis and extracellular matrix deposition32. The use of a nitric oxide donor is the most useful strategy for the reduction in peripheral vascular resistance, i.e., afterload. Redfield et al. performed a trial using isosorbide mononitrate, an oral long-acting nitric oxide derivative, and found that exercise capacity was not improved23. The Inorganic Nitrite Delivery to Improve Exercise Capacity in HFpEF (INDIE-HFpEF) clinical trial enrolled additional patients. Disappointingly, inorganic nitrite failed to improve exercise capacity33. Further, sildenafil, a phosphodiesterase-5 inhibitor, was evaluated. Redfield et al., in the Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (RELAX) trial, reported that sildenafil treatment failed to improve exercise capacity or clinical status34. Several trials with limited numbers of patients showed promising outcomes with the use of lipid-lowering statins35. Confirmation would require a well-designed clinical trial with adequate power. In summary, clearly effective medication treatment for HFpEF remains undefined.The absence of clear evidence of benefit from β-blockers, RAAS inhibitors, or aldosterone antagonists prompted numerous clinical trials. Physicians chose medicines according to the risk factors for HFpEF to perform these clinical trials. Nitric oxide directly stimulates and relaxes vascular smooth muscle36, and its indication has expanded to chronic HFrEF38. Ivabradine significantly reduced the risk of hospitalization in HF patients who were tolerant to beta-blockers and had a left ventricular ejection fraction of <35% and a heart rate that exceeded 70 beats per minute38. A randomized, double-blinded, placebo-controlled trial to assess the effectiveness of ivabradine in HFpEF was completed . Unfortunately, the reduction in heart rate by ivabradine failed to improve the clinical outcome of HFpEF39.Recently, several therapeutics have been developed for HFrEF. The negative inotropic agent ivabradine was first approved in angina40. A randomized clinical trial on HFpEF  with ARNI has completed patient enrollment. The PARAGON-HF trial will determine the usefulness of the combination drug ARNI in HFpEF41.Angiotensin receptor neprilysin inhibitor (ARNI) is a combination drug that includes conventional angiotensin blockers and neprilysin inhibitors. Although neprilysin degrades several vasoactive peptides, the natriuretic peptide is a notable substrate. Compared with ACEi or ARB, ARNI reduced the rate of cardiovascular death or hospitalization by 20%42. A notable clinical outcome was observed in the EMPA-REG trial: cardiovascular events were reduced when the SGLT2 inhibitor was combined with conventional treatment regimens43. New clinical trials such as DELIVER (NCT03619213), EMPEROR-Preserved (NCT03057951), and PRESERVED-HF (NCT03030235) are ongoing to determine the therapeutic potential of SGLT2 in HFpEF patients45.It is noteworthy to observe the clinical outcome of the sodium-glucose co-transporter-2 (SGLT2) inhibitor trial. Originally, SGLT2 inhibitors were used for the treatment of type II DM. SGLT2 inhibitors interfere with sodium-glucose-linked co-transporter 2 in renal proximal convoluted tubules, which then reduces glucose reuptake46. A significant proportion of HFpEF patients have normal diastolic function and normal cardiac geometry. However, there are more patients with diastolic dysfunction and ventricular hypertrophy than there are patients without diastolic dysfunction. For this reason, an experimental model of human HFpEF generally requires an investigation of diastolic dysfunction, ventricular hypertrophy, interstitial fibrosis, or exercise intolerance47.Clinically, HFpEF and diastolic HF are not synonymous1. Doppler images record two prominent peaks, which are the E (ventricular relaxation) and A  waves. As the E wave primarily reflects diastolic properties, a decreased E wave indicates diastolic dysfunction48. A significant amount of residual blood in the atrium due to impaired diastolic function augments the atrial contribution to ventricular filling, demonstrated by an increased A wave48. A decreased E/A ratio reflects diastolic dysfunction48.Echocardiography is widely utilized to assess diastolic function. Ventricular filling is divided into two phases. Ventricular relaxation in early diastole induces rapid and passive ventricular filling, whereas atrial contraction in late diastole adds a small amount of filling to the ventricle49. For differential diagnosis, the direct measurement of ventricular movement is required. The tissue Doppler imaging mode directly records the mitral valve annulus movement. The two phases of mitral valve motion show waveforms similar to the E and A waves in tissue Doppler mode: the E′ wave represents early diastole, and the A′ wave represents atrial contraction48. Because it directly represents ventricular function, the E′ wave is further diminished in the pseudonormal period49. Therefore, the E/E′ ratio is markedly increased in diastolic dysfunction  strain is widely used for human hypertension research60. Starting at <12 months of age, the SHR strain shows the gradual development of concentric hypertrophy with preserved contractility61. However, the strain develops cardiac decompensation, with left ventricular dilatation and reduced ejection fraction after 18 months62, a phenomenon rarely observed in human HFpEF. The SHR model closely resembles human HFpEF, but the phenotypic onset time is relatively late. It is difficult to maintain a group of these rats for ~1 year, making the use of the SHR strain expensive and time consuming.Many research groups have used rats for hypertension research. Both systolic and diastolic pressures are sensitive to volume overload, the use of hypertensive agonists, and increased salt ingestion60. Young adult Wistar rats (~8 weeks of age) undergo unilateral nephrectomy and are given drinking water with 1% sodium chloride. Starting 1 week after nephrectomy, the rats received subcutaneous injections of DOCA (25 mg in 0.4 mL of dimethylformamide every 4 days) for 28 days. The rats develop concentric hypertrophy, fibrosis, and hypertension63. Moreover, the DOCA-salt model can be used in mice, but blood pressure elevation is limited64. Thus, it is not clear whether the DOCA-salt model truly represents hypertension-induced HFpEF.In addition, the deoxycorticosterone acetate (DOCA)-salt model is an acceptable alternative for hypertension research66. The heart shows concentric hypertrophy, interstitial fibrosis, and diastolic dysfunction. The DSS with a high-salt diet model shows prolonged action potential, resulting in polymorphic ventricular tachyarrhythmia65. The DSS model, however, develops severe hypertension (>175 mmHg), which is uncommon in humans67. To accurately reflect human HFpEF, the DSS model would require the use of an antihypertensive drug.The Dahl salt-sensitive (DSS) rat with high-salt chow is considered a noninvasive and short-term HFpEF model. DSS rats fed 4–8% salty chow for 2 months develop typical HFpEF phenotypesAlthough rats are acceptable models for research on HFpEF, mice are easier to work with than rats. There is no significant change in blood pressure in mice compared with rats. Therefore, most acceptable hypertension models using rats are not comparable to those using mice. The ability to perform large-scale experiments with genetically modified animals and access to an animal bank is easier to accomplish with mice than with rats.64. DOCA salt combined with 1% sodium chloride causes hypertension in rats. Blood pressure is mildly increased or unchanged in mice, although concentric hypertrophy and interstitial fibrosis are observed. Unilateral nephrectomy in mice treated with DOCA salt leads to diastolic dysfunction. Unilateral nephrectomy was performed on C57BL/6 mice, followed by implantation of a DOCA pellet releasing 0.7 mg/day for 3 weeks; in addition, the mice were given 1% sodium chloride drinking water68. The DOCA model showed no difference in heart rate or ejection fraction. DOCA reduced exercise capacity, but pulmonary congestion was uncommon69.The DOCA model is applicable to both mice and rats70. Although applicable in rats, mice are considered more suitable for this model. C57BL/6 mice underwent implantation of d-aldosterone osmotic pumps (0.30 μg/h) after unilateral nephrectomy and were given 1% sodium chloride drinking water for 30 days71. Both systolic and diastolic pressure were elevated, but the heart rate was not changed. Moreover, systolic function was preserved. The mice developed concentric hypertrophy, diastolic dysfunction, pulmonary edema, and locomotor impairment72. Humans with HFpEF show high levels of aldosterone. The elevation of serum creatinine and albuminuria was detected in aldosterone model mice, similar to the findings in humans with HFpEF. Molecular events