Confirmation that variants in TTI2 are responsible for autosomal recessive intellectual disability

TTI2 (MIM 614126) has been described as responsible for autosomal recessive intellectual disability (ID; MRT39, MIM:615541) in only two inbred families. Here, we give an account of two individuals from two unrelated outbred families harbouring compound heterozygous TTI2 pathogenic variants. Together with severe ID, progressive microcephaly, scoliosis and sleeping disorder are the most striking features in the two individuals concerned. TTI2, together with TTI1 and TELO2, encode proteins that constitute the triple T heterotrimeric complex. This TTT complex interacts with the HSP90 and R2TP to form a super-complex that has a chaperone function stabilising and maturing a number of kinases, such as ataxia-telangiectasia mutated and mechanistic target of rapamycin, which are key regulators of cell proliferation and genome maintenance. Pathogenic variants in TTI2 logically result in a phenotype close to that caused by TELO2 variants

The imprint of the analogue Hawking effect in subcritical flows

We study the propagation of low-frequency shallow water waves on a one-dimensional flow of varying depth. When taking into account dispersive effects, the linear propagation of long-wavelength modes on uneven bottoms excites new solutions of the dispersion relation which possess a much shorter wavelength. The peculiarity is that one of these new solutions has a negative energy. When the flow becomes supercritical, this mode has been shown to be responsible for the (classical) analog of the Hawking effect. For subcritical flows, the production of this mode has been observed numerically and experimentally, but the precise physics governing the scattering remained unclear. In this work, we provide an analytic treatment of this effect in subcritical flows. We analyze the scattering of low-frequency waves using a new perturbative series, derived from a generalization of the Bremmer series. We show that the production of short-wavelength modes is governed by a complex value of the position: a complex turning point. Using this method, we investigate various flow profiles and derive the main characteristics of the induced spectrum

Rotational superradiant scattering in a vortex flow

When an incident wave scatters off of an obstacle, it is partially reflected and partially transmitted. In theory, if the obstacle is rotating, waves can be amplified in the process, extracting energy from the scatterer. Here we describe in detail the first laboratory detection of this phenomenon, known as superradiance 1, 2, 3, 4. We observed that waves propagating on the surface of water can be amplified after being scattered by a draining vortex. The maximum amplification measured was 14% ± 8%, obtained for 3.70 Hz waves, in a 6.25-cm-deep fluid, consistent with the superradiant scattering caused by rapid rotation. We expect our experimental findings to be relevant to black-hole physics, since shallow water waves scattering on a draining fluid constitute an analogue of a black hole 5, 6, 7, 8, 9, 10, as well as to hydrodynamics, due to the close relation to over-reflection instabilities 11, 12, 13

Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer

PurposeWe examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FAC×6 preoperative chemotherapy. We also performed an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms.Experimental Design273 patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n=138), or FAC × 6 (n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling and treatment response prediction.ResultsThe pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (p<0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% (95%CI:21–56%), the negative predictive value (NPV) 88% (CI:77–95%) and the AUC 0.711. In the FAC arm, the PPV was 9% (CI:1–29%) and the AUC 0.584. This suggests that the genomic predictor may have regimen-specificity. Its performance was similar to a clinical variable-based predictor nomogram.ConclusionsGene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next generation predictive markers will need to be developed separately for different molecular subsets of breast cancers

Population dynamics of methicillin-susceptible and -resistant Staphylococcus aureus in remote communities

