Multiple Complex Congenital Malformations in a Rabbit Kit (Oryctolagus cuniculi)

Congenital malformations may occur during early embryogenesis in cases of genetic abnormalities or various environmental factors. Affected subjects most often have only one or 2 abnormalities; subjects rarely have several unrelated congenital defects. Here we describe a case of a stillborn New Zealand white rabbit with multiple complex congenital malformations, including synophthalmia, holoprosencephaly, gastroschisis, and a supernumerary hindlimb, among other anomalies. There was no historical exposure to teratogens or other known environmental causes. Although not confirmed, this case was most likely a rare spontaneous genetic event.Congenital malformations occur when there is derangement of the embryologic developmental process. Neural development and organogenesis is a critical time of development that occurs during early embryogenesis.^2,37 Congenital malformations that manifest at this stage of development may occur in association with various genetic abnormalities, such as point mutations and chromosomal abnormalities.^25,29 In addition, environmental factors, including maternal health status, nutritional deficiencies, and exposure to teratogenic drugs or chemicals, may play a role in the development of congenital malformations.^12,25 However, in 65% to 75% of human cases, the cause is unknown, resulting from a complex set of events such as polygenic or multifactorial genetic disorders, spontaneous genetic errors, and synergistic interactions of teratogens.^3,25 Approximately 78% of human cases demonstrate only a single developmental malformation, with cardiovascular defects accounting for approximately 30% to 35% of organ defects. Cases of more than 2 or 3 malformations in a single person are extremely rare.²⁸Here we describe a New Zealand white rabbit that was stillborn with numerous complex developmental abnormalities, including synophthalmia, a supraoptic proboscis, holoprosencephaly with other associated craniofacial deformities, Chiari malformation type I, gastroschisis, a supernumerary hindlimb, a fused (horseshoe) kidney with a supernumerary kidney, and male pseudohermaphroditism.     

Dissolution Properties and Physical Characterization of Telmisartan–Chitosan Solid Dispersions Prepared by Mechanochemical Activation

Solid dispersion systems of telmisartan (a poorly water-soluble antihypertension drug) with biopolymer carrier chitosan have been investigated in this study. The mechanism of solubilization of chitosan for drug has been studied. In addition, the influence of several factors was carefully examined, including the preparation methods, the drug/carrier weight ratios, and the milling time. Drug dissolution and physical characterization of different binary systems were studied by in vitro dissolution test, particle size distribution, Fourier transform infrared spectroscopy, differential scanning calorimetry, powder X-ray diffractometry, and scanning electron microscopy. The results presented that the weak basic property of chitosan appeared as the main driving force for the drug dissolution enhancement. Other effects such as decreased drug crystallinity and size played a positive contributory role. Among the preparation methods, cogrinding was the best method showing strong drug amorphization, reduced particle size, and enhanced dissolution. The drug dissolution markedly improved with increasing the amount of chitosan in solid mixtures. As a result, a significant effect of chitosan increasing telmisartan dissolution has been demonstrated, and cogrinding in a roll ball mill was the best way to prepare solid dispersions, which had high degree of uniformity in drug content and had a practical application in manufacturing.     

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy–based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers—malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine—was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.Major depressive disorder (MDD)¹ is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptom-based method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6–10). Nuclear magnetic resonance (NMR) spectroscopy–based metabonomic approaches characterized by sensitive, high-throughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11–13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14–19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psychiatric disorders in the urine. For example, using an NMR-based metabonomics approach, Yap et al. (21) identified a unique urinary metabolite signature that clearly discriminated autism patients from healthy controls. As systemic metabolic disturbances have been observed in the urine of a depressed animal model, it is likely that diagnostic metabolite markers for MDD can be detected in human urine.Therefore, in this study, NMR spectroscopy combined with multivariate pattern recognition techniques were used to profile 82 first-episode drug-naïve MDD subjects and 82 healthy controls (the training set) in order to identify potential metabolite biomarkers for MDD. Furthermore, 44 unselected MDD subjects and 52 healthy controls (the test set) were employed to independently validate the diagnostic performance of these urinary metabolite biomarkers.     