in the aldosterone infusion model were comparable to those in humans with HFpEF4. Hence, the aldosterone model represents both the clinical phenotype of HFpEF and the molecular events73.In addition, in place of DOCA, infusion with d-aldosterone induces HFpEF74. Constriction can be applied at the aortic root, transverse aorta, descending aorta, or even abdominal aorta75. The aortic root and transverse aorta are frequently selected. The constriction of a large artery generates a steep increase in blood pressure just proximal to the point of manipulation76. However, the segment distal to the constriction maintains normal blood pressure. Hence, the arterial constriction model resembles hypertension-based HFpEF, but the effect is limited to the heart. Aortic root constriction or aortic banding (AoB) usually results in eccentric hypertrophy due to intracardiac volume overload with excessive pulmonary edema55. Massive interstitial fibrosis accompanies hypertrophy, which interferes with ventricular movement. Although mice with AoB transiently maintain physical hemodynamics, they undergo aberrant decompensatory remodeling77. Prolonged AoB exacerbates systolic dysfunction, ultimately leading to HFrEF. The transition from HFpEF to HFrEF is rarely observed in humans4, and the findings after prolonged AoB do not correspond to clinical outcomes. Transverse aortic constriction (TAC) represents partial ligation between the brachiocephalic artery and left common carotid artery. TAC induces less severe vascular resistance and intraventricular volume overload than AoB due to the preservation of the brachiocephalic artery76. TAC induces concentric hypertrophy as well as eccentric hypertrophy with interstitial fibrosis. Moreover, prolonged TAC progresses to systolic heart failure. Hence, diastolic dysfunction induced by pressure overload fails to elicit human HFpEF78.Partial ligation or constriction of a large artery induces an abrupt and severe increase in afterload79, genetically modified db/db80 or ob/ob81 mice are widely used for cardiometabolic research. The db/db mouse has mutant leptin receptors that induce aberrant obesity. This leads to spontaneous insulin resistance, which is comparable to that in human type 2 DM. Without further administration of agonists, the db/db mouse shows diastolic dysfunction with concentric hypertrophy. The left ventricular ejection fraction remains in the normal range. Locomotor ability is associated with the severity of diastolic dysfunction82. The ob/ob mouse lacks leptins. Because of polyphagia, obesity and type 2 DM develop spontaneously. Disease progression in ob/ob mice resembles that in human type 2 DM. However, the use of the ob/ob mouse has some limitations. Leptin governs appetite control in addition to having a cardio-protective role. A loss of leptin impairs contraction-relaxation coupling in cardiomyocytes. Furthermore, leptin deficiency in human obesity is rare, meaning that the ob/ob mouse model is not appropriate for the study of human HFpEF83.Since obesity and DM are notable comorbidities in HFpEF84. Schiattarella et al. considered common comorbidities in human HFpEF and created a model that combined hyperlipidemia and hypertension. They administered a high-fat diet with a nitric oxide synthase inhibitor ad libitum. After 5 weeks, they observed significant impairment of diastolic function, with pulmonary congestion and exercise intolerance. At 15 weeks, significant signs and symptoms of HFpEF had developed84. This model mimics human pathophysiology, implying its suitability for use in research.A noninvasive and nongenetically modified model of HFpEF was recently reported12. The relative life span in rodents is even shorter than in humans, but “old” age in mice is only 18 months. It is difficult to maintain stable conditions during long-term rearing in a research facility. Therefore, the inbred senescence-accelerated mouse prone (SAMP) strain is useful to reduce the time course of aging85. Spontaneous acceleration of aging in the SAMP strain phenotype begins as early as postnatal 6 months. The SAMP8 strain shows cardiac hypertrophy with fibrosis as well as diastolic dysfunction. Systolic function is well preserved. The SAMP8 mouse carries more comorbidities associated with HFpEF than do other mice86. A detailed study using SAMP8 mice is required in the future.In addition, age is an important risk factor for HFpEF4. Furthermore, approximately half of newly diagnosed HF patients have preserved ejection fraction. The social cost of HFpEF has greatly increased, but no verified treatment regimen is available4. This brief review discussed the clinical trials and rodent models used for human HFpEF research. Disappointingly, several treatments that proved effective for HFrEF failed to improve the survival rate or even quality of life in HFpEF patients9. A limited number of Asian studies described the therapeutic potential of lipid-lowering statins, but the power of a randomized, multirace trial is required for verification35. In the TOPCAT study, the efficacy of spironolactone was unclear30. This mineralocorticoid receptor blocker reduced mortality only in patients enrolled in the United States, implying that genetic background could be a confounding factor in HFpEF. Detailed and homogeneous grouping of HFpEF patients could resolve this question.Mortality and morbidity in HFpEF have continuously increased for more than two decades47. Cumulative data revealed that pharmacological intervention for cardiac hypertrophy ameliorates cardiac fibrosis accompanying the ventricular stiffness that results in diastolic dysfunction87. Histone deacetylase (HDAC) inhibitors are considered promising for the treatment of cardiac hypertrophy and therefore diastolic dysfunction90. In a rodent model, cardiac hypertrophy was completely blocked when HDAC inhibitors were concurrently administered90. Moreover, our group showed that HDAC inhibitors could induce the regression of preexisting hypertrophy and fibrosis91. HDAC inhibitors should be considered novel therapeutic agents for HFpEF. HDAC inhibitors were first developed as an anticancer agent, which suggests that using HDAC inhibitors as HFpEF modulators may have cytotoxic side effects. In fact, notable cytotoxicity-associated adverse reactions of HDAC inhibitors such as SAHA include hair loss, diarrhea, peripheral numbness, or tingling sensation92. Furthermore, our group demonstrated vascular calcification as a potential side effect of HDAC inhibitors93. Since DM is one of the notable comorbidities of HFpEF, using HDAC inhibitors to treat vulnerable patients should be avoided. In addition, immune modulators may be useful in HFpEF. Hulsmans et al. reported the contribution of macrophages to diastolic dysfunction94. In fact, a clinical trial using interleukin-1β, the Interleukin-1 Blockade in Heart Failure with Preserved Ejection Fraction (HFpEF): A Randomized Placebo-controlled Double-Blinded Study (D-HART2), has been completed and the results have been reported95. While systemic inflammation was significantly improved, survival benefits were not observed. The power of the D-HART2, however, was relatively small, which was a weak point of the study95. Thus, another immune modulator should be considered. Endothelial dysfunction failed to produce nitric oxide or improvement in vascular elasticity. Restoration of either endothelial nitric oxide synthase activity or cGMP activity was attempted. Organic nitrate donors and inorganic nitrate suppliers showed no benefit for exercise intolerance33.Rats and mice have been used for the study of human HFpEF. Most rodent HFpEF models show concentric or eccentric hypertrophy, interstitial fibrosis, and diastolic dysfunction. For the assessment of underlying molecular events, vascular inflammation, endothelial dysfunction, or microvascular uncoupling was suggested4. Numerous clinical trials have been instituted, but these require long-term follow-up. To correctly understand HFpEF, an animal study of HFpEF should be conducted in parallel with a human study. Promising outcomes from animal research should be considered when developing treatment regimens in humans. Combined comorbidity is common in humans with HFpEF, making the development of a rodent model difficult. It is noteworthy that a rodent model with more than two comorbidities in a single mouse has been generated84. Nonetheless, understanding HFpEF in a rodent model could aid in the understanding of human HFpEF.Physicians have focused on symptom control due to the lack of therapeutic agents


