Strontium and Oxygen Isotope Analyses Reveal Late Cretaceous Shark Teeth in Iron Age Strata in the Southern Levant

Skeletal remains in archaeological strata are often assumed to be of similar ages. Here we show that combined Sr and O isotope analyses can serve as a powerful tool for assessing fish provenance and even for identifying fossil fish teeth in archaeological contexts. For this purpose, we established a reference Sr and O isotope dataset of extant fish teeth from major water bodies in the Southern Levant. Fossil shark teeth were identified within Iron Age cultural layers dating to 8–9th century BCE in the City of David, Jerusalem, although the reason for their presence remains unclear. Their enameloid 87Sr/86Sr and δ18OPO4 values [0.7075 ± 0.0001 (1 SD, n = 7) and 19.6 ± 0.9‰ (1 SD, n = 6), respectively], are both much lower than values typical for modern marine sharks from the Mediterranean Sea [0.7092 and 22.5–24.6‰ (n = 2), respectively]. The sharks’ 87Sr/86Sr are also lower than those of rain- and groundwater as well as the main soil types in central Israel (≥0.7079). This indicates that these fossil sharks incorporated Sr (87Sr/86Sr ≈ 0.7075) from a marine habitat with values typical for Late Cretaceous seawater. This scenario is in line with the low shark enameloid δ18OPO4 values reflecting tooth formation in the warm tropical seawater of the Tethys Ocean. Age estimates using 87Sr/86Sr stratigraphy place these fossil shark teeth at around 80-million-years-old. This was further supported by their taxonomy and the high dentine apatite crystallinity, low organic carbon, high U and Nd contents, characteristics that are typical for fossil specimens, and different from those of archaeological Gilthead seabream (Sparus aurata) teeth from the same cultural layers and another Chalcolithic site (Gilat). Chalcolithic and Iron Age seabream enameloid has seawater-like 87Sr/86Sr of 0.7091 ± 0.0001 (1 SD, n = 6), as expected for modern marine fish. Fossil shark and archaeological Gilthead seabream teeth both preserve original, distinct enameloid 87Sr/86Sr and δ18OPO4 signatures reflecting their different aquatic habitats. Fifty percent of the analysed Gilthead seabream teeth derive from hypersaline seawater, indicating that these seabreams were exported from the hypersaline Bardawil Lagoon in Sinai (Egypt) to the Southern Levant since the Iron Age period and possibly even earlier

Association of Insulin Resistance With Schizophrenia Polygenic Risk Score and Response to Antipsychotic Treatment

This study examines the association between insulin resistance, schizophrenia polygenic risk, and treatment outcomes in first-episode, antipsychotic-naive patients with schizophrenia.Funding/Support: This work was supported by grants from the Stanley Medical Research Institute (Dr Bahn)

Peripheral lymphocyte signaling pathway deficiencies predict treatment response in first-onset drug-naïve schizophrenia

Despite being a major cause of disability worldwide, the pathophysiology of schizophrenia and molecular basis of treatment response heterogeneity continue to be unresolved. Recent evidence suggests that multiple aspects of pathophysiology, including genetic risk factors, converge on key cell signaling pathways and that exploration of peripheral blood cells might represent a practical window into cell signaling alterations in the disease state. We employed multiplexed phospho-specific flow cytometry to examine cell signaling epitope expression in peripheral blood mononuclear cell (PBMC) subtypes in drug-naïve schizophrenia patients (n = 49) relative to controls (n = 61) and relate these changes to serum immune response proteins, schizophrenia polygenic risk scores and clinical effects of treatment, including drug response and side effects, over the longitudinal course of antipsychotic treatment. This revealed both previously characterized (Akt1) and novel cell signaling epitopes (IRF-7 (pS477/pS479), CrkL (pY207), Stat3 (pS727), Stat3 (pY705) and Stat5 (pY694)) across PBMC subtypes which were associated with schizophrenia at disease onset, and correlated with type I interferon-related serum molecules CD40 and CXCL11. Alterations in Akt1 and IRF-7 (pS477/pS479) were additionally associated with polygenic risk of schizophrenia. Finally, changes in Akt1, IRF-7 (pS477/pS479) and Stat3 (pS727) predicted development of metabolic and cardiovascular side effects following antipsychotic treatment, while IRF-7 (pS477/pS479) and Stat3 (pS727) predicted early improvements in general psychopathology scores measured using the Brief Psychiatric Rating Scale (BPRS). These findings suggest that peripheral blood cells can provide an accessible surrogate model for intracellular signaling alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic and cardiovascular side effects following antipsychotic therapy

Dietary soy and meat proteins induce distinct physiological and gene expression changes in rats