Quantitative Measurement of Phosphoproteome Response to Osmotic Stress in Arabidopsis Based on Library-Assisted eXtracted Ion Chromatogram (LAXIC)

Global phosphorylation changes in plants in response to environmental stress have been relatively poorly characterized to date. Here we introduce a novel mass spectrometry-based label-free quantitation method that facilitates systematic profiling plant phosphoproteome changes with high efficiency and accuracy. This method employs synthetic peptide libraries tailored specifically as internal standards for complex phosphopeptide samples and accordingly, a local normalization algorithm, LAXIC, which calculates phosphopeptide abundance normalized locally with co-eluting library peptides. Normalization was achieved in a small time frame centered to each phosphopeptide to compensate for the diverse ion suppression effect across retention time. The label-free LAXIC method was further treated with a linear regression function to accurately measure phosphoproteome responses to osmotic stress in Arabidopsis. Among 2027 unique phosphopeptides identified and 1850 quantified phosphopeptides in Arabidopsis samples, 468 regulated phosphopeptides representing 497 phosphosites have shown significant changes. Several known and novel components in the abiotic stress pathway were identified, illustrating the capability of this method to identify critical signaling events among dynamic and complex phosphorylation. Further assessment of those regulated proteins may help shed light on phosphorylation response to osmotic stress in plants.Phosphorylation plays a pivotal role in the regulation of a majority of cellular processes via signaling transduction pathways. During the last decade, quantitative phosphoproteomics has become a powerful and versatile platform to profile signaling pathways at a system-wide scale. Multiple signaling networks in different organisms have been characterized through global phosphorylation profiling (1–3), which has evolved over the years with highly optimized procedures for sample preparation and phosphopeptide enrichment, high resolution mass spectrometry, and data analysis algorithms to identify and quantify thousands of phosphorylation events (4–8).Quantitative phosphoproteomics can be achieved mainly by two major techniques, stable isotope labeling and label-free quantitation. Isotope labeling prior to liquid chromatography-mass spectrometry (LC-MS)¹ has been widely used, including metabolic labeling such as stable isotope labeling by amino acids in cell culture (SILAC), chemical labeling such as multiplexed isobaric tags for relative and absolute quantification (iTRAQ) and isotope-coded affinity tags (ICAT) (9–12). On the other hand, label-free quantitation has gained momentum in recent years (13–15), as no additional chemistry or sample preparation steps are required. Compared with stable isotope labeling, label-free quantitation has higher compatibility with the source of the samples, the number of samples for comparison, and the instrument choice.Many label-free approaches, in particular to phosphoproteomics, are based on ion intensity (16, 17), but they are relatively error-prone because of run-to-run variations in LC/MS performance (18). In theory, such systematic errors can be corrected by spiking an internal standard into every sample to be compared. Several methods based on internal standard spiking have been reported so far. Absolute quantification peptide technology (AQUA) (19), for example, uses synthetic peptides with isotope labeling for absolute quantitation. For a global quantitative proteomics study, it is unrealistic to spike-in all reference peptides. Another labeling reference method, spike-in SILAC appears to be a promising technique to quantify the proteome in vivo with multiplex capability and it can be extended to clinical samples (20). One solution to large-scale quantitation without any isotope labeling is pseudo internal standard approach (21), which selects endogenous house-keeping proteins as the internal standard so that no spike-in reagent is required. However, finding a good pseudo internal standard remains a challenge for phosphoproteome samples. Spike-in experiments are an alternative way to improve normalization profile. Some internal standard peptides such as MassPREP^TM (Waters) were already widely used. Other groups spiked an identical amount of standard protein into samples prior to protein digestion (22–24). There are two major normalization procedures. In one approach, sample peptides were normalized to the total peak intensity of spike-in peptides (25). Alternatively, the digested peptides were compared at first and the normalization factor was determined in different ways such as the median (26) or average of ratios (27). However, spiking an identical amount of standard proteins into phosphoproteomic samples before protein digestion may not be compatible with phosphoproteomic analyses which typically require a phosphopeptide