Simulation of Y-chromosomal haplotype data

The non-recombining nature of the Y-chromosome determines the non-independence of alleles between loci. The evolution of short tandem repeat (STR) loci in the Y-chromosome is the result of different factors such as differential mutation rates, mutation modes, gene conversion, selection and demographic processes. The degree of correlation between loci is dependent on the magnitude of these processes. The simulation of data is a routine tool used for testing hypotheses in population and evolutionary studies. The most basic parameters hitherto used in lineage haplotype simulations are the allele frequency distributions and mutation rates, assuming either full independence or linkage between loci. In this study we introduce use of the Spearman correlation coefficient to estimate the degree of dependence between non-recombining loci. Then, both the interdependence between loci and the allele frequency distributions at multi-allelic loci are incorporated in an algorithm for simulating haplotypes. We illustrate the method using published and unpublished Y-chromosome STR data.     

Moving forward with the primate microbiome: Introduction to a special issue of the American Journal of Primatology

Primate microbiome research is a quickly growing field with exciting potential for informing our understanding of primate biology, ecology, and evolution as well as host‐microbe interactions more broadly. This introductory essay to a special section of the American Journal of Primatology provides a cross‐sectional snapshot of current activity in these areas by briefly summarizing the diversity of contributed papers and their relationships to key themes in host‐associated microbiome research. It then uses this survey as a foundation for consolidating a set of key research questions to broadly guide future research. It also argues for the importance of methods standardization to facilitate comparative analyses and the identification of generalizable patterns and relationships. While primatology will benefit greatly from the integration of microbial datasets, it is uniquely positioned to address important questions regarding microbiology and macro‐ecology and evolution more generally. We are eager to see where the primate microbiome leads us.     

Gut microbiota in wild and captive Guizhou snub‐nosed monkeys,Rhinopithecus brelichi

Many colobine species—including the endangered Guizhou snub‐nosed monkey (Rhinopithecus brelichi) are difficult to maintain in captivity and frequently exhibit gastrointestinal (GI) problems. GI problems are commonly linked to alterations in the gut microbiota, which lead us to examine the gut microbial communities of wild and captive R. brelichi. We used high‐throughput sequencing of the 16S rRNA gene to compare the gut microbiota of wild (N = 7) and captive (N = 8) R. brelichi. Wild monkeys exhibited increased gut microbial diversity based on the Chao1 but not Shannon diversity metric and greater relative abundances of bacteria in the Lachnospiraceae and Ruminococcaceae families. Microbes in these families digest complex plant materials and produce butyrate, a short chain fatty acid critical to colonocyte health. Captive monkeys had greater relative abundances of Prevotella and Bacteroides species, which degrade simple sugars and carbohydrates, like those present in fruits and cornmeal, two staples of the captive R. brelichi diet. Captive monkeys also had a greater abundance of Akkermansia species, a microbe that can thrive in the face of host malnutrition. Taken together, these findings suggest that poor health in captive R. brelichi may be linked to diet and an altered gut microbiota.     

Pyrosequencing Unveils Cystic Fibrosis Lung Microbiome Differences Associated with a Severe Lung Function Decline

Chronic airway infection is a hallmark feature of cystic fibrosis (CF) disease. In the present study, sputum samples from CF patients were collected and characterized by 16S rRNA gene-targeted approach, to assess how lung microbiota composition changes following a severe decline in lung function. In particular, we compared the airway microbiota of two groups of patients with CF, i.e. patients with a substantial decline in their lung function (SD) and patients with a stable lung function (S). The two groups showed a different bacterial composition, with SD patients reporting a more heterogeneous community than the S ones. Pseudomonas was the dominant genus in both S and SD patients followed by Staphylococcus and Prevotella. Other than the classical CF pathogens and the most commonly identified non-classical genera in CF, we found the presence of the unusual anaerobic genus Sneathia. Moreover, the oligotyping analysis revealed the presence of other minor genera described in CF, highlighting the polymicrobial nature of CF infection. Finally, the analysis of correlation and anti-correlation networks showed the presence of antagonism and ecological independence between members of Pseudomonas genus and the rest of CF airways microbiota, with S patients showing a more interconnected community in S patients than in SD ones. This population structure suggests a higher resilience of S microbiota with respect to SD, which in turn may hinder the potential adverse impact of aggressive pathogens (e.g. Pseudomonas). In conclusion, our findings shed a new light on CF airway microbiota ecology, improving current knowledge about its composition and polymicrobial interactions in patients with CF.     