This is due to a variety of reasons, but chiefly has been attributed to the lack of clear and consistent flagging of retracted papers (often only the retracting note is available) and to the lack of a central repository, for all retracted papers, which could be used for checking the status of the literature cited by a given manuscript in preparation. While manuscript flagging has improved in recent years, it is only last year that the group led by Ivan Oransky released a massive retraction database2, which has been proposed to cross-check for retractions of the literature cited by each new manuscript3. This database seems to be still far from complete and it is dependent on the efforts of a private foundation, which relies on donations for its operations: an intolerable risk for such an important public resource. However, referees and editorial boards are strongly advised to make use of it for checking manuscripts’ references, and publishers are urged to develop automatic plugins or tools, which can facilitate this editorial task.Retractions of scientific papers are valuable because they alert the scientific community that some specific scientific findings should no longer be considered valid, either because the underlying data are not solid or because the conclusions were unsupported by the data. In principle, once it is withdrawn, a paper is expected to be cited only with reference to its retraction, not in support of some research published later. However, at least in the last 30 years, this does not seem to be the case. We have witnessed the development of the so-called “zombie literature”: more often than expected, retracted papers continue to be cited in a positive way, as if they were still a valid scienceTo clarify the risks of not doing so, here, I will highlight two cases of retracted papers where I was personally involved in uncovering the data manipulation.5 authored by the group led by professor Federico Infascelli, purportedly “proving” the health risks of GMO food consumption, were retracted for containing manipulated images. The two papers had been used to campaign against GMO food; thus, the two retractions went viral in the scientific community, so that one of them was listed among the top 10 retractions occurring in 20166. Despite the wide resonance of those retractions, we can read in a late 2018 paper7 discussing the risks for health of consuming GMO food the following words: “A number of studies concerning this issue have previously been published concluding absence of any detrimental effects on human and animal health […]. Opposite opinions, however, do exist and are experimentally documented ”, where “Tudisco et al., 2010”4 refers to one of the two retracted papers.In 2016, two papersThe damages of reporting as valid data retracted for being manipulated, completely ignoring the retraction of that paper, are immediately apparent: a socially important issue, which has been already settled by science, is reported as still open to debate, in a journal published by a respected publisher, such as Wiley.8 citing the original papers as examples of bad science. Despite the intention of the authors of the 2017 paper, simply discussing bad science and citing some examples will increase the citational impact of the authors found guilty of misconduct, i.e., retracted papers will continue to boost the bibliometric indexes of their authors even when they are simply pointed at for being fraudulent.A more subtle effect is evident when considering negative citations of the two retracted papers, like, for example, in the case of a 2017 paper9. The number of problematic papers identified during the investigation steadily grew up, before the news spread to the public in 2013;10 by the end of 2018, Fusco and his co-authors had retracted over 22 papers and published no fewer than 10 errata/corrigenda, which can be still considered a provisional figure for the overall impact expected as a consequence of the original investigation. Seven years after the appearance of the first retraction for image manipulations in 2013, there are some lessons to be learnt by the extensive literature correction caused by this case of misconduct: to what extent was the original damage truly accepted by the scientific community? How aware is this community of the retracted papers? A possible way to investigate this is to analyse how, and if, retracted papers are still cited by the scientific community, after the retractions occurred. In this case, one can refer to the data provided by Google Scholar: although the citations counted by this service are known to exceed those recorded by Scopus or Clarivate, they have the advantage of including also citations in a highly diverse range of scientific production, including, for example, PhD theses. For this reason, Google Scholar citations may best serve our aim, which is to measure the overall citational impact of the papers cited after their retraction. Ten publications were retracted in the period 2013–2017 (2018 retractions are too recent to count the citation); for eight of them, citational data are available at the time of preparation of this paper. Overall, those eight papers received 638 citations; on average, 18.6 ± 8% of the citations received by a retracted paper occurred at least 1 year after its retraction, adding up to 71 citations. The vast majority of these 71 citations are non-negative or positive , possibly in connection with the referring authors being not aware of the retractions.The second example I will refer to includes a larger sample of retracted papers to gain further insight on their citational impact. In 2011, I started investigating the scientific production of Alfredo Fusco, an Italian researcher in the field of preclinical oncology, after identifying seven papers containing manipulated images, which were flagged during an automated image screening11. The retracted paper12 appears to be cited as reference n.9 by another paper published in 201813. The retracted paper and the 2018 paper have one author in common: professor Alfredo Fusco, who of course cannot be unaware of his previous retraction. Thus, we have not only to fear “zombie papers”, but also Frankenstein doctors, who are resurrecting their withdrawn literature, pretending that their science was genuine and appropriating some more self-citations.Moreover, there is a further sinister phenomenon: authors of the retracted papers, which (by definition) are aware of the retraction, continue to cite those papers. Consider for example one of the notes of retraction by Fusco et al., appearing on May 12, 2016There is an urgent need for reviewers and editorial boards to check the reference list of submitted manuscripts, to prevent the spreading of zombie literature.We need central repositories of retracted papers, in order to facilitate the work of editorial boards and reviewers.Ideally, this database should be under public control; anyway, it must be consistently funded (an interesting possibility would be to have it funded by the Committee for Publication Ethics—COPE).We need to ensure that citations, referring to retracted papers when discussing their retraction, do not boost the bibliometric indexes of the retracting authors.I hope at this point to have made some points clear:


GSTT1/GSTM1 genes and single nucleotide polymorphisms (SNPs) in the TNF-α gene at positions −308/−238 with the risk and outcome in MM and sensitivity to bortezomib under in vitro conditions. One hundred newly diagnosed MM patients and 100 healthy blood donors were genotyped by means of multiplex PCR (for GSTs) and PCR-RFLP (for TNF-α). In a subgroup of 50 MM patients, bone marrow cells were treated with bortezomib in vitro. Patients with −238GA+AA or GSTT1-null genotypes had 2.0 (p = 0.002) or 2.29 (p = 0.013) fold increased risk of MM. The interaction effects and risk of MM were observed in GSTT1/GSTM1-null , −308/−238GA+AA , as well as in all combinations of −308 with GSTs. The −308/−238GA+AA genotypes in comparison to GG were associated with earlier MM onset−61.14 vs. 66.86 years (p = 0.009) and 61.72 vs. 66.52 years (p = 0.035), respectively. Patients with GSTM1-present had shorter progression-free-survival  and overall-survival  compared with GSTM1-null. We did not observe relationship between response rate and studied polymorphisms. The in vitro study revealed significantly higher number of apoptotic cells at 12 nM of bortezomib in GSTT1-present, GSTM1-null/present, −308GG and −238GG/GA+AA genotypes. Our findings comprise large analysis of studied polymorphisms in MM.Oxidative stress and systemic inflammation are closely linked with increased risk of cancer development. Tumor necrosis factor alpha (TNF-α) is one of the pro-inflammatory cytokines. Glutathione S-transferases (GSTs) are enzymes involved in oxidative stress handling. Polymorphisms of genes encoding mentioned molecules may potentially influence the risk and the outcome in neoplastic diseases. Multiple myeloma (MM) is a hematological malignancy characterized by clonal, atypical plasma cell proliferation. In the present study we investigated the association