Objectives: Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) was first reported in remote regions of Western Australia (WA) in 1992 and is now the predominant MRSA isolated in the State. To gain insights into the emergence of CA-MRSA, 2146 people living in 11 remote WA communities were screened for colonization with S. aureus. Methods: Antibiogram analysis, contour-clamped homogeneous electric field electrophoresis, multilocus sequence typing, Panton-Valentine leucocidin determinant detection and accessory genetic regulator typing were performed to characterize the isolates. MRSA was further characterized by staphylococcal cassette chromosome mec typing. Results: The S. aureus population consisted of 13 clonal complexes and two Singleton lineages together with 56 sporadic isolates. Five lineages contained MRSA; however, these were not the predominant methicillin-susceptible S. aureus (MSSA) lineages. There was greater diversity amongst the MSSA while the MRSA appeared to have emerged clonally following acquisition of the staphylococcal cassette chromosome mec. Three MRSA lineages were considered to have been endemic in the communities and have subsequently become predominant lineages of CA-MRSA in the wider WA community. People colonized with MSSA tended to harbour clones of a different genetic lineage at each anatomical site while people colonized with MRSA tended to harbour clones of the same lineage at each site. Overall, the isolates were resistant to few antimicrobials. Conclusions: Although the evidence suggests that in WA CA-MRSA strains arose in remote communities and have now disseminated into the wider community, there is no evidence that they arose from the predominant MSSA clones in these communities

Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1900548116/-/DCSupplemental.Copyright © 2019 The Author(s). One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.HIST-ERA AdaLab project: The Engineering and Physical Sciences Research Council (EPSRC), UK(EP/M015661/1) ANR-14-CHR2-0001-01

Prediction models for short children born small for gestational age (SGA) covering the total growth phase. Analyses based on data from KIGS (Pfizer International Growth Database)

<p>Abstract</p> <p>Background</p> <p>Mathematical models can be developed to predict growth in short children treated with growth hormone (GH). These models can serve to optimize and individualize treatment in terms of height outcomes and costs. The aims of this study were to compile existing prediction models for short children born SGA (SGA), to develop new models and to validate the algorithms.</p> <p>Methods</p> <p>Existing models to predict height velocity (HV) for the first two and the fourth prepubertal years and during total pubertal growth (TPG) on GH were applied to SGA children from the KIGS (Pfizer International Growth Database) - 1^styear: N = 2340; 2^ndyear: N = 1358; 4^thyear: N = 182; TPG: N = 59. A new prediction model was developed for the 3^rdprepubertal year based upon 317 children by means of the all-possible regression approach, using Mallow's C(p) criterion.</p> <p>Results</p> <p>The comparison between the observed and predicted height velocity showed no significant difference when the existing prediction models were applied to new cohorts. A model for predicting HV during the 3^rdyear explained 33% of the variability with an error SD of 1.0 cm/year. The predictors were (in order of importance): HV previous year; chronological age; weight SDS; mid-parent height SDS and GH dose.</p> <p>Conclusions</p> <p>Models to predict growth to GH from prepubertal years to adult height are available for short children born SGA. The models utilize easily accessible predictors and are accurate. The overall explained variability in SGA is relatively low, due to the heterogeneity of the disorder. The models can be used to provide patients with a realistic expectation of treatment, and may help to identify compliance problems or other underlying causes of treatment failure.</p

The prevalence of enteroviral capsid protein vp1 immunostaining in pancreatic islets in human type 1 diabetes.

addresses: Institute of Biomedical and Clinical Sciences, Peninsula Medical School, Plymouth, UK.The final publication is available at link.springer.com/article/10.1007%2Fs00125-009-1276-0Evidence that the beta cells of human patients with type 1 diabetes can be infected with enterovirus is accumulating, but it remains unclear whether such infections occur at high frequency and are important in the disease process. We have now assessed the prevalence of enteroviral capsid protein vp1 (vp1) staining in a large cohort of autopsy pancreases of recent-onset type 1 diabetic patients and a range of controls

A Semi-Physiologically Based Pharmacokinetic Model Describing the Altered Metabolism of Midazolam Due to Inflammation in Mice

This is the author's accepted manuscript.Purpose To investigate influence of inflammation on metabolism and pharmacokinetics (PK) of midazolam (MDZ) and construct a semi-physiologically based pharmacokinetic (PBPK) model to predict PK in mice with inflammatory disease. Methods Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined. Results The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment. Conclusion The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs