Identifying context-specific gene profiles of social, reproductive and mate preference behavior in a fish species with female mate choice

Sensory and social inputs interact with underlying gene suites to coordinate social behavior. Here we use a naturally complex system in sexual selection studies, the swordtail, to explore how genes associated with mate preference, receptivity, and social affiliation interact in the female brain under specific social conditions. We focused on 11genes associated with mate preference in this species (neuroserpin, neuroligin-3, NMDA receptor, tPA, stathmin-2, β-1 adrenergic receptor) or with female sociosexual behaviors in other taxa (vasotocin, isotocin, brain aromatase,α-1 adrenergic receptor, tyrosinehydroxylase). We exposed females to four social conditions, including pairings of differing mate choice complexity (large males, large/ small males, small males), and a social lcontrol (two females). Female mate preference differed significantly by context. Multiple discriminant analysis (MDA) of behaviors revealed a primary axis (explaining 50.2% between-group variance) highlighting differences between groups eliciting high preference behaviors (LL,LS) vs. other contexts, and a secondary axis capturing general measures distinguishing a non-favored group (SS) from other groups. Gene expression MDA revealed a major axis (68.4% between-group variance) that distinguished amongst differential male pairings and was driven by suites of “preference and receptivity genes”; whereas a second axis, distinguishing high affiliation groups (large males, females) from low (small males), was characterized by traditional affiliative-associated genes (isotocin, vasotocin). We found context-specific correlations between behavior and gene MDA, suggesting gene suites covary with behaviors in a socially relevant context. Distinct associations between “affiliative” and “preference” axes suggest mate preference maybe mediated by distinct clusters from those of social affiliation. Our results highlight the need to incorporate natural complexity of mating systems into behavioral genomics

Identifying Context-Specific Gene Profiles of Social, Reproductive, and Mate Preference Behavior in a Fish Species with Female Mate Choice

Sensory and social inputs interact with underlying gene suites to coordinate social behavior. Here we use a naturally complex system in sexual selection studies, the swordtail, to explore how genes associated with mate preference, receptivity, and social affiliation interact in the female brain under specific social conditions. We focused on 11 genes associated with mate preference in this species (neuroserpin, neuroligin-3, NMDA receptor, tPA, stathmin-2, β-1 adrenergic receptor) or with female sociosexual behaviors in other taxa (vasotocin, isotocin, brain aromatase, α-1 adrenergic receptor, tyrosine hydroxylase). We exposed females to four social conditions, including pairings of differing mate choice complexity (large males, large/small males, small males), and a social control (two females). Female mate preference differed significantly by context. Multiple discriminant analysis (MDA) of behaviors revealed a primary axis (explaining 50.2% between-group variance) highlighting differences between groups eliciting high preference behaviors (LL, LS) vs. other contexts, and a secondary axis capturing general measures distinguishing a non-favored group (SS) from other groups. Gene expression MDA revealed a major axis (68.4% between-group variance) that distinguished amongst differential male pairings and was driven by suites of “preference and receptivity genes”; whereas a second axis, distinguishing high affiliation groups (large males, females) from low (small males), was characterized by traditional affiliative-associated genes (isotocin, vasotocin). We found context-specific correlations between behavior and gene MDA, suggesting gene suites covary with behaviors in a socially relevant context. Distinct associations between “affiliative” and “preference” axes suggest mate preference may be mediated by distinct clusters from those of social affiliation. Our results highlight the need to incorporate natural complexity of mating systems into behavioral genomics

Sexual and social stimuli elicit rapid and contrasting genomic responses

Sensory physiology has been shown to influence female mate choice, yet little is known about the mechanisms within the brain that regulate this critical behaviour. Here we examine preference behaviour of 58 female swordtails, Xiphophorus nigrensis, in four different social environments (attractive and unattractive males, females only, non-attractive males only and asocial conditions) followed by neural gene expression profiling. We used a brain-specific cDNA microarray to identify patterns of genomic response and candidate genes, followed by quantitative PCR (qPCR) examination of gene expression with variation in behaviour. Our microarray results revealed patterns of genomic response differing more between classes of social stimuli than between presence versus absence of stimuli. We identified suites of genes showing diametrically opposed patterns of expression: genes that are turned ‘on’ while females interact with attractive males are turned ‘off’ when interacting with other females, and vice versa. Our qPCR results identified significant predictive relationships between five candidate genes and specific mate choice behaviours (preference and receptivity) across females exposed to males, with no significant patterns identified in female or asocial conditions or with overall locomotor activity. The identification of stimulus- and behaviour-specific responses opens an exciting window into the molecular pathways associated with social behaviour and mechanisms that underlie sexual selection