This study reports on a comprehensive comparison of the effects of soy and meat proteins given at the recommended level on physiological markers of metabolic syndrome and the hepatic transcriptome. Male rats were fed semi-synthetic diets for 1 wk that differed only regarding protein source, with casein serving as reference. Body weight gain and adipose tissue mass were significantly reduced by soy but not meat proteins. The insulin resistance index was improved by soy, and to a lesser extent by meat proteins. Liver triacylglycerol contents were reduced by both protein sources, which coincided with increased plasma triacylglycerol concentrations. Both soy and meat proteins changed plasma amino acid patterns. The expression of 1571 and 1369 genes were altered by soy and meat proteins respectively. Functional classification revealed that lipid, energy and amino acid metabolic pathways, as well as insulin signaling pathways were regulated differently by soy and meat proteins. Several transcriptional regulators, including NFE2L2, ATF4, Srebf1 and Rictor were identified as potential key upstream regulators. These results suggest that soy and meat proteins induce distinct physiological and gene expression responses in rats and provide novel evidence and suggestions for the health effects of different protein sources in human diets

Drug discovery for psychiatric disorders using high-content single-cell screening of signaling network responses ex vivo

There is a paucity of efficacious new compounds to treat neuropsychiatric disorders. We present a novel approach to neuropsychiatric drug discovery based on high-content characterization of druggable signaling network responses at the single-cell level in patient-derived lymphocytes ex vivo. Primary T lymphocytes showed functional responses encompassing neuropsychiatric medications and central nervous system ligands at established (e.g., GSK-3?) and emerging (e.g., CrkL) drug targets. Clinical application of the platform to schizophrenia patients over the course of antipsychotic treatment revealed therapeutic targets within the phospholipase C?1-calcium signaling pathway. Compound library screening against the target phenotype identified subsets of L-type calcium channel blockers and corticosteroids as novel therapeutically relevant drug classes with corresponding activity in neuronal cells. The screening results were validated by predicting in vivo efficacy in an independent schizophrenia cohort. The approach has the potential to discern new drug targets and accelerate drug discovery and personalized medicine for neuropsychiatric conditions

Exploring cellular markers of metabolic syndrome in peripheral blood mononuclear cells across the neuropsychiatric spectrum

Recent evidence suggests that comorbidities between neuropsychiatric conditions and metabolic syndrome may precede and even exacerbate long-term side-effects of psychiatric medication, such as a higher risk of type 2 diabetes and cardiovascular disease, which result in increased mortality. In the present study we compare the expression of key metabolic proteins, including the insulin receptor (CD220), glucose transporter 1 (GLUT1) and fatty acid translocase (CD36), on peripheral blood mononuclear cell subtypes from patients across the neuropsychiatric spectrum, including schizophrenia, bipolar disorder, major depression and autism spectrum conditions (n = 25/condition), relative to typical controls (n = 100). This revealed alterations in the expression of these proteins that were specific to schizophrenia. Further characterization of metabolic alterations in an extended cohort of first-onset antipsychotic drug-naïve schizophrenia patients (n = 58) and controls (n = 63) revealed that the relationship between insulin receptor expression in monocytes and physiological insulin sensitivity was disrupted in schizophrenia and that altered expression of the insulin receptor was associated with whole genome polygenic risk scores for schizophrenia. Finally, longitudinal follow-up of the schizophrenia patients over the course of antipsychotic drug treatment revealed that peripheral metabolic markers predicted changes in psychopathology and the principal side effect of weight gain at clinically relevant time points. These findings suggest that peripheral blood cells can provide an accessible surrogate model for metabolic alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic complications following antipsychotic therapy.This work was supported by grants from the Stanley Medical Research Institute (SMRI); the Engineering and Physical Sciences Research Council UK (EPSRC); the Dutch Government-funded Virgo consortium (ref. FES0908); the Netherlands Genomics Initiative (ref. 050-060-452); the European Union FP7 funding scheme: Marie Curie Actions Industry Academia Partnerships and Pathways (ref. 286334, PSYCH-AID project); SAF2016-76046-R and SAF2013-46292-R (MINECO) and PI16/00156 (isciii and FEDER)

Bullet-Shaped Magnetite Biomineralization Within a Magnetotactic Deltaproteobacterium: Implications for Magnetofossil Identification