of deletion polymorphisms in  Multiple myeloma (MM) is a malignant clonal expansion of plasma cells in the bone marrow . Some geGSTT1 (locus 22q11.2) and GSTM1 (locus 1p13.3), respectively  and introns are removed (6 kbp deletion), but promoter and other non-coding regions  are present. Larger deletion (9 kbp) is observed in GSTT1 gene (exon count = 8), and it causes loss of structural gene and some regions of flanking sequences. Null genotype results in a complete lack of corresponding enzyme activity (The reactive oxygen species (ROS) neutralization and cellular oxidative stress handling involves glutathione (GSH) system, which consists, among others, of glutathione-S-transferases (GSTs) , 6. GSTsectively , 6. The TNF-α gene (locus 6p21.33) contains single nucleotide polymorphisms (SNPs) at positions −308 (rs1800629) and −238 (rs361525) in the 5′ promoter region. Both SNPs are characterized by the substitution of guanine (G) by adenine (A). In the case of both −308G>A or −238 G>A polymorphisms the presence of A-allele is associated with higher transcription rate and TNF-α production  secretion , 10. TNFoduction . Enhanceoduction .The introduction of proteasome inhibitors and new immunomodulatory drugs (IMiDs) in the treatment of MM resulted in improvement of overall survival (OS) relative to previous observations , 15. BorGSTT1, GSTM1, and TNF-α genes as genetic indicators of risk and progression of MM. A few similar studies have assessed the significance of GSTT1 and GSTM1 polymorphisms in MM (in vivo and in vitro) and combination with −308 G>A and −238G>A TNF-α polymorphisms. It is very important to define the role of both TNF-α polymorphisms in MM.In the current study, we investigated the influence of polymorphisms in ms in MM , 19. HowIn this paper we present analysis for studied genetic variants individually and in different combinations to evaluate the role of these genes in modifying the susceptibility toward MM, as well as survival and response to treatment with thalidomide and/or bortezomib. Furthermore, we have analyzed whether these polymorphisms predict sensitivity to bortezomib in cell cultures derived from studied patients. To our knowledge the presented results were not previously published by other authors.Bone marrow aspirates and peripheral blood samples were collected from 100 newly-diagnosed patients with MM, who were hospitalized at the Chair and Department of Hematooncology and Bone Marrow Transplantation, Medical University of Lublin in years 2013–2019. The study obtained a positive opinion from the Bioethics Committee (no. KE-0254/165/2013 and no. KE-0254/337/2016), according with the ethical standards established by the Helsinki Declaration. The patients and healthy blood donors provided written informed consent.The characteristics of MM patients is shown in Therapeutic induction regimens consisted of thalidomide and/or bortezomib combined with steroids and/or cyclophosphamide. 37 MM patients underwent autologous hematopoietic stem cell transplantation (auto-HSCT). Response to treatment was evaluated using the International Myeloma Working Group guidelines, and classified as stringent complete remission (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), minimal response (MR), stable disease (SD) or progressive disease (PD) as described elsewhere , 21. Oven = 100) and bortezomib treatment (n = 50). MM patients without chromosomal aberrations were included in the in vitro bortezomib study.Peripheral blood was used for DNA isolation and to determine polymorphisms. Cell cultures were established from bone marrow aspirates to carry out the research associated with cIg-FISH  attending the Regional Blood Donation and Blood Treatment Center in Kielce. The mean age of blood donors was 34.4 years (range 18–61 years). The inclusion and exclusion criteria for MM patients and control group are shown in DNA isolation from peripheral blood was performed using a commercial kit  according to manufacturer's procedure. The concentration and quality of DNA was checked using the NanoDrop device .GSTT1 and GSTM1 polymorphisms, the multiplex PCR method was applied. The −308 (rs1800620) and −238 (rs361525) polymorphisms of TNF-α gene were assessed by PCR restriction fragment length polymorphisms (PCR-RFLP). The primers, enzymes used and band sizes obtained in multiplex PCR and PCR-RFLP methods are shown in For analysis of For the multiplex PCR each reaction mixture (25 μl) contained 100 ng genomic DNA and PCR buffer , dNTPs mixture , HD polymerase  and primers (10 μM of each). The method used was by Abdel-Rahman et al. with minor modifications . The mixTNF-α polymorphism was analyzed according to the Wilson's protocol with modifications , t were tested by cIg-FISH according to Ross et al. recommendations , and Vysis IGH/MAF DF FISH Probe Kit for detection of t. Fluorescent microscopic analysis was performed by scoring 100 AMCA-positive plasma cells to determine the frequency of each aberration. Cut off level were 20% for deletion probes and 10% for dual fusion probes, according to the recommendations of the European Myeloma Network  and ndations . Culturendations , 28. The Network .n = 50) (mean number of plasma cells was 31.31% ± 20.69) were stratified on Lymphoprep  and lymphocyte fraction was used to established cell cultures, which were grown in 15 ml of culture medium—RPMI 1640 with L-glutamine ; 10% inactivated fetal calf serum , 1% antibiotic antimycotic , and different doses of bortezomib −1 nM/2 nM/4 nM/8 nM/12 nM. Bortezomib was dissolved in DMSO and stored at −80°C. The final DMSO concentration in culture medium was <0.1%. As a control, cell cultures without bortezomib (with 0.1% DMSO) were used. The amount of 1–1.5 ml of lymphocyte fraction (from each patient) was added, respectively, to the 15 ml of culture medium. The cultures were grown at 37°C in the atmosphere of 5% CO2 for 24 h (without granulocyte colony-stimulating factor). The cell cultures were routinely terminated and cell suspensions were prepared to determine the number of apoptotic, necrotic, and viable cells by means of Annexin V-Cy3 Apoptosis Detection Kit according to manufacturer's protocol . For fluorescence microscopy, viable cells were stained with 6-CF (6-carboxyfluorescein)—green, necrotic cells were stained only with AnnCy3 (Annexin V Cy3.18). Cells starting apoptotic process were stained both with AnnCy3 (red) and 6-CF (green) (Bone marrow aspirates ( (green) . Plasma  (green) .t-test for continuous variables and Chi-square test for categorical variables. The association of studied polymorphisms with prognostic factors was evaluated using Chi-square test or Fischer's exact test . The quantitative data was shown as a frequency or percentage. Deviation of genotype frequencies in controls and cases from Hardy-Weinberg equilibrium (HWE) was assessed by Chi-squared test with Yates's correction for the groups with <5 patients  software.Laboratory values of MM patients with polymorphisms were compared using an independent patients . For theorphisms . LogistiTNF-α) for healthy individuals (controls) and MM patients were balanced . The allelic frequencies of both TNF-α polymorphisms between study and control groups were statistically insignificant  compared with GG genotypes (−238 G>A). Similar findings were observed in patients with the GSTT1 null genotype   and 5.63 (p < 0.001), respectively  . The effectively . Similar −238G>A .p = 0.002) and 5.0 (p < 0.001) times increased risk of death, respectively (p < 0.001) and without auto-HSCT   of disease relapse or progression  . The mul= 0.001) . Patient= 0.001) . In contgression .The analysis of response rate in MM showed that patients at stage III or without auto-HSCT had an increased chance of progressive disease (PD) . We did p = 0.009) and 61.72 vs. 66.52 years (p = 0.035), respectively (p = 0.038). In MM patients with −238 GG, we observed higher concentration of β2-microglobulin in comparison to −238 GA+AA genotype carriers . In the case of other analyzes, we did not confirm any significant correlations among studied polymorphisms regarding such parameters as free light chain ratio, baseline hemoglobin concentration, levels of albumin, calcium, creatinine, C-reactive protein and estimated glomerular filtration rate (GSTT1 null (p = 0.19), GSTM1 null (p = 0.20), −308 GA+AA (p = 0.80), and −238 GA+AA (p = 1.0).Furthermore, we analyzed potential relationships between clinical and laboratory results and selected genotypes. The −308/−238 GA+AA genotypes were associated with development of MM at an earlier age in comparison to −308/−238 GG−61.14 vs. 66.86 years . In the log rank test was observed the tendency for shorter OS and PFS in patients with genotype , 4. Furtgenotype .in vitro studies, bortezomib increased the number of apoptotic and necrotic cells in all studied genotypes. The only