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A genome-wide association study was performed to identify genetic factors involved in susceptibility to psoriasis (PS) and psoriatic arthritis (PSA), inflammatory diseases of the skin and joints in humans. 223 PS cases (including 91 with PSA) were genotyped with 311,398 single nucleotide polymorphisms (SNPs), and results were compared with those from 519 Northern European controls. Replications were performed with an independent cohort of 577 PS cases and 737 controls from the U.S., and 576 PSA patients and 480 controls from the U.K.. Strongest associations were with the class I region of the major histocompatibility complex (MHC). The most highly associated SNP was rs10484554, which lies 34.7 kb upstream from HLA-C (P = 7.8x10(-11), GWA scan; P = 1.8x10(-30), replication; P = 1.8x10(-39), combined; U.K. PSA: P = 6.9x10(-11)). However, rs2395029 encoding the G2V polymorphism within the class I gene HCP5 (combined P = 2.13x10(-26) in U.S. cases) yielded the highest ORs with both PS and PSA (4.1 and 3.2 respectively). This variant is associated with low viral set point following HIV infection and its effect is independent of rs10484554. We replicated the previously reported association with interleukin 23 receptor and interleukin 12B (IL12B) polymorphisms in PS and PSA cohorts (IL23R: rs11209026, U.S. PS, P = 1.4x10(-4); U.K. PSA: P = 8.0x10(-4); IL12B:rs6887695, U.S. PS, P = 5x10(-5) and U.K. PSA, P = 1.3x10(-3)) and detected an independent association in the IL23R region with a SNP 4 kb upstream from IL12RB2 (P = 0.001). Novel associations replicated in the U.S. PS cohort included the region harboring lipoma HMGIC fusion partner (LHFP) and conserved oligomeric golgi complex component 6 (COG6) genes on chromosome 13q13 (combined P = 2x10(-6) for rs7993214; OR = 0.71), the late cornified envelope gene cluster (LCE) from the Epidermal Differentiation Complex (PSORS4) (combined P = 6.2x10(-5) for rs6701216; OR 1.45) and a region of LD at 15q21 (combined P = 2.9x10(-5) for rs3803369; OR = 1.43). This region is of interest because it harbors ubiquitin-specific protease-8 whose processed pseudogene lies upstream from HLA-C. This region of 15q21 also harbors the gene for SPPL2A (signal peptide peptidase like 2a) which activates tumor necrosis factor alpha by cleavage, triggering the expression of IL12 in human dendritic cells. We also identified a novel PSA (and potentially PS) locus on chromosome 4q27. This region harbors the interleukin 2 (IL2) and interleukin 21 (IL21) genes and was recently shown to be associated with four autoimmune diseases (Celiac disease, Type 1 diabetes, Grave's disease and Rheumatoid Arthritis).

Human sleeping sickness in east Africa is caused by the parasite Trypanosoma brucei rhodesiense . The basis of this pathology is the resistance of these parasites to lysis by normal human serum (NHS) 1 , 2 . Resistance to NHS is conferred by a gene that encodes a truncated form of the variant surface glycoprotein termed serum resistance associated protein (SRA) 3 , 4 . We show that SRA is a lysosomal protein, and that the amino-terminal α-helix of SRA is responsible for resistance to NHS. This domain interacts strongly with a carboxy-terminal α-helix of the human-specific serum protein apolipoprotein L-I (apoL-I). Depleting NHS of apoL-I, by incubation with SRA or anti-apoL-I, led to the complete loss of trypanolytic activity. Addition of native or recombinant apoL-I either to apoL-I-depleted NHS or to fetal calf serum induced lysis of NHS-sensitive, but not NHS-resistant, trypanosomes. Confocal microscopy demonstrated that apoL-I is taken up through the endocytic pathway into the lysosome. We propose that apoL-I is the trypanosome lytic factor of NHS, and that SRA confers resistance to lysis by interaction with apoL-I in the lysosome.

