Mycoprotein as novel functional ingredient: Mapping of functionality, composition and structure throughout the Quorn fermentation process

Without consideration of other linked QTLs responsible for dynamic trait, original functional mapping based on a single QTL model is not optimal for analyzing multiple dynamic trait loci. Despite that composite functional mapping incorporates the effects of genetic background outside the tested QTL in mapping model, the arbitrary choice of background markers also impact on the power of QTL detection. In this study, we proposed Bayesian functional mapping strategy that can simultaneously identify multiple QTL controlling developmental patterns of dynamic traits over the genome. Our proposed method fits the change of each QTL effect with the time by Legendre polynomial and takes the residual covariance structure into account using the first autoregressive equation. Also, Bayesian shrinkage estimation was employed to estimate the model parameters. Especially, we specify the gamma distribution as the prior for the first-order auto-regressive coefficient, which will guarantee the convergence of Bayesian sampling. Simulations showed that the proposed method could accurately estimate the QTL parameters and had a greater statistical power of QTL detection than the composite functional mapping. A real data analysis of leaf age growth in rice is used for the demonstration of our method. It shows that our Bayesian functional mapping can detect more QTLs as compared to composite functional mapping.

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age.

Background: Plants have evolved the dual capacity for maximizing light assimilation through stem growth (phototropism) and maximizing water and nutrient absorption through root growth (gravitropism). Previous studies have revealed the physiological and molecular mechanisms of these two processes, but the genetic basis for how gravitropism and phototropism interact and coordinate with one another to determine plant growth remains poorly understood. Methods: We designed a seed germination experiment using a full-sib F1 family of Populus euphratica to simultaneously monitor the gravitropic growth of the radicle and the phototropic growth of the plumule throughout seedling ontogeny. We implemented three functional mapping models to identify quantitative trait loci (QTLs) that regulate gravitropic and phototropic growth. Univariate functional mapping dissected each growth trait separately, bivariate functional mapping mapped two growth traits simultaneously, and composite functional mapping mapped the sum of gravitropic and phototropic growth as a main axis. Results: Bivariate model detected 8 QTLs for gravitropism and phototropism (QWRF, GLUR, F-box, PCFS4, UBQ, TAF12, BHLH95, TMN8), composite model detected 7 QTLs for growth of main axis (ATL8, NEFH, PCFS4, UBQ, SOT16, MOR1, PCMP-H), of which, PCFS4 and UBQ were pleiotropically detected with the both model. Many of these QTLs are situated within the genomic regions of candidate genes. Conclusions: The results from our models provide new insight into the mechanisms of genetic control of gravitropism and phototropism in a desert tree, and will stimulate our understanding of the relationships between gravity and light signal transduction pathways and tree adaptation to arid soil.

The alignment accuracy and impact on functional maps of four spatial normalization procedures have been compared using a set of high resolution brain MRIs and functional PET volumes acquired in 20 subjects. Simple affine (AFF), fifth order polynomial warp (WRP), discrete cosine basis functions (SPM), and a movement model based on full multi grid (FMG) approaches were applied on the same dataset for warping individual volumes onto the Human Brain Atlas (HBA) template. Intersubject averaged structural volumes and tissue probability maps were compared across normalization methods and to the standard brain. Thanks to the large number of degrees of freedom of the technique, FMG was found to provide enhanced alignment accuracy as compared to the other three methods, both for the grey and white matter tissues; WRP and SPM exhibited very similar performances whereas AFF had the lowest registration accuracy. SPM, however, was found to perform better than the other methods for the intra-cerebral cerebrospinal fluid (mainly in the ventricular compartments). Limited differences in terms of activation morphology and detection sensitivity were found between low resolution functional maps (FWHM approximately 10 mm) spatially normalized with the four methods, which overlapped in 42.8% of the total activation volume. These findings suggest that the functional variability is much larger than the anatomical one and that precise alignment of anatomical features has low influence on the resulting intersubject functional maps. When increasing the spatial resolution to approximately 6 mm, however, differences in localization of activated areas appear as a consequence of the different spatial normalization procedure used, restricting the overlap of the normalized activated volumes to only 6.2%.

Structural and functional connectivity mapping of the human corpus callosum organization with white-matter functional networks