statistically significant differences were observed in the number of apoptotic cells at concentration of 12 nM between the genotypes as follows—TNF-α −308 GA+AA vs. GSTT1 present , p = 0.026; TNF-α −308 GA+AA vs. GSTM1 null , p = 0.020; TNF-α −308 GA+AA vs. GSTM1 present , p = 0.046; TNF-α −308 GA+AA vs. TNF-α −308 GG , p = 0.001; TNF-α −308 GA+AA vs. TNF-α −238 GG , p = 0.004; TNF-α −308 GA+AA vs. TNF-α −238 GA+AA , p = 0.017 and TNF-α −238 GG vs. TNF-α −238 GA+AA , p = 0.032  or as a result of linkage disequilibrium of G alleles with other functional SNPs involved in the development of MM   . Deletion of GSTs alleles resulted in lack of enzyme activity. This may increase the level of carcinogens and mutation rates in genes regulating cell cycle progression, which result in higher cell proliferation rates, including proliferation of plasma cells.In the case of uding MM , 46, 47.a (ANLL) . Yuille ia (CLL) . They foenotypes . In our GSTT1 and GSTM1 polymorphisms in the pathogenesis of B-cell malignancies. Few studies are focused on GSTT1 and GSTM1 polymorphisms in MM (GSTM1 present genotype with shorter OS (at the level of tendency) and PFS of MM patients. Multivariate Cox analysis showed that patients with GSTM1 present genotype had lower chances for longer PFS, which is consistent with the research of Lopes-Aguiar et al. , according with the ethical standards established by Helsinki Declaration.in vitro study with bortezomib. MH and AF were involved in planning and supervised the work. SZ, AS-S, and WS processed the experimental data and performed the analysis. SZ, SP-M, and WS designed the figures. SZ, AS-S, and WS wrote the manuscript with support from MH and AF. All authors discussed the results and commented on the manuscript.SZ, SP-M, MW-L, SC, and IK-P carried out the experiment. SZ, SP-M, MW-L, and IK-P performed the molecular analysis of GSTs and TNF-alpha polymorphisms. SC performed cytogenetic analysis. SZ and SP-M performed 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.


Human and animal studies suggest an intriguing relationship between the immune system and the development of depression. Some peripherally produced cytokines, such as TNF-α, can cross the blood brain barrier and result in activation of brain microglia which produces additional TNF-α and fosters a cascade of events including decreases in markers of synaptic plasticity and increases in neurodegenerative events. This is exemplified by preclinical studies, which show that peripheral administration of pro-inflammatory cytokines can elicit depression-like behavior. Importantly, this depression-like behavior can be ameliorated by anti-cytokine therapies. Work in our laboratory suggests that TNF-α is particularly important for the development of a depressive phenotype and that TNF-α antagonists might have promise as novel antidepressant drugs. Future research should examine rates of inflammation at baseline in depressed patients and whether anti-inflammatory agents could be included as part of the treatment regimen for depressive disorders. Depression remains the most common psychiatric disorder, affecting approximately 350 million people worldwide. Accordingly, depression now ranks as the top cause of global disability in terms of years lost due to disability. The defining characteristics of depression include anhedonia, a loss of interest in pleasurable activities, and lowered mood . DepressThe notion that inflammation could be a contributing factor in the pathogenesis of depression is not a new one. Work in the 1980s’s first revealed that some patients with heightened immune activity  displayed the hallmark features of depression  . This inStress is also known to transiently elevate the expression of pro-inflammatory cytokines. Unsurprisingly, pro-inflammatory cytokines are efficient activators of the hypothalamic-pituitary-adrenal (HPA) axis , which oper se produced by a spinal cord injury can lead to the development of depression-like behaviors. Cytokines are increasingly being recognized as having a contributive role in the development of depressive symptoms. Treatment with proinflammatory cytokines, including IL-1, IL-6, or TNF-α, or lipopolysaccharide (LPS), induces sickness behavior and corresponding depression-like behavior on the forced swim test . Yang etAlthough the release of pro-inflammatory cytokines can contribute to the development of depression-like behavior, TNF-α in particular is receiving considerable attention due to its prominent roles in promoting inflammation and its dampening effects on synaptic plasticity . It is iTNF-α is a protein that is initially released as a soluble cytokine (sTNF-α) after being enzymatically cleaved by its cell surface bound precursor (tmTNF-α) by TNF-α converting enzyme (TACE)  and is tOne of the main roles of TNF-α is in maintaining inflammation during times of proinflammatory conditions. During proinflammatory events, TNF-α production is induced by other cytokines  and microglia. Once released, TNF-α stimulates the production of other proinflammatory cytokines, including IL-1 and 6, and it increases the production of reactive oxygen intermediates, including nitric oxide . It is ePreclinical studies corroborate the role of TNF-α in depression-like behavior. Peripheral administration of TNF-α can produce anhedonic behavior in rodents . LikewisGiven that cytokine treatment produces depression-like behavior and reducing cytokine levels alleviates this, the question that arises is whether or not antidepressants can influence inflammation. Interestingly, SSRIs and tricyclic antidepressants are known to reduce levels of TNF-α and other proinflammatory cytokines and increase anti-inflammatory cytokines, including IL-10 . Two potConsiderable attention has been dedicated to the antidepressant potential of ketamine. Ketamine rapidly reverses depression-like behavior in both clinical and preclinical subjects, in a timeframe of hours . An impoIn terms of clinical studies, a large body of evidence supports the role of TNF-α in depression. Endotoxin administration in control subjects produces an increase in TNF-α in addition to depressed mood and cognitive impairment . MicroarSome interesting extensions of these clinical studies have been observed in patients with treatment-resistant depression. per se can cross the blood brain barrier by a receptor mediated mechanism  causes a functional decrease in peripheral TNF-α with only an indirect effect on central TNF-α expression, as drugs like etanercept cannot cross the blood brain barrier . Therefoechanism , and wheechanism . Peripheechanism . As statOne should therefore expect that etanercept injections would prevent some of the central effects of protracted release of central TNF-α, such as its effects on hippocampal activity and neurogenesis see . In lineAnother intriguing hypothesis about the mechanism by which etanercept might enhance hippocampal neurogenesis comes from a separate set of studies we conducted that focus on the extracellular matrix protein reelin. Reelin has been extensively studied for its role in guiding cell migration during development, but in the adult brain it is involved in the promotion of synaptic plasticity. Reelin binds to two receptors, the very-low-density lipoprotein receptor (VLDR) and apolipoprotein receptor 2 (ApoER2). Activation of these receptors by reelin ultimately excites downstream targets including mTOR and P13K . ImportaIt is therefore worth considering how etanercept might interact with the reelin signaling system in the dentate gyrus. Very little direct research has been done on this topic to date, but there could be a link through neuronal nitric oxide. TNF-α promotes the expression of nitric oxide synthase, and there are reports that some neuronal subtypes might reflect nitric oxide-mediated oxidative damage in response to increased levels of TNF-α  downregulates ApoE production . It is twhen etanercept actually interacts with reelin. In the per se. It will be important to conduct further studies on this issue, with etanercept given in different animal models of depression at different time periods during and after periods of stress, so that a more complete understanding of the beneficial effects of etanercept can emerge.Another question that remains to be answered is Numerous reports point toward the effectiveness of anti-TNFα drugs for some depression patients. Use of TNF-α antagonists as antidepressants may be particularly important for subpopulations of patients with treatment resistant depression that show high levels of expression of proinflammatory cytokines.All authors contributed to the ideas presented in this review and contributed to editing the manuscript. KB wrote the first draft and developed the figure. LK and HC finalized the manuscript.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.