Truncating mutations in the giant sarcomeric protein Titin result in dilated cardiomyopathy and skeletal myopathy. The most severely affected dilated cardiomyopathy patients harbor Titin truncations in the C-terminal two-thirds of the protein, suggesting that mutation position might influence disease mechanism. Using CRISPR/Cas9 technology, we generated six zebrafish lines with Titin truncations in the N-terminal and C-terminal regions. Although all exons were constitutive, C-terminal mutations caused severe myopathy whereas N-terminal mutations demonstrated mild phenotypes. Surprisingly, neither mutation type acted as a dominant negative. Instead, we found a conserved internal promoter at the precise position where divergence in disease severity occurs, with the resulting protein product partially rescuing N-terminal truncations. In addition to its clinical implications, our work may shed light on a long-standing mystery regarding the architecture of the sarcomere. The heart is able to beat partly because of a large protein called Titin that helps to give heart muscle its elasticity. Mutations that shorten the gene that encodes Titin can cause part of the heart to become enlarged and weakened, a condition called dilated cardiomyopathy. Some people with shortened copies of this protein have a mild form of cardiomyopathy and are able to lead relatively normal lives. Others develop more severe symptoms that prevent the heart from pumping blood effectively and may even cause the individual to need a heart transplant. Genetic studies have revealed that mutations that shorten the Titin protein by disrupting the portion of the gene corresponding to the latter two-thirds of the protein (which encodes the so-called “C-terminal” end of the protein) cause more severe symptoms than mutations that occur near the start of the gene. But it is not clear why the location of the mutation matters. To investigate this problem, Zou et al. used a gene-editing tool called CRISPR to create genetically engineered zebrafish. These fish had mutations at one of six different points in the gene that encodes the zebrafish version of Titin. Just as with humans, mutations near the C-terminal end of the gene caused more severe muscle problems in the fish. Specifically, Zou et al. found that the worst disease was associated with mutations that occurred at or after a “promoter” region within the gene and near this C-terminal end. Normally, the promoter produces an independent smaller form of the Titin protein, which helps to reduce the severity of muscle problems in zebrafish that have mutations near the start of the gene. However, mutations near the C-terminal end of the gene also damage this smaller form, preventing this failsafe from working, and so lead to more severe symptoms. Zou et al. also found this promoter to be active in both mouse and human hearts. Future work will focus on learning how this smaller form of Titin works to help muscle develop and withstand stress and determine whether increasing its production can overcome the more severe forms of disease.

With the legalization of cannabis in New Jersey on April 21, 2022, including the licensing of cannabis dispensaries, concerns have arisen about potential adverse events related to cannabis use. Here, we explore temporal trends and risk factors for cannabis‐related harm in both adult and pediatric cannabis‐related visits at a tertiary care academic institution. We performed a retrospective chart review and temporal trend analysis via the electronic health record from May 1, 2019 to October 31, 2022, covering 2 years before, and 6 months after, cannabis legalization in New Jersey. The pediatric charts identified were analyzed for root causes of adverse events, and changes in the frequency of specific unsafe practices since cannabis legalization were tracked. We found that adult cannabis ED‐related visits significantly increased during the COVID‐19 pandemic and remained higher than pre‐pandemic levels for the remainder of the study periods, without a significant change upon legalization. Pediatric rates of cannabis‐related ED visits did not vary significantly during the study period. The vast majority of visits for children aged 0–12 years were related to accidental cannabis exposures—often a household member's edibles—whereas most visits for older children stemmed from intentional cannabis use. This project highlights the unintended consequences of wider cannabis access in New Jersey. Notably, cannabis use increased even before its legalization, presumably in response to the COVID‐19 pandemic and its attendant mental health effects. Rates of cannabis use disorder and its highlight of other concurrent psychiatric disorders are important topics for both clinicians and lawmakers to consider.

This is an issue edsumm for 24. Identification of the Palaeocene/Eocene thermal maximum in a marine sedimentary sequence shows that sea surface temperatures near the North Pole increased from roughly 18 degrees Celsius to over 23 degrees Celsius — such warm values imply the absence of ice and thus exclude the influence of ice-albedo feedbacks on this Arctic warming. Hypertriglyceridemia is a hallmark of many disorders, including metabolic syndrome, diabetes, atherosclerosis and obesity 1 , 2 , 3 . A well-known cause is the deficiency of lipoprotein lipase (LPL), a key enzyme in plasma triglyceride hydrolysis 4 , 5 , 6 . Mice carrying the combined lipase deficiency ( cld ) mutation show severe hypertriglyceridemia owing to a decrease in the activity of LPL and a related enzyme, hepatic lipase (HL) 7 , 8 , 9 , caused by impaired maturation of nascent LPL and hepatic lipase polypeptides in the endoplasmic reticulum (ER) 10 . Here we identify the gene containing the cld mutation as Tmem112 and rename it Lmf1 (Lipase maturation factor 1). Lmf1 encodes a transmembrane protein with an evolutionarily conserved domain of unknown function that localizes to the ER. A human subject homozygous for a deleterious mutation in LMF1 also shows combined lipase deficiency with concomitant hypertriglyceridemia and associated disorders. Thus, through its profound effect on lipase activity, LMF1 emerges as an important candidate gene in hypertriglyceridemia 4 , 11 , 12 .

Background As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of “At Risk” CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates. Results A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p -value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Regulator of G protein signaling 9-2 (RGS9-2) is a protein that is highly enriched in the striatum, a brain region that mediates motivation, movement and reward responses. We identified a naturally occurring 5 nucleotide deletion polymorphism in the human RGS9 gene and found that the mean body mass index (BMI) of individuals with the deletion was significantly higher than those without. A splicing reporter minigene assay demonstrated that the deletion had the potential to significantly decrease the levels of correctly spliced RGS9 gene product. We measured the weights of rats after virally transduced overexpression of RGS9-2 or the structurally related RGS proteins, RGS7, or RGS11, in the nucleus accumbens (NAc) and observed a reduction in body weight after overexpression of RGS9-2 but not RGS7 or 11. Conversely, we found that the RGS9 knockout mice were heavier than their wild-type littermates and had significantly higher percentages of abdominal fat. The constituent adipocytes were found to have a mean cross-sectional area that was more than double that of corresponding cells from wild-type mice. However, food intake and locomotion were not significantly different between the two strains. These studies with humans, rats and mice implicate RGS9-2 as a factor in regulating body weight.