Insulin Responsiveness in Metabolic Syndrome after Eight Weeks of Cycle Training

Introduction Insulin resistance in obesity is decreased after successful diet and exercise. Aerobic exercise training alone was evaluated as an intervention in subjects with the metabolic syndrome. Methods Eighteen nondiabetic, sedentary subjects, 11 with the metabolic syndrome, participated in 8 wk of increasing intensity stationary cycle training. Results Cycle training without weight loss did not change insulin resistance in metabolic syndrome subjects or sedentary control subjects. Maximal oxygen consumption (V˙O2max), activated muscle AMP-dependent kinase, and muscle mitochondrial marker ATP synthase all increased. Strength, lean body mass, and fat mass did not change. The activated mammalian target of rapamycin was not different after training. Training induced a shift in muscle fiber composition in both groups but in opposite directions. The proportion of type 2x fibers decreased with a concomitant increase in type 2a mixed fibers in the control subjects, but in metabolic syndrome, type 2x fiber proportion increased and type 1 fibers decreased. Muscle fiber diameters increased in all three fiber types in metabolic syndrome subjects. Muscle insulin receptor expression increased in both groups, and GLUT4 expression increased in the metabolic syndrome subjects. The excess phosphorylation of insulin receptor substrate 1 (IRS-1) at Ser337 in metabolic syndrome muscle tended to increase further after training in spite of a decrease in total IRS-1. Conclusions In the absence of weight loss, the cycle training of metabolic syndrome subjects resulted in enhanced mitochondrial biogenesis and increased the expression of insulin receptors and GLUT4 in muscle but did not decrease the insulin resistance. The failure for the insulin signal to proceed past IRS-1 tyrosine phosphorylation may be related to excess serine phosphorylation at IRS-1 Ser337, and this is not ameliorated by 8 wk of endurance exercise training

Local complement activation is associated with primary graft dysfunction after lung transplantation

BACKGROUNDThe complement system plays a key role in host defense but is activated by ischemia/reperfusion injury (IRI). Primary graft dysfunction (PGD) is a form of acute lung injury occurring predominantly due to IRI, which worsens survival after lung transplantation (LTx). Local complement activation is associated with acute lung injury, but whether it is more reflective of allograft injury compared with systemic activation remains unclear. We proposed that local complement activation would help identify those who develop PGD after LTx. We also aimed to identify which complement activation pathways are associated with PGD.METHODSWe performed a multicenter cohort study at the University of Pennsylvania and Washington University School of Medicine. Bronchoalveolar lavage (BAL) and plasma specimens were obtained from recipients within 24 hours after LTx. PGD was scored based on the consensus definition. Complement activation products and components of each arm of the complement cascade were measured using ELISA.RESULTSIn both cohorts, sC4d and sC5b-9 levels were increased in BAL of subjects with PGD compared with those without PGD. Subjects with PGD also had higher C1q, C2, C4, and C4b, compared with subjects without PGD, suggesting classical and lectin pathway involvement. Ba levels were higher in subjects with PGD, suggesting alternative pathway activation. Among lectin pathway-specific components, MBL and FCN-3 had a moderate-to-strong correlation with the terminal complement complex in the BAL but not in the plasma.CONCLUSIONComplement activation fragments are detected in the BAL within 24 hours after LTx. Components of all 3 pathways are locally increased in subjects with PGD. Our findings create a precedent for investigating complement-targeted therapeutics to mitigate PGD.FUNDINGThis research was supported by the NIH, American Lung Association, Children\u27s Discovery Institute, Robert Wood Johnson Foundation, Cystic Fibrosis Foundation, Barnes-Jewish Hospital Foundation, Danish Heart Foundation, Danish Research Foundation of Independent Research, Svend Andersen Research Foundation, and Novo Nordisk Research Foundation

When a calorie is not just a calorie : Diet quality and timing as mediators of metabolism and healthy aging

