Characterization of Cre recombinase activity for in vivo targeting of adipocyte precursor cells.

The increased incidence of obesity and metabolic disease underscores the importance of elucidating the biology of adipose tissue development. The recent discovery of cell surface markers for prospective identification of adipose precursor cells (APCs) in vivo will greatly facilitate these studies, yet tools for specifically targeting these cells in vivo have not been identified. Here, we survey three transgenic mouse lines, Fabp4-Cre, PdgfRα-Cre, and Prx1-Cre, precisely assessing Cre-mediated recombination in adipose stromal populations and mature tissues. Our data provide key insights into the utility of these tools to modulate gene expression in adipose tissues. In particular, Fabp4-Cre is not effective to target APCs, nor is its activity restricted to these cells. PdgfRα-Cre directs recombination in the vast majority of APCs, but also targets other populations. In contrast, adipose expression of Prx1-Cre is chiefly limited to subcutaneous inguinal APCs, which will be valuable for dissection of APC functions among adipose depots

Wandoo Walk 1

Cervical cancer is a life-threatening complication, appearing as the uncontrolled growth of abnormal cells in the lining of the cervix. Every year, increasing numbers of cervical cancer cases are reported worldwide. Different identification strategies were proposed to detect cervical cancer at the earlier stages using various biomarkers. Squamous cell carcinoma antigen (SCC-Ag) is one of the potential biomarkers for this diagnosis. Nanomaterial-based detection systems were shown to be efficient with different clinical biomarkers. In this study, we have demonstrated strontium oxide-modified interdigitated electrode (IDE) fabrication by the sol-gel method and characterized by scanning electron microscopy and high-power microscopy. Analysis of the bare devices indicated the reproducibility with the fabrication, and further pH scouting on the device revealed that the reliability of the working pH ranges from 3 to 9. The sensing surface was tested to detect SCC-Ag against its specific antibody; the detection limit was found to be 10 pM, and the sensitivity was in the range between 1 and 10 pM as calculated by 3σ. The specificity experiment was carried out using major proteins from human serum, such as albumin and globulin. SCC-Ag was shown to be selectively detected on the strontium oxide-modified IDE surface

Genetic parameters analysis of milk citrate for Holstein cows in early lactation

peer reviewedDelivering innovative and holistic monitoring and decision-making PLF tools relies on the availability of critical biomarkers. Negative energy balance is a difficult trait complex as there is a difference between perceived imbalance and physiological imbalance. Milk citrate is considered to be an early biomarker of negative energy balance for dairy cows in early lactation, but its genetic analysis is lacking. The objectives of this study were to (1) show the distribution of milk citrate content in early lactation; (2) analyze the genetic parameters of milk citrate. The coefficient of determination (R2) and root mean square error (RMSE) of the predicted milk citrate model by milk mid-infrared (MIR) spectra in external validation were 0.86 and 0.76 mmol/L, and available from DIM 5 to 50 d. Records were divided into three traits according to the first (citrate1), second (citrate2), and from third to fifth party (citrate3+). After editing, the data included 134,517 records, from 52,198 cows, and 4,479 animals in the pedigree with 566,170 SNPs. A multiple-trait repeatability model was used in this study. The citrate is decreasing in early lactation, on average from 10.04 to 8.58 mmol/L from DIM 5 to 50 d. When cows start to be in energy balance (DIM ≈ 40 d), milk citrate was 8.82 mmol/l. The average of citrate1 was 8.93 mmol/l; citrate2 was 8.93 mmol/l; citrate3+ was 9.17 mmol/l. The heritability for citrate1 was 0.40; for citrate2, 0.37 and for citrate3+, 0.35. The ranges of genetic correlations between the three traits were from 0.98 to 0.99, and of phenotypic correlations, from 0.41 to 0.42. This study shows that considering MIR-based milk citrate as an indicator to identify negative energy balance should be possible in early lactation and that this indicator could help select for animals less affected by negative energy balance

Relationship between proxies of energy states and nitrogen use efficiency for Holstein cows in early lactation

peer reviewedThe purposes of this study were to estimate the genetic parameters of the energy status indicator (C18:1 cis-9) of Holstein cows in early lactation and its relationship with nitrogen use efficiency (NUE) (predicted NUE and milk urea concentration). After editing, the data included 143,517 records within 5 to 50 days in milk from 52,198 cows, and 3,546 animals in the pedigree with 28,427 SNPs. Two multiple-trait repeatability models were used in this study. In early lactation, the average C18:1 cis-9 was gradually decreasing and was highest in May. The heritabilities of C18:1 cis-9 for primiparous and multiparous cows were 0.12 and 0.09, respectively. The C18:1 cis-9 had positive genetic correlations with predicted NUE (from 0.28 to 0.67), and weak genetic correlations with milk yield, milk urea concentration (from-0.15 to 0.14). This study suggests that breeding for NUE alone may enhance energy troubles in early lactation

Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls.

peer reviewedThe purposes of this study were (1) to explore the relationship between 3 milk mid-infrared predicted features including nitrogen intake (NINT), milk true protein N (MTPN), and milk urea-N yield (MUNY); (2) to integrate these 3 features into an N efficiency index (NEI) and analyses approximate genetic correlations between the NEI and 37 traits (indices) of interest; and (3) to assess the potential effect of including the NEI into breeding programs of bulls. The edited data were 1,043,171 test-day records on 342,847 cows in 1,931 herds and 143,595 test-day records on 53,660 cows in 766 herds used for estimating breeding values (EBV) and variance components, respectively. The used records were within 5 to 50 d in milk. The records were grouped into primiparous and multiparous. The genetic parameters for the included mid-infrared features and EBV of the animals included in the pedigree were estimated using a multiple-trait repeatability animal model. Then, the EBV of the NINT, MTPN, MUNY were integrated into the NEI using a selection index assuming weights based on the N partitioning. The approximate genetic correlations between the NEI and 37 traits of interest were estimated using the EBV of the selected bulls. The bulls born from 2011 to 2014 with NEI were selected and the NEI distribution of these bulls having EBV for the 8 selected traits (indices) was checked. The heritability and repeatability estimates for NINT, MTPN, and MUNY ranged from 0.09 to 0.13, and 0.37 to 0.65, respectively. The genetic and phenotypic correlations between NINT, MTPN, and MUNY ranged from -0.31 to 0.87, and -0.02 to 0.42, respectively. The NEI ranged from -13.13 to 12.55 kg/d. In total, 736 bulls with reliability ≥0.50 for all included traits (NEI and 37 traits) and at least 10 daughters distributed in at least 10 herds were selected to investigate genetic aspects of the NEI. The NEI had positive genetic correlations with production yield traits (0.08-0.46), and negative genetic correlations with the investigated functional traits and indices (-0.71 to -0.07), except for the production economic index and functional type economic index. The daughters of bulls with higher NEI had lower NINT and MUNY, and higher MTPN. Furthermore, 26% of the bulls (n = 50) with NEI born between 2011 to 2014 had higher NEI and global economic index than the average in the selected bulls. Finally, the developed NEI has the advantage of large-scale prediction and therefore has the potential for routine application in dairy cattle breeding in the future.13. Climate actio

Selenium tolerance, accumulation, localization and speciation in a Cardamine hyperaccumulator and a non-hyperaccumulator

Cardamine violifolia (family Brassicaceae) is the first discovered selenium hyperaccumulator from the genus Cardamine with unique properties in terms of selenium accumulation, i.e., high abundance of selenolanthionine. In our study, a fully comprehensive experiment was conducted with the comparison of a non-hyperaccumulator Cardamine species, Cardamine pratensis, covering growth characteristics, chlorophyll fluorescence, spatial selenium/sulfur distribution patterns through elemental analyses (synchrotron-based X-Ray Fluorescence and ICP-OES) and speciation data through selenium K-edge micro X-ray absorption near-edge structure analysis (μXANES) and strong cation exchange (SCX)-ICP-MS. The results revealed remarkable differences in contrast to other selenium hyperaccumulators as neither Cardamine species showed evidence of growth stimulation by selenium. Also, selenite uptake was not inhibited by phosphate for either of the Cardamine species. Sulfate inhibited selenate uptake, but the two Cardamine species did not show any difference in this respect. However, μXRF derived speciation maps and selenium/sulfur uptake characteristics provided results that are similar to other formerly reported hyperaccumulator and non-hyperaccumulator Brassicaceae species. μXANES showed organic selenium, "C-Se-C", in seedlings of both species and also in mature C. violifolia plants. In contrast, selenate-supplied mature C. pratensis contained approximately half "C-Se-C" and half selenate. SCX-ICP-MS data showed evidence of the lack of selenocystine in any of the Cardamine plant extracts. Thus, C. violifolia shows clear selenium-related physiological and biochemical differences compared to C. pratensis and other selenium hyperaccumulators

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation.

peer reviewedNitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs

Magnetic Resonance Imaging of Bone Marrow Cell-Mediated Interleukin-10 Gene Therapy of Atherosclerosis

A characteristic feature of atherosclerosis is its diffuse involvement of arteries across the entire human body. Bone marrow cells (BMC) can be simultaneously transferred with therapeutic genes and magnetic resonance (MR) contrast agents prior to their transplantation. Via systemic transplantation, these dual-transferred BMCs can circulate through the entire body and thus function as vehicles to carry genes/contrast agents to multiple atherosclerosis. This study was to evaluate the feasibility of using in vivo MR imaging (MRI) to monitor BMC-mediated interleukin-10 (IL-10) gene therapy of atherosclerosis.For in vitro confirmation, donor mouse BMCs were transduced by IL-10/lentivirus, and then labeled with a T2-MR contrast agent (Feridex). For in vivo validation, atherosclerotic apoE(-/-) mice were intravenously transplanted with IL-10/Feridex-BMCs (Group I, n = 5) and Feridex-BMCs (Group II, n = 5), compared to controls without BMC transplantation (Group III, n = 5). The cell migration to aortic atherosclerotic lesions was monitored in vivo using 3.0T MRI with subsequent histology correlation. To evaluate the therapeutic effect of BMC-mediated IL-10 gene therapy, we statistically compared the normalized wall indexes (NWI) of ascending aortas amongst different mouse groups with various treatments.Of in vitro experiments, simultaneous IL-10 transduction and Feridex labeling of BMCs were successfully achieved, with high cell viability and cell labeling efficiency, as well as IL-10 expression efficiency (≥90%). Of in vivo experiments, MRI of animal groups I and II showed signal voids within the aortic walls due to Feridex-created artifacts from the migrated BMCs in the atherosclerotic plaques, which were confirmed by histology. Histological quantification showed that the mean NWI of group I was significantly lower than those of group II and group III (P<0.05).This study has confirmed the possibility of using MRI to track, in vivo, IL-10/Feridex-BMCs recruited to atherosclerotic lesions, where IL-10 genes function to prevent the progression of atherosclerosis

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models