Consequently, targeting of TNFR2 by either antagonists or agonists may represent a novel strategy in the treatment of cancer and autoimmune diseases, by downregulating or upregulating suppressor cell activity. The advance in the understanding of complex structure of TNFR2 and its binding with TNF at molecular levels offers opportunity for structure-guided drug discovery. This article reviews the current evidences regarding the decisive role of TNFR2 in immunosuppressive function of Tregs and MDSCs, and the current effort to develop novel TNFR2-targeting therapeutic agents in the treatment of cancer, autoimmune diseases, and graft-versus-host disease. To shed light on the potential TNFR2-targeting small molecules, we for the first time performed virtual screening of 400,000 natural compounds against the two TNF-binding sites, regions 3 and 4, of TNFR2. Our result showed that the top hits at region 4 had slightly higher docking energies than those at region 3. Nevertheless, free energy calculation from the TNF–TNFR2 molecular dynamics simulation revealed that the binding strength of TNF in region 3 is only one-tenth of that in region 4. This suggests that region 3 is a potentially more viable binding site to be targeted by small molecules than region 4. Therefore, the effectiveness in targeting region 3 of TNFR2 deserves further investigation.There is now compelling evidence that TNF receptor type II (TNFR2) is predominantly expressed on CD4 TNFR1 is ubiquitously expressed on almost all cell types, and TNF–TNFR1 signaling has various functions such as activation of nuclear factor kappa B (NF-κB) and induction of cell death, which depends on its cellular environment , endothe (Tregs) . There i (Tregs) –14. TNFR (Tregs) , while T (Tregs) , by uprein silico screening), the search for potential drug leads to specific target receptor by computer programs, is of central importance in early-stage drug discovery   , 13. In llo-HCT) . In RA pllo-HCT) . Taken tammation –29. STARammation . These f+ Tregs is increased in the peripheral blood or in the tumor-associated ascites (+ effector T cells (Teffs) cells and CD8+ cytotoxic T lymphocytes (CTLs) in metastatic melanoma patients, and the expression of TNFR2 is associated with CD8+ CTLs exhaustion  and some tumor cells. It has been shown that mTNF, by interacting with TNFR2, activates MDSCs and enhances their suppressive activities, including upregulating arginase-1 and inducible NO synthase transcription, promoting secretion of NO, reactive oxygen species, interleukin (IL)-10, and transforming growth factor beta , 36. TNFof tumor . The sigof tumor , Hodgkinof tumor , myelomaof tumor , renal cof tumor , and ovaof tumor . Therefoof tumor . Our groof tumor . This cor  as SPD304 (IC50 = ~3 µM), the most potent TNF binder known at that time (Kd = 0.11 µM). It was again found by virtual screening from a library of 90,000 compounds (O-β-d-monoglucoside (Kd = 0.376 µM) identified by Cao et al. in high-throughput screening experiments  docking. Candidates with high XP scores and Glide energies were analyzed for their residual binding patterns. Selected compounds were further subjected to QM-polarized ligand docking (QPLD) available in the Glide module. We also docked a model tripeptide RRA which contains the same three residues of TNF that interact with TNFR2 at region 3 when bound. This is to provide a baseline energy value for compound selection.As a first attempt to identify potential TNFR2 binders, we performed virtual screening of 400,000 natural compounds from the Traditional Chinese Medicine Database . This cosoftware , the GliRRA . Compounds targeting region 3, the negatively charged pocket, contain amine groups that can interact with the key residues, such as Asp54, Glu57, and Glu70. However, top hits in region 4 have only slightly higher QPLD scores than top hits at region 3. Since TNF binds much stronger to region 4 than to region 3, region 4 compounds are very unlikely to be able to compete with TNF.As listed in Table in vitro and in vivo experiments.To assess the stability of top virtual hits in region 3, compounds 1 and 2 were subjected to 15-ns MD simulations. Binding poses of these compounds are depicted in Figure Targeting TNF–TNFR2 with small molecules is a challenging task. Here, we demonstrated the use of virtual screening, MD, and MM/PBSA methods to identify promising hits from a screening of 400,000 natural compounds to target the major TNF–TNFR2 binding regions. Combined MD and MM/PBSA method can provide detail picture of the protein–protein and protein–ligand interactions which helps to identify and compare key receptor residues that contribute to the binding. Our analysis indicates that region 3 is potentially more druggable by small molecules due to its relatively much weaker but essential binding to TNF than region 4 . Also, TFS, JH, XC, and SS designed the overall project. XC supervised JH, and SS supervised FS and PB. FS performed virtual screening, FS and PB performed simulations and analyzed data. FS, JH, XC, and SS wrote and edited the manuscript. XC and SS provided funding for the project. FS and JH contributed equally.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.


The concept of kinship permeates many domains of fundamental and applied biology ranging from social evolution to conservation science to quantitative and human genetics. Until recently, pedigrees were the gold standard to infer kinship, but the advent of next‐generation sequencing and the availability of dense genetic markers in many species make it a good time to (re)evaluate the usefulness of genetic markers in this context. Using three published data sets where both pedigrees and markers are available, we evaluate two common and a new genetic estimator of kinship. We show discrepancies between pedigree values and marker estimates of kinship and explore via simulations the possible reasons for these. We find these discrepancies are attributable to two main sources: pedigree errors and heterogeneity in the origin of founders. We also show that our new marker‐based kinship estimator has very good statistical properties and behaviour and is particularly well suited for situations where the source population is of small size, as will often be the case in conservation biology, and where high levels of kinship are expected, as is typical in social evolution studies. Ibdidentity by descentMAFminor allele frequencySNPsingle nucleotide polymorphism1Kinship, also known as coancestry or half‐relatedness, is important to many fields of biology  is measured to establish the extinction risk of threatened populations   is defined relative to a reference population, where different alleles are considered to be not ibd Wang, . EstimatWe recognize the many advantages of likelihood‐based (including Bayesian) methods, but simplicity and computational issues lead us to concentrate on the method of moments for estimation. A feature of our approach  have been important historically and foundational in many of the aforementioned fields; however, they have significant limitations. First, for many study populations pedigree data are unavailable and would be impossible to acquire. Second, pedigrees are susceptible to human error such as unnoticed extra‐pair copulations and misidentified paternity  share a common ancestor somewhere along the tree of life . The number of single nucleotide variants required to provide satisfactory estimates of the kinships among these individuals remains unknown. The aim of this study was threefold: (a) to examine the properties of kinship estimators for real pedigrees of varying size, structure and completeness; (b) to explore, using simulations, the causes underlying different estimator properties; and (c) to evaluate the usefulness of pedigree‐based estimates of kinship.22.12.1.1j, the inbreeding coefficient Fj is the probability that its two alleles at a given locus are ibd. The kinship, or coancestry, coefficient θjj′ for individuals j and j′ is defined here as the average of the four ibd probabilities for one allele from each individual. It follows that the kinship of individual j with itself is (1 + Fj)/2. Generally, however, we reserve the term kinship for distinct individuals. θS denotes the average of the kinships over pairs of individuals for (samples from) a population.For individual J is ancestral to individuals j and j′, and if there are n individuals in the pedigree path joining j to j′ through J, including j and j′, then θjj′ = ∑(0.5)n(1 + FJ), where FJ is the pedigree inbreeding coefficient of J and the sum is over all ancestors J and all paths joining j to j′ through J  reference for predicted kinship values from pedigrees is the set of founders, who are assumed to have a kinship of 0 with other founders.If individual  Wright, . The ped2.1.2θjj′ for individuals j and j′ in a sample of individuals relative to the average kinship θS of all pairs of individuals in the sample. Making estimates “relative to” meant that the reference allele frequencies did not need to be estimated. It also meant that kinship estimates for pairs of individuals who share less alleles than the population average are negative. Instead of estimating the total kinship coefficient θjj′, we focus on the within‐population parameter βjj′ = (θjj′ − θS)/(1 − θS). This comes from the following relationship for individual‐level kinship coefficients: (1 − θjj′) = (1 − βjj′)(1 − θS), analogous to the well‐known relation for population‐level inbreeding coefficients  = (1 − FIS)(1 − FST). This leads to a discrepancy between marker‐based estimates (which can be negative) and pedigree‐based expectations (which range from 0 to 1).We (Xjl′−1)]/2}, where Xjl is the dosage of a particular allele, for example, reference allele, at marker l for individual j. For a sample of n individuals, MS is the average matching for all pairs of distinct individuals:In Weir and Goudet , we wrotre Wang, . rjj′β c Goudet,  and is drjj′β is the target parameter (θjj′ − θS)/(1 − θS), and we showed Weir and Goudet /2 − θS]/(1 − θS), and an estimator of Fj relative to the average kinship is (2rjβ−1). We note that Mjj′ is the central quantity in the third estimator of VanRaden  for the reference allele at a locus can be used in place of the reference population allele frequencies. Details for two such estimators that differ in how they combine information over loci now follow /2 when j = j′ and θjj′ when j ≠ j′. There is no simple translation from these estimates to those we propose in Equation The weighted estimator in Equation (Xjl−2p~l)/[2p~l(1−p~l)] as a standardized genotype measure on the basis that the expected value of Xjl is twice the allele frequency (2pl) in the reference population. However, the variance of Xjl is 2pl(1−pl)(1+Fj) rather than 2pl(1−pl).It is common to refer to j, j′ relative to that for all pairs of individuals in a sample. Another perspective is implicit in the use of the estimators rjj′w and rjj′u. There the kinship for the target pair of individuals measures the additional probability of ibd of each individual with every other sample member over that between all sample pairs. This takes account of the different pedigrees of each of the target pairs: If ψj is the average kinship of j with every other individual in the sample, ψj = ∑ j″≠jθjj″/(n − 1), then the target parameter with this perspective is:We have focused on the kinship for individuals rjj′w is a good estimator for this new parameter and rjj′u less so.We showed in simulations /(1 − θS), but clearly neither is the same as the pedigree‐based value of θjj′.For large sample sizes, we note that the ratio of the expected values of the numerator and denominator of Equation  will be rjj′u:When information over loci is combined as an unweighted average, we write a common kinship estimator as D of locus variances comes between the dosage matrices X and X′ . Assuming there is some ‘correct’ value of kinship, this noise could be due to inaccuracies in the pedigree‐based expectations, in the marker‐based estimates, or in both. Note that in the tit and sheep data sets, some genotypes are missing, while they have been imputed in the pig data set. We described in supplementary materials how to handle missing data when estimating k SNPs for founders and unknown parents using the program ms  from a Poisson distribution with parameter λ = 2. For each offspring, the two parents were drawn at random and without replacement. Contrastingly, under monogamy, two parents were drawn at random and had their number of offspring drawn from a Poisson distribution with λ = 2. Thus, more individuals will be full‐sibs in a monogamous population than in a random‐mating population.Under random mating, the total number of offspring for a given generation was obtained by drawing 2.3.2For each mating system, we simulated pedigrees with between 20 and 1,000 founders. We varied founder number incrementally: in steps of 10 between 20 and 100, in steps of 50 between 100 and 250 and in steps of 250 between 250 and 1,000. For each mating system and number of founders, we simulated 10 pedigrees.2.4rβ, rw and ru) and those expected from pedigrees (rp). We show the correlations as a function of the standard deviation in pedigree kinship SD(rp) among all individuals in the pedigree: If there were no variation in pedigree kinship, as would be the case in an infinite random‐mating population, pedigree‐based kinship values should not correlate with marker‐based estimates. As the standard deviation in pedigree kinship increases , correlation between marker‐based estimates and pedigree values should increase. We therefore expect an increase in the correlation between pedigree values and marker‐based estimates as the standard deviation in pedigree kinship increases . We show in Supporting Information Figure rp) is a decreasing function of the number of individuals in the pedigree.For each of the pedigrees and associated genetic data sets, we report the correlation between the kinships estimated with markers  using ms. These two populations were at equilibrium between mutation, migration and drift and exchanged one migrant per generation, and thus, FST between these two populations is 0.2. There is a large discrepancy between rp and rβ, rw and ru in this situation (see below).When calculating the pedigree kinship A possible solution to this problem, if founder genotypes are available}, is to plug in the marker‐based kinship estimates of the founders, rather than assuming they are unrelated  and almost perfectly for the pedigree with 750 founders (bottom left). The downward discrepancy for rβ is due to the constraint that the average of all rβ is 0; this can easily be corrected (by imposing the same constraint on rp and replacing it with (rp−rp¯)/(1−rp¯), where rp¯ is the mean kinship of all individuals in the pedigree), as shown in Weir and Goudet , ru shows extreme scatter, and a similar effect, although less pronounced, is seen for half‐sibs (rp = 0.125). For the large pedigree, the relation between ru and rp for low kinship values is very tight (bottom middle panel), more so than for rβ.rw, another estimate of γ, and rp , the relation is much tighter; the regression slope is close to 1 and the