Funding Information: We thank Dr. Yih-Woei Fridell of the National Institute on Aging for organizing the meeting, as well as the NIA Division of Aging Biology for their support. We thank Dr. Gino Cortopassi for his edits and suggestions. The figures were created with BioRender.com. The Mihaylova lab is supported in part by the NIA (R00AG054760), Office of the NIH Director (DP2CA271361), the American Federation for Aging Research, the V Foundation, Pew Biomedical Scholar award, and startup funds from the Ohio State University. The Delibegovic lab is funded by the British Heart Foundation, Diabetes UK, BBSRC, NHS Grampian, Tenovus Scotland, and the Development Trust (University of Aberdeen). J.J.R. is supported by NIA PO1AG062817, R21AG064290, and R21AG071156. Research support for J.B. was from NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01DK127800, R01DK113011, R01DK090625, and R01DK050203 and the National Institute on Aging (NIA) grants R01AG065988 and P01AG011412, as well as the University of Chicago Diabetes Research and Training Center grant P30DK020595. This work was supported by NIH grants AG065992 to G.M. and AG068550 to G.M. and S.P. as well as UAB Startup funds 3123226 and 3123227 to G.M. R.S. is supported by NIH grants RF1AG043517, R01AG065985, R01DK123327, R56AG074568, and P01AG031782. Z.C. is primarily funded by The Welch Foundation (AU-1731-20190330) and NIH/NIA (R01AG065984, R56AG063746, RF1AG061901, and R56AG076144). A.C. is supported by NIA grant R01AG065993. W.W.J. is supported by the NIH (R01DC020031). M.S.-H. is supported by NIH R01 R35GM127049, R01 AG045842, and R21 NS122366. The research in the Dixit lab was supported in part by NIH grants AG031797, AG045712, P01AG051459, AR070811, AG076782, AG073969, and AG068863 and Cure Alzheimer's Fund (CAF). A.E.T.-M. is supported by the NIH/NIA (AG075059 and AG058630), NIAMS (AR071133), NHLBI (HL153460), pilot and feasibility funds from the NIDDK-funded UAB Nutrition Obesity Research Center (DK056336) and the NIA-funded UAB Nathan Shock Center (AG050886), and startup funds from UAB. J.A.M. is supported by the Intramural Research Program, NIA, NIH. The Panda lab is supported by the NIH (R01CA236352, R01CA258221, RF1AG068550, and P30CA014195), the Wu Tsai Human Performance Alliance, and the Joe and Clara Tsai Foundation. The Lamming lab is supported in part by the NIA (AG056771, AG062328, AG061635, and AG081482), the NIDDK (DK125859), startup funds from UW-Madison, and the U.S. Department of Veterans Affairs (I01-BX004031), and this work was supported using facilities and resources from the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government. D.W.L. has received funding from, and is a scientific advisory board member of, Aeovian Pharmaceuticals, which seeks to develop novel, selective mTOR inhibitors for the treatment of various diseases. S.P. is the author of the books The Circadian Code and The Circadian Diabetes Code. Funding Information: We thank Dr. Yih-Woei Fridell of the National Institute on Aging for organizing the meeting, as well as the NIA Division of Aging Biology for their support. We thank Dr. Gino Cortopassi for his edits and suggestions. The figures were created with BioRender.com . The Mihaylova lab is supported in part by the NIA ( R00AG054760 ), Office of the NIH Director ( DP2CA271361 ), the American Federation for Aging Research , the V Foundation , Pew Biomedical Scholar award, and startup funds from the Ohio State University . The Delibegovic lab is funded by the British Heart Foundation , Diabetes UK , BBSRC , NHS Grampian , Tenovus Scotland , and the Development Trust ( University of Aberdeen ). J.J.R. is supported by NIA PO1AG062817 , R21AG064290 , and R21AG071156 . Research support for J.B. was from NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01DK127800 , R01DK113011 , R01DK090625 , and R01DK050203 and the National Institute on Aging (NIA) grants R01AG065988 and P01AG011412 , as well as the University of Chicago Diabetes Research and Training Center grant P30DK020595 . This work was supported by NIH grants AG065992 to G.M. and AG068550 to G.M. and S.P., as well as UAB Startup funds 3123226 and 3123227 to G.M. R.S. is supported by NIH grants RF1AG043517 , R01AG065985 , R01DK123327 , R56AG074568 , and P01AG031782 . Z.C. is primarily funded by The Welch Foundation ( AU-1731-20190330 ) and NIH/NIA ( R01AG065984 , R56AG063746 , RF1AG061901 , and R56AG076144 ). A.C. is supported by NIA grant R01AG065993 . W.W.J. is supported by the NIH ( R01DC020031 ). M.S.-H. is supported by NIH R01 R35GM127049 , R01 AG045842 , and R21 NS122366 . The research in the Dixit lab was supported in part by NIH grants AG031797 , AG045712 , P01AG051459 , AR070811 , AG076782 , AG073969 , and AG068863 and Cure Alzheimer's Fund (CAF). A.E.T.-M. is supported by the NIH/NIA ( AG075059 and AG058630 ), NIAMS ( AR071133 ), NHLBI ( HL153460 ), pilot and feasibility funds from the NIDDK -funded UAB Nutrition Obesity Research Center ( DK056336 ) and the NIA -funded UAB Nathan Shock Center ( AG050886 ), and startup funds from UAB . J.A.M. is supported by the Intramural Research Program, NIA, NIH . The Panda lab is supported by the NIH ( R01CA236352 , R01CA258221 , RF1AG068550 , and P30CA014195 ), the Wu Tsai Human Performance Alliance , and the Joe and Clara Tsai Foundation . The Lamming lab is supported in part by the NIA ( AG056771 , AG062328 , AG061635 , and AG081482 ), the NIDDK ( DK125859 ), startup funds from UW-Madison , and the U.S. Department of Veterans Affairs ( I01-BX004031 ), and this work was supported using facilities and resources from the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government.Peer reviewedPostprin