ObjectivesDry-aging is a traditional butchery process, but currently, it has been more practiced in a niche market as a value-adding process. As dry aging involves placing primal/sub-primal sections under a controlled refrigerated condition without packaging materials, the formation of the dried surface (crust) is inevitable due to moisture evaporation. A considerable portion of the crust is to be trimmed off as waste, which is one of the major drawbacks of dry aging. While the beef crust may still exert its functional/technological properties, no information is available regarding the efficacy of utilizing beef crust as a potential food ingredient. Thus, the objective of this study was to determine the physicochemical and functional properties of beef crust from dry-aged beef loins processed under various dry-aging conditions.Materials and MethodsPaired bone-in beef shell loins from 13 cull cow carcasses (C-maturity) were obtained at 5d postmortem, divided into 2 sections and assigned to four aging treatments: wet-aging (WA), dry-aging (DA), dry-aging in water-permeable bag (DWA) and dry-aging under UV-light (UDA; 5 J/s/12 h per day). Beef sections were aged for 28d at 2°C, 65% RH and 0.8 m/s air flow. After aging, the crusts were separated and beef samples from WA and initial (aged for 0 d, INI) were collected for comparison. In three independent batches, the crust samples were freeze-dried and powdered. Moisture contents of samples were measured before lyophilization. Emulsification capacity, salt-soluble protein solubility, emulsifying activity index, and surface hydrophobicity were determined. CIE* color attributes, lipid oxidation (TBARS), and protein oxidation (carbonyl and thiol contents) were measured. The PROC MIXED procedure of SAS was used to analyze the data. Significance level of least square means was set at the confidence level of 95%.ResultsBeef crusts from dry-aged loins had lower moisture contents compared with WA and INI (P < 0.05), while no difference between dry-aging methods was found (P > 0.05). The crust samples had lower L* and chroma values than WA and INI (P < 0.05). Emulsification capacity of DA, DWA, and UDA were lower than WA and INI (P < 0.05), with DA being the lowest (P < 0.05). In general, the crust had a significantly higher salt-soluble protein solubility compared to WA, while no difference between crusts and INI was found (P > 0.05). For emulsifying activity index, DA exhibited higher values than DWA, UDA and WA (P < 0.05), and was comparable to INI (P > 0.05). DA and INI had higher surface hydrophobicity values than the other samples, which could possibly explain the results of emulsifying activity index. A trend of higher TBARS values was found in all dry-aged crusts than WA and INI (P = 0.0688). The crust from dry aging had a higher carbonyl content compared to WA (P < 0.05), while thiol contents were not affected by the treatment (P = 0.1092).ConclusionThe results from the current study indicate that beef crusts exert its functional and technological properties, which could be superior or at least equivalent to wet-aged or unaged beef samples. This study provides novel insight into the potential feasibility and utilization of beef crust from dry-aged beef as a value-added product. Further studies determining the practical application of beef crust as a novel food ingredient (e.g., meat emulsion or beef patty) are in progress.

Fish protein hydrolysates were prepared from Catla catla using different proteases viz. alcalase, bromelain, flavorzyme and protamex and designated as HA (alcalase), HB (bromelain), HF (flavorzyme) and HP (protamex), respectively. HB was found to have higher 2, 2 diphynyl-1-picrylhydrazyl (DPPH) free radical-scavenging activity followed by HF, HP and HA. HB had higher ferric-reducing power followed by HP, HF and HA. Linoleic acid peroxidation inhibition of HA, HB, HF and HP were 54 ± 4.75, 50 ± 2.60, 49 ± 3.00 and 36 ± 6.15%, respectively. Higher emulsion stability index and foaming capacity were recorded for HB. The emulsion activity index was higher in HP (52.76 ± 1.27 m2/g). The foaming stability was found to be good in HF. Practical Application The properties of fish protein hydrolysates from Catla catla using different enzymes have been studied. The protein hydrolysates prepared from C. catla can be used as antioxidant and functional ingredient in the processed food products. Among four different enzymes used, bromelain was effective in obtaining the protein hydrolysate with desired antioxidant and functional properties. The potential to use bromelain for hydrolysate preparation is high considering the abundance of pineapples in southern India.

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PTPRD pleiotropy, genetically driven childbirth timing, and corpus callosum microstructure as potential mechanisms underlying ADHD-RLS comorbidity.

While the association between smoking and accelerated facial aging is well documented, the specific pathways underlying this association remain poorly understood. To investigate the shared genetic architecture between smoking and facial aging, we performed genetic analyses based on genome-wide association studies (GWAS) data. These analyses included linkage disequilibrium score regression (LDSC), pleiotropic analysis under composite null hypothesis (PLACO), functional mapping and annotation (FUMA), and multi-marker analysis of genomic annotation (MAGMA). To further explore the shared target genes, we utilized expression quantitative trait loci (eQTLs) and mediation Mendelian randomization (MR) analysis, with subsequent validation conducted through in vitro experiments using NIH/3T3 cells. Additionally, we carried out pan-cancer correlation analyses to assess the broader implications of the identified genes in cancer biology. Through pleiotropy and colocalization analyses, IREB2, along with CHRNA5 and AARS1, were identified as having strong evidence linking smoking and facial aging. Functional enrichment, tissue-specific analyses, and gene co-expression network were conducted to further elucidate the functions of these genes. Following eQTLs and mediation analyses, IREB2 was identified as a potential mediator connecting smoking to facial aging. Cellular experiments demonstrated that exposure to cigarette smoke particles induces cellular senescence and downregulates IREB2 expression. The pan-cancer analysis highlighted IREB2's role in shaping the tumor microenvironment and influencing immune processes. This study identifies IREB2 as a critical factor in the molecular mechanisms by which smoking accelerates facial aging, while also contributing to tumor development and immune evasion. Further functional exploration of IREB2 could uncover new therapeutic avenues to address these interconnected conditions.

Understanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we performed a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression. Five hundred and twenty-six individuals participated in the study across two sites (356 male, average age 52.4 ± 14.5 years). Each subject had a focal brain lesion identified on structural imaging and an assessment of depression using the Beck Depression Inventory-II, both obtained in the chronic period post-lesion (>3 months). Multivariate lesion-symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as 'risk' versus 'resilience' regions. The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively. Lesion-symptom mapping identified brain regions significantly associated with both higher and lower depression severity (r = 0.11; P = 0.01). Peak 'risk' regions include the bilateral anterior insula, bilateral dorsolateral prefrontal cortex and left dorsomedial prefrontal cortex. Functional lesion network mapping demonstrated that these 'risk' regions localized to nodes of the salience network. Peak 'resilience' regions include the right orbitofrontal cortex, right medial prefrontal cortex and right inferolateral temporal cortex, nodes of the default mode network. Structural lesion network mapping implicated dorsal prefrontal white matter tracts as 'risk' tracts and ventral prefrontal white matter tracts as 'resilience' tracts, although the structural lesion network mapping findings did not survive correction for multiple comparisons. Taken together, these results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

In the summer of 2017, I contacted Shawn, then working at the Max Planck Institute in Martinsried, to ask him to join the Editorial Board of the Journal of Comparative Neurology (JCN). I was very impressed by his scientific acumen and his research on high-throughput electron microscopy that he wanted to apply at the level of an entire brain. Shawn immediately accepted and became a member of our journal's board in January 2018. Even before he was officially a member of the board, with characteristic enthusiasm, he began to assemble a group of contributors for a special issue of JCN dedicated to cutting-edge methods and approaches to structural and functional mapping of the mouse brain. Most unfortunately, his tenure as a member of JCN's board was to be a short one. With great sadness, we learned in July 2018 Shawn's tragic passing. We lost a great friend, a highly dedicated and dynamic colleague, and one of the most talented and promising young investigator in neuroscience at a point when he was developing his career independently at Keio University in Japan. Shawn's career was brief and cut short, but remarkably productive. He received his College Education at the University of Texas in Austin, where he graduated in 1998 with no fewer than three BS degrees in Mathematics, Physics, and Biochemistry. He then completed his PhD in neuroscience in 2005 at Johns Hopkins University School of Medicine at the Krieger Mind-Brain Institute in Baltimore, MD, under the mentorship of Ernst Niebur. Afterward, he was a postdoctoral researcher with Ted Jones at the University of California Davis where he developed the BrainMaps project, an internet-enabled multiresolution histochemical and tract-tracing brain atlas for various mammalian species. He moved on to Germany in 2009 for further postdoctoral work with Winfried Denk at the Max Planck Institute for Medical Research in Heidelberg, to pursue his work on comprehensive mapping the mammalian whole-brain circuitry and structure at the synaptic level using high-throughput volume electron microscopy. He became a Researcher at the Max Planck Institute for Neurobiology in Martinsried in 2013 where he developed methods to prepare small mammalian brains for electron microscopy and to image them using high-throughput multibeam scanning electron microscopy. In 2018, he moved to Japan, first at the National Institute for Physiological Sciences in Okazaki, and a few months later to Keio University School of Medicine in Tokyo, where he had just been appointed Assistant Professor at the time he passed away, working on a comprehensive ultrastructural mapping of the circuitry of the whole mouse brain at a single-synapse level of resolution. His goals were eventually to apply these approaches to small nonhuman primate brains. A list of Shawn's major scientific contributions appears at the end of this editorial. This special issue reflects Shawn's last efforts as an editor for our journal. It represents our recognition of his outstanding work and a memory of his contributions to understanding the mammalian whole-brain connectome and developing new techniques “to reconstruct the whole brain's wiring diagram.” The articles assembled in the present issue summarize recent efforts to attain a functional and structural connectome map of the mouse brain through multimodal and multiscale approaches. I am personally indebted to the many contributors who participated in the completion of this issue after Shawn's death and graciously helped me finalize it in a timely manner to celebrate the memory of our friend and colleague. We also express our most sincere and heartfelt condolences to his friends and family.

Closed-loop control of bistable symptom states

We have analyzed structure--function relationships of the protein hormone murine interleukin 2 by fine structural deletion mapping. A total of 130 deletion mutant proteins, together with some substitution and insertion mutant proteins, was expressed in Escherichia coli and analyzed for their ability to sustain the proliferation of a cloned murine T cell line. This analysis has permitted a functional map of the protein to be drawn and classifies five segments of the protein, which together contain 48% of the sequence, as unessential to the biological activity of the protein. A further 26% of the protein is classified as important, but not crucial, for the activity. Three regions, consisting of amino acids 32-35, 66-77 and 119-141 contain the remaining 26% of the protein and are critical to the biological activity of the protein. The functional map is discussed in the context of the possible role of the identified critical regions in the structure of the hormone and its binding to the interleukin 2 receptor complex.