enrichment step. Spectral counting has been extensively applied in large sets of proteomic samples because of its simplicity but the method is often not reliable for the quantitation of phosphoproteins, which are typically identified by single phosphopeptides with few spectra (28–30). Many software packages have been implemented to support the wide variety of those quantitation techniques, including commercial platforms such as Progenesis LC-MS^TM, Mascot Distiller^TM, PEAKS Q^TM, etc., as well as open-source software packages including MaxQuant (31), PEPPeR (32), Skyline (33), etc.In this study, we have devised a novel label-free quantitation strategy termed Library Assisted eXtracted Ion Chromatogram (LAXIC) for plant phosphoproteomic analyses with high accuracy and consistency (Fig. 1). The approach employs synthetic peptide libraries as the internal standard. These peptides were prepared to have proper properties for quality control assessments and mass spectrometric measurements. In particular, peptides were designed to elute sequentially over an entire LC gradient and to have suitable ionization efficiency and m/z values within the normally scanned mass range. Local normalization of peak intensity is performed using Loess Procedure, a data treatment adopted from cDNA microarray data analysis (34). To monitor the diverse ion suppression effect across retention time, the local normalization factors (LNFs) are determined by internal standard pairs in individual time windows. Finally, samples will be quantified with LNFs in order to correct variance of LC-MS conditions. This quantification occurs in a small time frame centered to each target peptide.Open in a separate windowFig. 1.Work flow for the LAXIC strategy to quantify the phosphorylation change in response to osmotic stress. A, Schematic representation of the LAXIC algorithm. First, all the chromatographic peaks were aligned and the ratios were calculated. Second, the normalization factors which equal to ratios of library peptide peaks between MS runs were chosen to construct normalization curve. Third, sample peptide peak ratios were normalized against predicted normalization factor corresponding to certain retention time. B, Schematic representation of quantitative phosphoproteomics. Plants either treated with mannitol or PBS were lysed and mixed proportionally at first. Following peptide digestion and enrichment, phosphopeptides were identified and further quantified through LAXIC incorporated with self-validating process using thelinear regression model to analyze the fold change (fold), linearity (R²) and accuracy (%Acc).Water deficit and salinity causes osmotic stress, which is a major environmental factor limiting plant agricultural productivity. Osmotic stress rapidly changes the metabolism, gene expression and development of plant cells by activating several signaling pathways. Several protein kinases have been characterized as key components in osmotic stress signaling. Arabidopsis histidine kinase AHK1 can complement the histidine kinase mutant yeast, which can act as the osmosensor in yeast (35). Overexpression of AHK1 gene increases the drought tolerance of transgenic plants in Arabidopsis (36). Similar to yeast, the MAPK kinase cascade is also involved in osmotic stress response in plants. It is reported that AtMPK3, AtMPK6, and tobacco SIPK can be activated by NaCl or mannitol, and play positive roles in osmotic signaling (37, 38). MKK7 and MKKK20 may act as the up-stream kinase in the kinase cascade (39). Involvement of some calcium-dependent protein kinases, such as AtCPK21, AtCPK6, and OsCPK7 (CDPK) in osmotic stress signaling has also been reported (40–42). Another kinase family, SNF1-related protein kinase (SnRK) 2, also participates in osmotic stress response. In Arabidopsis, there are ten members in the SnRK2 family. Five from the ten SnRK2s, SnRK2, 3, 6, 7, and 8, can be activated by abscisic acid (ABA) and play central roles in ABA-receptor coupled signaling (43, 44). Furthermore, all SnRK2s except SnRK2.9 can be activated by NaCl or mannitol treatment (43). The decuple mutant of SnRK2 showed a strong osmotic hypersensitive phenotype (45). It is proposed that protein kinases including MAPK and SnRK2s have a critical function in osmotic stress (46), but the detailed mechanism and downstream substrates or target signal components are not yet clarified. We applied, therefore, the LAXIC approach with a self-validating method (47) to profile the osmotic stress-dependent phosphoproteome in Arabidopsis by quantifying phosphorylation events before and after mannitol treatment. Among a total of over 2000 phosphopeptides, more than 400 of them are dependent on osmotic stress. Those phosphoproteins are present on enzymes participating in signaling networks that are involved in many processes such as signal transduction, cytoskeleton development, and apoptosis. Overall, LAXIC represents a powerful tool for label-free quantitative phosphoproteomics.     