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).     

Mathematical Modeling of Renal Tubular Glucose Absorption after Glucose Load

A partial differential Progressive Tubular Reabsorption (PTR) model, describing renal tubular glucose reabsorption and urinary glucose excretion following a glucose load perturbation, is proposed and fitted to experimental data from five subjects. For each subject the Glomerular Filtration Rate was estimated and both blood and urine glucose were sampled following an Intra-Venous glucose bolus. The PTR model was compared with a model representing the conventional Renal Threshold Hypothesis (RTH). A delay bladder compartment was introduced in both formulations. For the RTH model, the average threshold for glycosuria varied between 9.90±4.50 mmol/L and 10.63±3.64 mmol/L (mean ± Standard Deviation) under different hypotheses; the corresponding average maximal transport rates varied between 0.48±0.45 mmol/min (86.29±81.22 mg/min) and 0.50±0.42 mmol/min (90.62±76.15 mg/min). For the PTR Model, the average maximal transports rates varied between 0.61±0.52 mmol/min (109.57±93.77 mg/min) and 0.83±0.95 mmol/min (150.13±171.85 mg/min). The time spent by glucose inside the tubules before entering the bladder compartment varied between 1.66±0.73 min and 2.45±1.01 min.The PTR model proved much better than RTH at fitting observations, by correctly reproducing the delay of variations of glycosuria with respect to the driving glycemia, and by predicting non-zero urinary glucose elimination at low glycemias. This model is useful when studying both transients and steady-state glucose elimination as well as in assessing drug-related changes in renal glucose excretion.     

An Optimized Method for Manufacturing a Clinical Scale Dendritic Cell-Based Vaccine for the Treatment of Glioblastoma

Immune-based treatments represent a promising new class of therapy designed to boost the immune system to specifically eradicate malignant cells. Immunotherapy may generate specific anti-tumor immune responses, and dendritic cells (DC), professional antigen-presenting cells, are widely used in experimental cancer immunotherapy. Several reports describe methods for the generation of mature, antigen-pulsed DC for clinical use. Improved quality and standardization are desirable to obtain GMP-compliant protocols. In this study we describe the generation of DC from 31 Glioblastoma (GB) patients starting from their monocytes isolated by immunomagnetic CD14 selection using the CliniMACS® device. Upon differentiation of CD14+ with IL-4 and GM-CSF, DC were induced to maturation with TNF-α, PGE₂, IL-1β, and IL-6. Whole tumor lysate was obtained, for the first time, in a closed system using the semi-automated dissociator GentleMACS®. The yield of proteins improved by 130% compared to the manual dissociation method. Interestingly the Mean Fluorescence Intensity for CD83 increased significantly in DC pulsed with “new method” lysate compared to DC pulsed with “classical method” lysate. Our results indicate that immunomagnetic isolation of CD14⁺ monocytes using the CliniMACS® device and their pulsing with whole tumor lysate proteins is a suitable method for clinical-scale generation of high quality, functional DC under GMP-grade conditions.     

A Simple,Realistic Stochastic Model of Gastric Emptying

Several models of Gastric Emptying (GE) have been employed in the past to represent the rate of delivery of stomach contents to the duodenum and jejunum. These models have all used a deterministic form (algebraic equations or ordinary differential equations), considering GE as a continuous, smooth process in time. However, GE is known to occur as a sequence of spurts, irregular both in size and in timing. Hence, we formulate a simple stochastic process model, able to represent the irregular decrements of gastric contents after a meal. The model is calibrated on existing literature data and provides consistent predictions of the observed variability in the emptying trajectories. This approach may be useful in metabolic modeling, since it describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.