Identification of blood-based molecular signatures for prediction of response and relapse in schizophrenia patients

The current inability of psychiatric medicine to objectively select the most appropriate treatment or to predict imminent relapse are major factors contributing to the severity and clinical burden of schizophrenia. We have previously used multiplexed immunoassays to show that schizophrenia patients have a distinctive molecular signature in serum compared with healthy control subjects. In the present study, we used the same approach to measure biomarkers in a population of 77 schizophrenia patients who were followed up over 25 months with four aims: (1) to identify molecules associated with symptom severity in antipsychotic naive and unmedicated patients, (2) to determine biomarker signatures that could predict response over a 6-week treatment period, (3) to identify molecular panels that could predict the time to relapse in a cross-sectional population of patients in remission and (4) to investigate how the biological relapse signature changed throughout the treatment course. This led to identification of molecular signatures that could predict symptom improvement over the first 6 weeks of treatment as well as predict time to relapse in a subset of 18 patients who experienced recurrence of symptoms. This study provides the groundwork for the development of novel objective clinical tests that can help psychiatrists in the clinical management of schizophrenia

A polygenic burden of rare disruptive mutations in schizophrenia.

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease

Interpreting the role of de novo protein-coding mutations in neuropsychiatric disease

Pedigree, linkage and association studies are consistent with heritable variation for complex disease due to the segregation of genetic factors in families and in the population. In contrast, de novo mutations make only minor contributions to heritability estimates for complex traits. Nonetheless, some de novo variants are known to be important in disease etiology. The identification of risk-conferring de novo variants will contribute to the discovery of etiologically relevant genes and pathways and may help in genetic counseling. There is considerable interest in the role of such mutations in complex neuropsychiatric disease, largely driven by new genotyping and sequencing technologies. An important role for large de novo copy number variations has been established. Recently, whole-exome sequencing has been used to extend the investigation of de novo variation to point mutations in protein-coding regions. Here, we consider several challenges for the interpretation of such mutations in the context of their role in neuropsychiatric disease

Comparative efficacy of ultrasound-guided and stimulating popliteal-sciatic perineural catheters for postoperative analgesia

Perineural catheter insertion using ultrasound guidance alone is a relatively new approach. Previous studies have shown that ultrasound-guided catheters take less time to place with high placement success rates, but the analgesic efficacy compared with the established stimulating catheter technique remains unknown. We tested the hypothesis that popliteal-sciatic perineural catheter insertion relying exclusively on ultrasound guidance results in superior postoperative analgesia compared with stimulating catheters. Preoperatively, subjects receiving a popliteal-sciatic perineural catheter for foot or ankle surgery were assigned randomly to either ultrasound guidance (bolus via needle with non-stimulating catheter insertion) or electrical stimulation (bolus via catheter). We used 1.5% mepivacaine 40 mL for the primary surgical nerve block and 0.2% ropivacaine (basal 8 mL·hr−1; bolus 4 mL; 30 min lockout) was infused postoperatively. The primary outcome was average surgical pain on postoperative day one. Forty of the 80 subjects enrolled were randomized to each treatment group. One of 40 subjects (2.5%) in the ultrasound group failed catheter placement per protocol vs nine of 40 (22.5%) in the stimulating catheter group (P = 0.014). The difference in procedural duration (mean [95% confidence interval (CI)]) was −6.48 (−9.90 - −3.05) min, with ultrasound requiring 7.0 (4.0-14.1) min vs stimulation requiring 11.0 (5.0-30.0) min (P < 0.001). The average pain scores of subjects who provided data on postoperative day one were somewhat higher for the 33 ultrasound subjects than for the 26 stimulation subjects (5.0 [1.0-7.8] vs 3.0 [0.0-6.5], respectively; P = 0.032), a difference (mean [95%CI]) of 1.37 (0.03-2.71). For popliteal-sciatic perineural catheters, ultrasound guidance takes less time and results in fewer placement failures compared with stimulating catheters. However, analgesia may be mildly improved with successfully placed stimulating catheters. Clinical trial registration number NCT00876681

Deterministic diffusion fiber tracking improved by quantitative anisotropy

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T 1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics. © 2013 Yeh et al

Macrophage Migration Inhibitory Factor Antagonist Blocks the Development of Endometriosis In Vivo