Magnetite produced by magnetotactic bacteria (MTB) provides stable paleomagnetic signals because it occurs as natural single‐domain magnetic nanocrystals. MTB can also provide useful paleoenvironmental information because their crystal morphologies are associated with particular bacterial groups and the environments in which they live. However, identification of the fossil remains of MTB (i.e., magnetofossils) from ancient sediments or rocks is challenging because of their generally small sizes and because the growth, morphology, and chain assembly of magnetite within MTB are not well understood. Nanoscale characterization is, therefore, needed to understand magnetite biomineralization and to develop magnetofossils as biogeochemical proxies for paleoenvironmental reconstructions. Using advanced transmission electron microscopy, we investigated magnetite growth and chain arrangements within magnetotactic Deltaproteobacteria strain WYHR‐1, which reveals how the magnetite grows to form elongated, bullet‐shaped nanocrystals. Three crystal growth stages are recognized: (i) initial isotropic growth to produce nearly round ~20 nm particles, (ii) subsequent anisotropic growth along the [001] crystallographic direction to ~75 nm lengths and ~30-40 nm widths, and (iii) unidirectional growth along the [001] direction to ~180 nm lengths, with some growing to ~280 nm. Crystal growth and habit differ from that of magnetite produced by other known MTB strains, which indicates species‐specific biomineralization. These findings suggest that magnetite biomineralization might be much more diverse among MTB than previously thought. When characterized adequately at species level, magnetofossil crystallography, and apomorphic features are, therefore, likely to become useful proxies for ancient MTB taxonomic groups or species and for interpreting the environments in which they lived.This study was supported financially by the National Natural Science Foundation of China (grants no. 41920104009, 41890843, and 41621004), The Senior User Project of RVKEXUE2019GZ06 (Center for Ocean Me Mega‐Science, Chinese Academy of Sciences), and the Australian Research Council (grant DP160100805

A novel approach to the clustering of microarray data via nonparametric density estimation

<p>Abstract</p> <p>Background</p> <p>Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.</p> <p>Results</p> <p>Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.</p> <p>Conclusions</p> <p>The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.</p

Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

Background: Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia, with recent research finding systematic biological differences between antipsychotic resistant and responsive patients. Our aim was to determine whether cognitive impairment at first episode significantly differs between future antipsychotic responders and resistant cases. Methods: Analysis of data from seven international cohorts of first-episode psychosis (FEP) with cognitive data at baseline (N = 683) and follow-up data on antipsychotic treatment response: 605 treatment responsive and 78 treatment resistant cases. Cognitive measures were grouped into seven cognitive domains based on the pre-existing literature. We ran multiple imputation for missing data and used logistic regression to test for associations between cognitive performance at FEP and treatment resistant status at follow-up. Results: On average patients who were future classified as treatment resistant reported poorer performance across most cognitive domains at baseline. Univariate logistic regressions showed that antipsychotic treatment resistance cases had significantly poorer IQ/general cognitive functioning at FEP (OR = 0.70, p = .003). These findings remained significant after adjusting for additional variables in multivariable analyses (OR = 0.76, p = .049). Conclusions: Although replication in larger studies is required, it appears that deficits in IQ/general cognitive functioning at first episode are associated with future treatment resistance. Cognitive variables may be able to provide further insight into neurodevelopmental factors associated with treatment resistance or act as early predictors of treatment resistance, which could allow prompt identification of refractory illness and timely interventions

Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data

<p>Abstract</p> <p>Background</p> <p>Developing the right drugs for the right patients has become a mantra of drug development. In practice, it is very difficult to identify subsets of patients who will respond to a drug under evaluation. Most of the time, no single diagnostic will be available, and more complex decision rules will be required to define a sensitive population, using, for instance, mRNA expression, protein expression or DNA copy number. Moreover, diagnostic development will often begin with in-vitro cell-line data and a high-dimensional exploratory platform, only later to be transferred to a diagnostic assay for use with patient samples. In this manuscript, we present a novel approach to developing robust genomic predictors that are not only capable of generalizing from in-vitro to patient, but are also amenable to clinically validated assays such as qRT-PCR.</p> <p>Methods</p> <p>Using our approach, we constructed a predictor of sensitivity to dacetuzumab, an investigational drug for CD40-expressing malignancies such as lymphoma using genomic measurements of cell lines treated with dacetuzumab. Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor. In this way, we constructed several predictors that we validated on an independent DLBCL patient dataset. Similar analyses were performed on genomic measurements of breast cancer cell lines and patients to construct a predictor of estrogen receptor (ER) status.</p> <p>Results</p> <p>The best dacetuzumab sensitivity predictors involved ten or fewer genes and accurately classified lymphoma patients by their survival and known prognostic subtypes. The best ER status classifiers involved one or two genes and led to accurate ER status predictions more than 85% of the time. The novel method we proposed performed as well or better than other methods evaluated.</p> <p>Conclusions</p> <p>We demonstrated the feasibility of combining feature selection techniques with classification methods to develop assays using cell line genomic measurements that performed well in patient data. In both case studies, we constructed parsimonious models that generalized well from cell lines to patients.</p