Background: Coronary heart disease (CHD) remains a leading cause of death and has been associated with alterations in plasma lipoprotein particles and inflammation markers. This study aimed to evaluate and compare standard and advanced lipid parameters and inflammatory biomarkers in CHD cases and matched control subjects. We hypothesized that incorporating advanced lipid and inflammatory biomarkers into risk models would improve CHD risk prediction beyond the standard lipid measures. Methods: CHD cases (n = 227, mean age 61 years, 47% female) and matched controls (n = 526) underwent fasting blood collection while off lipid-lowering medications. Automated chemistry analyses were performed to measure total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-C (LDL-C), small dense LDL-C (sdLDL-C), apolipoproteins (apos) A-I and B, lipoprotein(a) (Lp(a)), high-sensitivity C-reactive protein (hsCRP), serum amyloid-A (SAA), myeloperoxidase (MPO), and apoA-I in HDL particles (via 2-dimensional electrophoresis and immunoblotting). Univariate, multivariate, and machine learning analyses compared the CHD cases with the controls. Results: The most significant percent differences between male and female cases versus controls were for hsCRP (+78%, +200%), MPO (+109%, +106%), SAA (+84%, +33%), sdLDL-C (+48%; +43%), Lp(a) (+43%,+70%), apoA-I in very large α-1 HDL (−34%, −26%), HDL-C (−24%, −27%), and apoA-I in very small preβ-1 HDL (+17%; +16%). Total C, non-HDL-C, and direct and calculated LDL-C levels were only modestly higher in the cases. Multivariate models incorporating advanced parameters were statistically superior to a standard model (C statistic: men: 0.913 vs. 0.856; women: 0.903 versus 0.838). Machine learning identified apoA-I in preβ-1-HDL, α-2-HDL, α-1-HDL, α-3-HDL, MPO, and sdLDL-C as the top predictors of CHD. Conclusions: This study introduces a novel approach to CHD risk assessment by integrating advanced HDL particle analysis and machine learning. By assessing HDL subpopulations (α-1, α-2, preβ-1 HDL), inflammatory biomarkers (MPO, SAA), and small dense LDL, we provide a more refined stratification model. Notably, preβ-1 HDL, an independent risk factor reflecting impaired cholesterol efflux from the artery wall, is highlighted as a critical marker of CHD risk. Our approach allows for earlier identification of high-risk individuals, particularly those with subtle lipid or inflammatory abnormalities, supporting more personalized interventions. These findings demonstrate the potential of advanced lipid profiling and machine learning to enhance CHD risk prediction.

Increasing carbon dioxide (CO2) levels in the atmosphere caused by excessive greenhouse gas emissions is strongly associated to global warming and climate change. This study aims to prove the feasibility of using pectin as the backbone for amine functionalization with application as coating on zeolites for carbon dioxide capture. Characterization of the solutions using FTIR and of the adsorbents using SEM demonstrated the successful modification of pectin using NH3 and TETA as alternative amine-functionalized coating for adsorbent. It has been reported for the first time that the polysaccharide pectin can be aminated and modified for CO2 capture upon coated on substrates such as zeolites. The adsorption capacities at 5% breakthrough of the adsorbents coated with the modified pectin are 2.24 mmol/CO2 g adsorbent and 2.28 mmol/CO2 g adsorbent, when coated with NH3-modified and TETA-modified pectin, respectively. It is recommended for further study to synthesize substrates with higher surface area, and optimize the formulations of the pectin modification.

Myocardial infarction (MI) is a common complex disease with a genetic component. While several single nucleotide polymorphisms (SNPs) have been reported to be associated with risk of MI, they do not fully explain the observed genetic component of MI. We have been investigating the association between MI and SNPs that are located in genes and have the potential to affect gene function or expression. We have previously published studies that tested about 12,000 SNPs for association with risk of MI, early-onset MI, or coronary stenosis. In the current study we tested 17,576 SNPs that could affect gene function or expression. In order to use genotyping resources efficiently, we staged the testing of these SNPs in three case-control studies of MI. In the first study (762 cases, 857 controls) we tested 17,576 SNPs and found 1,949 SNPs that were associated with MI (P<0.05). We tested these 1,949 SNPs in a second study (579 cases and 1159 controls) and found that 24 SNPs were associated with MI (1-sided P<0.05) and had the same risk alleles in the first and second study. Finally, we tested these 24 SNPs in a third study (475 cases and 619 controls) and found that 5 SNPs in 4 genes (ENO1, FXN (2 SNPs), HLA-DPB2, and LPA) were associated with MI in the third study (1-sided P<0.05), and had the same risk alleles in all three studies. The false discovery rate for this group of 5 SNPs was 0.23. Thus, we have identified 5 SNPs that merit further examination for their potential association with MI. One of these SNPs (in LPA), has been previously shown to be associated with risk of cardiovascular disease in other studies.