Endometriosis, a disease of reproductive age women, is a major cause of infertility, menstrual disorders and pelvic pain. Little is known about its etiopathology, but chronic pelvic inflammation is a common feature in affected women. Beside symptomatic treatment of endometriosis-associated pain, only two main suboptimal therapeutic approaches (hormonal and invasive surgery) are generally recommended to patients and no specific targeted treatment is available. Our studies led to the detection of a marked increase in the expression of macrophage migration inhibitory factor (MIF) in the eutopic endometrium, the peripheral blood and the peritoneal fluid of women with endometriosis, and in early, vascularized and active endometriotic lesions. Herein, we developed a treatment model of endometriosis, where human endometrial tissue was first allowed to implant into the peritoneal cavity of nude mice, to assess in vivo the effect of a specific antagonist of MIF (ISO-1) on the progression of endometriosis and evaluate its efficacy as a potential therapeutic tool. Administration of ISO-1 led to a significant decline of the number, size and in situ dissemination of endometriotic lesions. We further showed that ISO-1 may act by significantly inhibiting cell adhesion, tissue remodeling, angiogenesis and inflammation as well as by altering the balance of pro- and anti-apoptotic factors. Actually, mice treatment with ISO-1 significantly reduced the expression of cell adhesion receptors αv and ß3 integrins (P<0.05), matrix metalloproteinases (MMP) 2 and 9 (P<0.05), vascular endothelial cell growth factor (VEGF) (P<0.01), interleukin 8 (IL8) (P<0.05), cyclooxygenease (COX)2 (P<0.001) and the anti-apoptotic protein Bcl2 (P<0.01), but significantly induced the expression of Bax (P<0.05), a potent pro-apoptotic protein. These data provide evidence that specific inhibition of MIF alters endometriotic tissue growth and progression in vivo and may represent a promising potential therapeutic avenue

The venom composition of the parasitic wasp Chelonus inanitus resolved by combined expressed sequence tags analysis and proteomic approach

<p>Abstract</p> <p>Background</p> <p>Parasitic wasps constitute one of the largest group of venomous animals. Although some physiological effects of their venoms are well documented, relatively little is known at the molecular level on the protein composition of these secretions. To identify the majority of the venom proteins of the endoparasitoid wasp <it>Chelonus inanitus </it>(Hymenoptera: Braconidae), we have randomly sequenced 2111 expressed sequence tags (ESTs) from a cDNA library of venom gland. In parallel, proteins from pure venom were separated by gel electrophoresis and individually submitted to a nano-LC-MS/MS analysis allowing comparison of peptides and ESTs sequences.</p> <p>Results</p> <p>About 60% of sequenced ESTs encoded proteins whose presence in venom was attested by mass spectrometry. Most of the remaining ESTs corresponded to gene products likely involved in the transcriptional and translational machinery of venom gland cells. In addition, a small number of transcripts were found to encode proteins that share sequence similarity with well-known venom constituents of social hymenopteran species, such as hyaluronidase-like proteins and an Allergen-5 protein.</p> <p>An overall number of 29 venom proteins could be identified through the combination of ESTs sequencing and proteomic analyses. The most highly redundant set of ESTs encoded a protein that shared sequence similarity with a venom protein of unknown function potentially specific of the <it>Chelonus </it>lineage. Venom components specific to <it>C. inanitus </it>included a C-type lectin domain containing protein, a chemosensory protein-like protein, a protein related to yellow-e3 and ten new proteins which shared no significant sequence similarity with known sequences. In addition, several venom proteins potentially able to interact with chitin were also identified including a chitinase, an imaginal disc growth factor-like protein and two putative mucin-like peritrophins.</p> <p>Conclusions</p> <p>The use of the combined approaches has allowed to discriminate between cellular and truly venom proteins. The venom of <it>C. inanitus </it>appears as a mixture of conserved venom components and of potentially lineage-specific proteins. These new molecular data enrich our knowledge on parasitoid venoms and more generally, might contribute to a better understanding of the evolution and functional diversity of venom proteins within Hymenoptera.</p

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Following the genes: a framework for animal modeling of psychiatric disorders

The number of individual cases of psychiatric disorders that can be ascribed to identified, rare, single mutations is increasing with great rapidity. Such mutations can be recapitulated in mice to generate animal models with direct etiological validity. Defining the underlying pathogenic mechanisms will require an experimental and theoretical framework to make the links from mutation to altered behavior in an animal or psychopathology in a human. Here, we discuss key elements of such a framework, including cell type-based phenotyping, developmental trajectories, linking circuit properties at micro and macro scales and definition of neurobiological phenotypes that are directly translatable to humans