scatter is less than for rβ. The extreme scatter for rp = 0.25 seen in ru for both the small and large pedigrees (middle column) is also seen in rw for small pedigrees (top right) but disappears when the pedigree is large (bottom right).The relation between rβ, rw and ru with rp  as a function of variation in kinship. Pairs of individuals from pedigrees with few founders will have more chance of being related than pairs from larger pedigrees. The pedigrees with few founders will thus have more variation in kinship and will be located to the right‐hand side of the graph, while pedigrees with many founders will be to the left. For a given number of founders, monogamous pedigrees (filled circles) will show more variation in kinship than random‐mating pedigrees (+ symbols) and will thus be located to their right.Figure rβ and rp shows a very different pattern from that of the other two marker‐based estimates: It increases as the variance in rp increases, while the correlations of rw and ru decrease as the standard deviation in rp increases above ≈ 0.02. The relation for the correlation between marker‐ and pedigree‐based kinship is very tight for rβ, with all the points falling on the same trajectory. This correlation is around 0.6 when the standard deviation in pedigree kinship is ≈ 0.009, and asymptotes at one as the standard deviation of pedigree kinship increases.The correlation between rw and ru is almost the reverse. Correlation increases at first, when there is very little variance in pedigree kinship; reaches a maximum at around 0.93 for rw and 0.96 for ru, when the standard deviation in pedigree kinship is between 0.02 and 0.03; and decreases linearly thereafter, although with greater scatter, as the standard deviation in rp increases above 0.03.The pattern for rβ is a better estimator of pedigree kinship, while when the standard deviation in pedigree kinship is less than 0.03, ru is preferable (ru outperforms rw over the whole range). However, this is partly misleading as we saw in Figure rβ is actually very close to rp over its whole range; ru, while giving precise estimation for low values of rp, is not very good in estimating the kinships of full‐ and half‐sibs.From this, it would seem that when the standard deviation in pedigree kinship is larger than ≈ 0.02 − 0.04, rw and ru is puzzling. The pattern observed with rβ makes intuitive sense: If there is no variation in pedigree kinship, then it cannot be correlated with anything (correlation with a constant is 0 by definition). The larger the variation in pedigree kinship, the more there is to explain; thus, marker‐based estimates of kinship should be more correlated with pedigree values. We will return to this point later.The pattern observed for the correlation between marker and pedigree kinship for 3.2k. This is a fairly large number, though by no means exceptional nowadays. In Figure k, 10k, 5k, 1k, 500 and finally 100.In Figure k, 10k and 5k SNPs) shows slightly noisier versions of Figure rβ has a higher correlation with rp than rw or ru, while for low variation, rw and ru are more correlated with rp. Note that rw in particular reaches a plateau and starts decreasing when the variation in rp gets very low (for 10k and 5k SNPs).As the number of markers decreases, we see both quantitative and qualitative changes. The top row of Figure k, 500 and 100 SNPs) looks different. First, the correlations between marker‐based estimates and pedigree‐based predictions are much lower, below 0.8 for 1k, 0.6 for 500 and 0.4 for 100 SNPs. Second, for the three marker‐based kinship estimators, correlation with pedigree‐based kinship increases as variation in pedigree kinship increases. In all three panels of the bottom row, the correlation between rβ and rp is less than that for rw, which itself is less than that for ru.The bottom row of Figure 3.3rβ. We see in Figure rw and ru show a similar pattern; data not shown). In particular, for pedigree kinships of 0, 0.125 and 0.25, we see a large range of marker‐based estimates, larger than when the founders come from a single population , rβ is the most correlated with rp.We can use the same principle when all founders come from one population. The scatter seen in Figure 3.43.4.1The great tit pedigree is shallow, covering at most three generations, pedigree‐based predictions of kinship have few categories, and some of these have very few observations:rp = 0.125) who are identified as full‐sibs with marker‐based estimates of kinship . A similar pattern is seen for first cousins and half‐sibs. The three marker‐based estimators of kinship for the unrelated individuals show a unimodal distribution with all modes at r = 0, but long tails extending to 0.4. This is probably due to founders being related. For instance, individuals 17 and 557, both males and founders, have a pedigree‐based kinship assumed to be 0, but their estimated r17,557β=0.32. Descendants of these individuals will have their pedigree‐based kinships underestimated.Figure 3.4.2rβ, ru and rw from left to right. The correlations are 0.65, 0.73 and 0.71, respectively. While in all three panels we see a tendency for marker‐based estimates to increase with the pedigree‐based predictions, there is much scatter. In particular, for all three marker‐based estimators, some pairs of individuals assumed to be unrelated with pedigree‐based predictions have fairly high marker‐based estimates, and some individuals with pedigree‐based predictions of 0.25 (full‐sibs or parent–offspring) have marker‐based estimates around 0.Next, we look at the sheep data set. Figure rβ and rw, while ru shows similar scatter to previously, particularly for pedigree kinship class 0.25.The second row of Figure rβ and rw, most of the points fall close to the one‐to‐one line, and points outside this envelope are easy to identify , providing the opportunity to correct the pedigree. It would be much more difficult to use ru for such a correction.The last row of Figure 3.4.3rβ, ru and rw, respectively, for a standard deviation of pedigree‐based kinship estimates of 0.02. There is therefore little separating the three marker‐based estimators of kinship in terms of correlation, and rβ shows the least scatter and bias of the three estimators (compare the top left panel (b) of Figure This data set is the most complete: All individuals (bar founders) have both parents identified. Close to 55% of the 6,473 individuals in the pedigree have been genotyped. The first row of Figure rβ) from simulated genetic data  larger than 0.01 reduces the correlation for rβ and rw and marginally increases it (0.85 to 0.87) for ru). The last row of Figure rβ: Among the simulated marker‐based estimates of kinship close to 0, we observed two high‐density spots of estimates from real data (the two dark spots), an indication that the founders may not come from a homogeneous stock.The marker‐based estimates of kinship .4rβ closely tracks rp over the whole range of kinship values  and performs better than ru and rw for small pedigrees and pedigrees with many related individuals. ru is a poor estimator of rp for pairs of individuals with high kinship. We confirm that with a sufficient number of markers, marker‐based estimation of kinship better reflects individual relationship than pedigree‐based prediction.For three data sets where both pedigree and genetic data are available, the match between pedigree‐based predictions and marker‐based estimates of kinship is poor. Using simulated pedigrees and genetic data, we identify two likely causes for this mismatch: errors in the assignment of parentage when constructing the pedigree and heterogeneity in the origin of the founders. We show that the new estimator Heterogeneity in the origin of founders seems quite clear in the pig data set  by ru and rw could be obtained by using the founders’ allele frequencies (if these were available) instead of the sample allele frequencies , MS, is subtracted from each entry, ensuring that the overall mean kinship is 0. We then divide this difference by 1 − MS, constraining the upper bound of rβ to 1. In order to obtain the third estimate of VanRaden . In ecological and evolutionary biology studies, pedigrees are never as complete and accurate as in animal breeding, and inclusion of pedigree information is likely to add more noise than information.4.2There is not a general answer to this question. Below we list context‐dependent recommendations:rβ.Where founder populations are small , the recommended marker‐based estimate of kinship is rβ is the recommended estimator of kinship, as it performs well across the whole range of kinship values. At the level of first cousins and above, ru performs poorly.In sample populations with high kinship (such as those used to investigate cooperation between kin), ru should be used to estimate the genetic relationship matrix, since it estimates the kinship of unrelated individuals most accurately. It might be necessary in this case to filter the SNPs for MAF larger than 1%, as low‐frequency SNPs tends to generate a long tail of high kinship values for unrelated individuals  poorly. rβ is accurate over the whole range of possible kinship values.The recommended marker‐based estimate of kinship to use depends on the amount of variation in kinship in the population.MAF filtering is not a good idea; it diminishes the correlation with pedigree kinship in most cases.ru is a good choice. For choosing breeders in conservation genetics or for comparing levels of relatedness among pairs of individuals in social species, rβ is the estimator of choice.The most suitable marker‐based estimator also depends on why kinship is being estimated. If the purpose is estimation of heritability, and the proportion of related individuals is small, J.G. and B.S.W. conceived the study. T.K. carried out preliminary analyses as part of his master; J.G. wrote the paper, with contributions from all authors.https://doi.org/10.5061/dryad.ds8fk04.Data available from the Dryad Digital Repository:  Click here for additional data file.