PDEA-coated magnetic nanoparticles for gene delivery to Hep G2 cells

Poly(2-(diethylamino)ethyl methacrylate) coated magnetic nanoparticles (PDEA-MNPs) were synthesized as a new gene nanocarrier to delivery plasmids (pEGFPN1 and pRL-TK) into human hepatoma (Hep G2) cells. The PDEA-MNPs shows the pH-sensitive property. These nanoparticles are positively charged at acidic pH and negatively charged at neutral or alkaline pH. The PDEAMNPs exhibited a low cytotoxicity in Hep G2 cells. PDEA-MNPs could bind and protect DNA from DNase I degradation. The transfection study demonstrated that the PDEA-MNPs could carry plasmid into Hep G2 cells and exhibited a high gene transfection efficiency. These results indicated that the novel magnetic nanoparticles could enhance gene transfection in vitro and hold the potential to be a promising non-viral nanodevice.     

Lactobacillus paracasei subsp. paracasei LC01 positively modulates intestinal microflora in healthy young adults

Lactobacillus paracasei subsp. paracasei LC01 (LC01) can tolerate intestinal stresses and has antioxidant activity. To evaluate the effect of the bacterium on human intestinal microflora, a randomized, double-blind, placebo-controlled human trial was carried out. Fifty-two healthy adult volunteers were randomized equally to two groups. One group consumed 12% (wt/vol) skimmed milk supplemented with 10¹⁰ CFU of LC01 each day for the 4-week treatment period, and then consumed placebo in the next treatment period, separated by a 2-week washout. The other group followed the reverse order. Group-specific real-time PCR and biochemical analyses was used to determine the intestinal bacterial composition of fecal samples collected at the end of every period, and the concentration of short-chain fatty acids and ammonia. A significant inhibition in fecal Escherichia coli and increase in Lactobacillus, Bifidobacterium, and Roseburia intestinalis were observed after consumption of LC01. Acetic acid and butyric acid were significantly higher in the probiotic stage and fecal ammonia was significantly lower. The results indicated a modulation effect of LC01 on the intestinal microflora of young adults, suggesting a beneficial effect on bowel health. LC01 may have potential value as a probiotic.     

Molecular genetic characterization of rice seed lipoxygenase 3 and assessment of its effects on seed longevity

Lipoxygenases (LOXs) are enzymes involved in lipid peroxidation. Here we reported the identification, molecular and functional characterization of the gene encoding rice (Oryza sativa L.) seed LOX3 (sLOX3). Via a map-based cloning strategy we identified Os03g0700400 as the candidate gene encoding sLOX3. Further functional complementary test and biochemical characterization of the recombinant Os03g0700400 protein verified the identification. The sLOX3 gene was highly expressed in roots, moderately in embryos and very weakly in leaves, leaf sheaths and stems. Transient expression experiment (in rice protoplasts) and subsequent laser confocal microscopic analysis demonstrated that the sLOX3 protein was localized into the cytosol. We next showed that overexpression of sLOX3 in a japonica sLOX3-normal rice cultivar, Wuyunjing 7 accelerated the decrease of seed germination ability when the seeds were routinely stored, which demonstrated that sLOX3 had a negative effect on seed longevity (storability). Meanwhile, an increased occurrence of embryo decay was observed in the same transgenic seeds, suggesting that sLOX3 might negatively affect seed longevity by facilitating colonization of particular seed pathogens. Our result forwarded the understanding of the effects of 9-LOX on rice seed longevity.