Research Ethics Education for Community-Engaged Research: A Review and Research Agenda

Community engagement is increasingly becoming an integral part of research. “Community-engaged research” (CEnR) introduces new stakeholders as well as unique challenges to the protection of participants and the integrity of the research process. We—a group of representatives of CTSA-funded institutions and others who share expertise in research ethics and CEnR—have identified gaps in the literature regarding (1) ethical issues unique to CEnR; (2) the particular instructional needs of academic investigators, community research partners, and IRB members; and (3) best practices for teaching research ethics. This paper presents what we know, as well as what we still need to learn, in order to develop quality research ethics educational materials tailored to the full range of stakeholder groups in CEnR

Nebraska\u27s Advantage: Productive Agriculture and Bountiful Natural Resources

Nebraska\u27s Advantage: Productive Agriculture and Bountiful Natural Resources You can’t talk about healthy production systems and natural resource systems without also thinking about the resilience. We need to harmonize production agriculture in a way that ensures the resilience of our natural ecosystems and rural communities—all while addressing bundles of grand challenges

Study of Flare Assessment in Systemic Lupus Erythematosus Based on Paper Patients.

OBJECTIVE: To determine the level of agreement of disease flare severity (distinguishing severe, moderate, and mild flare and persistent disease activity) in a large paper-patient exercise involving 988 individual cases of systemic lupus erythematosus. METHODS: A total of 988 individual lupus case histories were assessed by 3 individual physicians. Complete agreement about the degree of flare (or persistent disease activity) was obtained in 451 cases (46%), and these provided the reference standard for the second part of the study. This component used 3 flare activity instruments (the British Isles Lupus Assessment Group [BILAG] 2004, Safety of Estrogens in Lupus Erythematosus National Assessment [SELENA] flare index [SFI] and the revised SELENA flare index [rSFI]). The 451 patient case histories were distributed to 18 pairs of physicians, carefully randomized in a manner designed to ensure a fair case mix and equal distribution of flare according to severity. RESULTS: The 3-physician assessment of flare matched the level of flare using the 3 indices, with 67% for BILAG 2004, 72% for SFI, and 70% for rSFI. The corresponding weighted kappa coefficients for each instrument were 0.82, 0.59, and 0.74, respectively. We undertook a detailed analysis of the discrepant cases and several factors emerged, including a tendency to score moderate flares as severe and persistent activity as flare, especially when the SFI and rSFI instruments were used. Overscoring was also driven by scoring treatment change as flare, even if there were no new or worsening clinical features. CONCLUSION: Given the complexity of assessing lupus flare, we were encouraged by the overall results reported. However, the problem of capturing lupus flare accurately is not completely solved