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Results for “Clinical Trials”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Complex polymorphisms in endocytosis genes suggest alpha-cyclodextrin against metastases in breast cancer

Most breast cancer deaths are caused by metastasis and treatment options beyond radiation and cytotoxic drugs, which have severe side effects, and hormonal treatments, which are or become ineffective for many patients, are urgently needed. This study reanalyzed existing data from three genome-wide association studies (GWAS) using a novel computational biostatistics approach (muGWAS), which had been validated in studies of 600-2000 subjects in epilepsy and autism. MuGWAS jointly analyzes several neighboring single nucleotide polymorphisms while incorporating knowledge about genetics of heritable diseases into the statistical method and about GWAS into the rules for determining adaptive genome-wide significance.\n\nResults from three independent GWAS of 1000-2000 subjects each, which were made available under the National Institute of Healths \"Up For A Challenge\" (U4C) project, not only confirmed cell-cycle control and receptor/AKT signaling, but, for the first time in breast cancer GWAS, also consistently identified many genes involved in endo-/exocytosis (EEC), most of which had already been observed in functional and expression studies of breast cancer. In particular, the findings include genes that translocate (ATP8A1, ATP8B1, ANO4, ABCA1) and metabolize (AGPAT3, AGPAT4, DGKQ, LPPR1) phospholipids entering the phosphatidylinositol cycle, which controls EEC. These novel findings suggest scavenging phospholipids via alpha-cyclodextrins (CD) as a novel intervention to control local spread of cancer, packaging of exosomes (which prepare distant microenvironment for organ-specific metastases), and endocytosis of {beta}1 integrins (which are required for spread of metastatic phenotype and mesenchymal migration of tumor cells).\n\nBeta-cyclodextrins ({beta}CD) have already been shown to be effective in in vitro and animal studies of breast cancer, but exhibits cholesterol-related ototoxicity. The smaller CDs also scavenges phospholipids, but cannot fit cholesterol. An in-vitro study presented here confirms hydroxypropyl (HP)-CD to be twice as effective as HP{beta}CD against migration of human cells of both receptor negative and estrogen-receptor positive breast cancer.\n\nIf the previous successful animal studies with {beta}CDs are replicated with the safer and more effective CDs, clinical trials of adjuvant treatment with CDs are warranted. Ultimately, all breast cancer are expected to benefit from treatment with HPCD, but women with triplenegative breast cancer (TNBC) will benefit most, because they have fewer treatment options and their cancer advances more aggressively.

cancer biology↗

bioRxiv2017

Sex differences in gene regulation in the dorsal root ganglion after nerve injury

Pain is a subjective experience derived from complex interactions among biological, environmental, and psychosocial pathways. Sex differences in pain sensitivity and chronic pain prevalence are well established. However, the molecular causes underlying these sex dimorphisms are poorly understood particularly with regard to the role of the peripheral nervous system. Here we sought to identify shared and distinct gene networks functioning in the peripheral nervous systems that may contribute to sex differences of pain after nerve injury. We performed RNA-seq on dorsal root ganglia following chronic constriction injury of the sciatic nerve in male and female rats. Analysis from paired naive and injured tissues showed that 1456 genes were differentially expressed between sexes. Appreciating sex-related gene expression differences and similarities in neuropathic pain models may help to improve the translational relevance to clinical populations and efficacy of clinical trials of this major health issue.

neuroscience↗

bioRxiv2017

Drug treatment efficiency depends on the initial state of activation in nonlinear pathways

An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.

systems biology↗

bioRxiv2017

Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

Histomorphology and current grading schemes are unable to predict glioma relapse and malignant tumor progression. We reported that the IDH-mutant associated Glioma-CpG Island Methylator Phenotype (G-CIMP) can be further divided into two clinically distinct subtypes independent of histopathological grading (G-CIMP-high and -low) with evidence of correlation with tumor progression. Here we performed a comprehensive epigenomic analysis of 74 longitudinally collected glioma samples (grade II-IV) to understand malignant recurrence from G-CIMP-high to G-CIMP-low. G-CIMP-low recurrence appeared in 12% of all gliomas and resemble IDH-wildtype primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, stem cell-like epigenomic phenotype, and genomic instability. Finally, we defined a set of candidate biomarker signatures that predict recurrence of G-CIMP-low with clinically relevance on patient outcomes. Our study provides opportunity for refined clinical trial designs and therapeutic targets that limit progression to more aggressive G-CIMP-low phenotype.\n\nHIGHLIGHTSO_LIIndolent G-CIMP-high progresses to aggressive G-CIMP-low phenotype\nC_LIO_LIIncidence of G-CIMP-low recurrent tumors are 3 times greater than G-CIMP-low primary\nC_LIO_LIG-CIMP-low recurrent tumors share epigenomic features with IDH-wildtype primary GBM\nC_LIO_LIPredictive biomarkers of G-CIMP-low progression at primary diagnosis\nC_LI

cancer biology↗

bioRxiv2017

Immune-related genetic enrichment in frontotemporal dementia

BackgroundConverging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed.\n\nMethods and findingsUsing large genome-wide association studies (GWAS) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders namely FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS) - and one or more immune-mediated diseases including Crohns disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis (PSOR). We found up to 270-fold genetic enrichment between FTD and RA and comparable enrichment between FTD and UC, T1D, and CeD. In contrast, we found only modest genetic enrichment between any of the immune-mediated diseases and CBD, PSP or ALS. At a conjunction false discovery rate (FDR) < 0.05, we identified numerous FTD-immune pleiotropic SNPs within the human leukocyte antigen (HLA) region on chromosome 6. By leveraging the immune diseases, we also found novel FTD susceptibility loci within LRRK2 (Leucine Rich Repeat Kinase 2), TBKBP1 (TANK-binding kinase 1 Binding Protein 1), and PGBD5 (PiggyBac Transposable Element Derived 5). Functionally, we found that expression of FTD-immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with frontotemporal dementia and is enriched in microglia compared to other central nervous system (CNS) cell types.\n\nConclusionsWe show considerable immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to risk for FTD. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD.

genetics↗

bioRxiv2017

Predicting serious rare adverse reactions of novel chemicals

Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the worlds economy. Due to the limitations of the animal model, computational prediction of serious, rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing. We present a novel method, based on the theory of \"compressed sensing\", which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased, and incomplete databases, but our data also demonstrates that the accuracy of our approach in predicting a serious adverse reaction (ADR) for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly. Thus, the compressed sensing based computational method reported here represents a major advance in predicting severe rare ADRs, and may facilitate reducing the time and cost of drug discovery and development.

pharmacology and toxicology↗

bioRxiv2017

TraPS-VarI: a python module for the identification of STAT3 modulating germline receptor variants

MotivationHuman individuals differ because of variations in the DNA sequences of all the 46 chromosomes. Information on genetic variations altering the membrane-proximal binding sites for signal transducer of transcription 3 (STAT3) is valuable for understanding the genetic basis of cancer prognosis and disease progression (Ulaganathan et al, 2015). In this regard, non-synonymous coding region mutations resulting in the alteration of protein sequence in the juxtamembrane region of the type I membrane proteins are biologically and clinically relevant. The knowledge of such rare cell line- and individual-specific germline receptor variants is crucial for the investigation of cell-line specific biological mechanisms and genotype-centric therapeutic approaches.\n\nResultsHere we present TraPS-VarI (Transmembrane Protein Sequence Variant Identifier), a python module to rapidly identify human germline receptor variants modulating STAT3 binding sites by using the genetic variation datasets in the variant call format 4.0. For the found protein variants the module also checks for the availability of associated therapeutic agents and ongoing clinical trial studies.\n\nAvailabilityThe Source code and binaries are freely available for download at https://gitlab.com/VJ-Ulaganathan/TraPS-VarI and the documentation can be found at http://traps-vari.readthedocs.io/.\n\nContactulaganat@biochem.mpg.de & ulaganat@icloud.com\n\nSupplementary informationSupplementary data enclosed with the manuscript file.

bioinformatics↗

bioRxiv2017

CXCR4 involvement in neurodegenerative diseases

Neurodegenerative diseases likely share common underlying pathobiology. Although prior work has identified susceptibility loci associated with various dementias, few, if any, studies have systematically evaluated shared genetic risk across several neurodegenerative diseases. Using genome-wide association data from large studies (total n = 82,337 cases and controls), we utilized a previously validated approach to identify genetic overlap and reveal common pathways between progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), Parkinsons disease (PD) and Alzheimers disease (AD). In addition to the MAPT H1 haplotype, we identified a variant near the chemokine receptor CXCR4 that was jointly associated with increased risk for PSP and PD. Using bioinformatics tools, we found strong physical interactions between CXCR4 and four microglia related genes, namely CXCL12, TLR2, RALB and CCR5. Evaluating gene expression from post-mortem brain tissue, we found that expression of CXCR4 and microglial genes functionally related to CXCR4 was dysregulated across a number of neurodegenerative diseases. Furthermore, in a mouse model of tauopathy, expression of CXCR4 and functionally associated genes was significantly altered in regions of the mouse brain that accumulate neurofibrillary tangles most robustly. Beyond MAPT, we show dysregulation of CXCR4 expression in PSP, PD, and FTD brains, and mouse models of tau pathology. Our multi-modal findings suggest that abnormal signaling across a network of microglial genes may contribute to neurodegeneration and may have potential implications for clinical trials targeting immune dysfunction in patients with neurodegenerative diseases.

genetics↗

bioRxiv2017

A Methodology for Evaluating the Performance of Alerting and Detection Algorithms Running on Continuous Patient Data

ObjectivesClinicians in the intensive care unit (ICU) are presented with a large number of physiological data consisting of periodic and frequently sampled measurements, such as heart rate and blood pressure, as well as aperiodic measurements, such as noninvasive blood pressure and laboratory studies. Because this data can be overwhelming, there is considerable interest in designing algorithms that help integrate and interpret this data and assist ICU clinicians in detecting or predicting in advance patients who may be deteriorating. In order to decide whether to deploy such algorithms in a clinical trial, it is important to evaluate these algorithms using retrospective data. However, the fact that these algorithms will be running continuously, i.e., repeatedly sampling incoming patient data, presents some novel challenges for algorithm evaluation. Commonly used measures of performance such as sensitivity and positive predictive value (PPV) are easily applied to static \"snapshots\" of patient data, but can be very misleading when applied to indicators or alerting algorithms that are running on continuous data. Our objective is to create a method for evaluating algorithm performance on retrospective data with the algorithm running continuously throughout the patients stay as it would in a real ICU.\n\nMethodsWe introduce our evaluation methodology in the context of evaluating an algorithm, a Hemodynamic Instability Indicator (HII), for assisting bedside ICU clinicians with the early detection of hemodynamic instability before the onset of acute hypotension. Each patients ICU stay is divided into segments that are labelled as hemodynamically stable or unstable based on clinician interventions typically aimed at treating hemodynamic instability. These segments can be of varying length with varying degrees of exposure to potential alerts, whether true positive or false positive. Furthermore, to simulate how clinicians might interact with the alerting algorithm, we use a dynamic alert supervision mechanism which suppresses subsequent alerts unless the indicator has significantly deteriorated since the prior alert. Under these conditions determining what counts as a positive or negative instance, and calculations of sensitivity, specificity, and positive predictive value can be problematic. We introduce a methodology for consistently counting positive and negative instances. The methodology distinguishes between counts based on alerting events and counts based on sub-segments, and show how they can be applied in calculating measures of performance such as sensitivity, specificity, positive predictive value.\n\nResultsThe introduced methodology is applied to retrospective evaluation of two algorithms, HII and an alerting algorithm based on systolic blood pressure. We use a database, consisting of data from 41,707 patients from 25 US hospitals, to evaluate the algorithms. Both algorithms are evaluated running continuously throughout each patients stay as they would in a real ICU setting. We show how the introduced performance measures differ for different algorithms and for different assumptions.\n\nDiscussionThe standard measures of diagnostic tests in terms of true positives, false positives, etc. are based on certain assumptions which may not apply when used in the context of measuring the performance on an algorithm running continuously, and thus repeatedly sampling from the same patient. When such measures are being reported it is important that the underlying assumptions be made explicit; otherwise, the results can be very misleading.\n\nConclusionWe introduce a methodology for evaluating how an alerting algorithm or indicator will perform running continuously throughout every patients ICU stay, not just for a subset of patients for selected episodes.

bioinformatics↗

bioRxiv2017

Vaccine waning and mumps re-emergence in the United States

Following decades of declining mumps incidence amid widespread vaccination, the United States and other high-income countries have experienced a resurgence in mumps cases over the last decade. Outbreaks affecting vaccinated individuals--and communities with high vaccine coverage--have prompted concerns about the effectiveness of the live attenuated vaccine currently in use: it is unclear if immune protection wanes, or if the vaccine protects inadequately against mumps virus lineages currently circulating. Synthesizing data from epidemiological studies, we estimate that vaccine-derived protection wanes at a timescale of 27 (95%CI: 16 to 51) years. After accounting for this waning, we identify no evidence of changes in vaccine effectiveness over time associated with the emergence of heterologous virus genotypes. Moreover, a mathematical model of mumps transmission validates our findings about the central role of vaccine waning in the re-emergence of cases: outbreaks from 2006 to the present among young adults, and outbreaks occurring in the late 1980s and early 1990s among adolescents, align with peaks in the susceptibility of these age groups attributable to loss of vaccine-derived protection. In contrast, evolution of mumps virus strains escaping pressure would be expected to cause a higher proportion of cases among children. Routine use of a third dose at age 18y, or booster dosing throughout adulthood, may enable mumps elimination and should be assessed in clinical trials.\n\nOne Sentence SummaryThe estimated waning rate of vaccine-conferred immunity against mumps predicts observed changes in the age distribution of mumps cases in the United States since 1967.

epidemiology↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗

bioRxiv2017

Mutational interactions define novel cancer subgroups

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

cancer biology↗

bioRxiv2017

Global determinants of navigation ability

Countries vary in their geographical and cultural properties. Only a few studies have explored how such variations influence how humans navigate or reason about space [1-7]. We predicted that these variations impact human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.

neuroscience↗

bioRxiv2017

Nonlinear dynamical shaping of the fitness landscape of an evolving tumor to combat competitive release

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoners dilemma mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor volume, but subsequently \"release\" the resistant cells to re-populate and re-grow the tumor in a resistant form. Importantly, a model fit of prostate data shows the emergence of a positive fitness cost associated with a majority of patients for each drug, without predetermining a cost in the model a priori. While the specific mechanism associated with this cost may be very different for each of the drugs, a measurable fitness cost emerges in each. The evolutionary model allows us to quantify responses to conventional therapeutic strategies as well as to design adaptive strategies.

cancer biology↗

bioRxiv2017

MULTILOCUS SEQUENCE TYPING REVEALS A UNIQUE CO-DOMINANT POPULATION STRUCTURE OF CRYPTOCOCCUS NEOFORMANS VAR. GRUBII IN VIETNAM

Cryptococcosis is amongst the most important invasive fungal infections globally, with cryptococcal meningitis causing an estimated 180,000 deaths each year in HIV infected patients alone. Patients with other forms of immunosuppression are also at risk, and disease is increasingly recognized in apparently immunocompetent individuals. Cryptococcus neoformans var. grubii (serotype A, molecular type VNI) has a global distribution and is responsible for the majority of cases. Here, we used the consensus ISHAM Multilocus Sequence Typing (MLST) for C. neoformans to define the population structure of clinical isolates of Cryptococcus neoformans var. grubii from Vietnam (n=136) and Laos (n=81). We placed these isolates into the global context using published MLST data from 8 other countries (total N = 669). We observed a phylo-geographical relationship in which Laos was similar to its Southeast Asian neighbor Thailand in being dominated (83%) by Sequence Type (ST) 4 and its Single Locus Variant ST6. On the other hand, Vietnam was uniquely intermediate between Southeast Asia and East Asia having both ST4/ST6 (35%) and ST5 (48%) which causes the majority of cases in East Asia. Analysis of genetic distance (Fst) between different populations of Cryptococcus neoformans var. grubii supported the intermediate nature of the population from Vietnam. A strong association between ST5 and infection in apparently immunocompetent, HIV-uninfected patients was observed in Vietnam (OR: 7.97, [95%CI: 3.18-19.97], p < 0.0001). Our study emphasizes that Vietnam, with its intermediate Cryptococcus neoformans var. grubii population structure, provides the strongest epidemiological evidence of the relationship between ST5 and infection of HIV-uninfected patients. Human population genetic distances within the region suggest these differences in CNVG population across Southeast Asia are driven by ecological factors rather than host factors.\n\nAuthor summaryCryptococcus neoformans is a yeast that causes meningitis in people, usually with damaged immune systems. There are >180,000 deaths in HIV-infected patients each year, most occurring where there are the highest HIV/AIDS disease burdens. Vietnam and Laos have contributed significantly to clinical trials aiming to improve the treatment of cryptococcal meningitis, but the relationship of isolates from these countries to the global population is not yet described. Here, we address this knowledge gap by using Multilocus Sequence Typing to study the population of Cryptococcus neoformans var. grubii (CNVG) in Laos and Vietnam, with the specific aim of incorporating these populations into the wider global context. We found that, in most countries, a single lineage (family) of strains was responsible for most disease. The Vietnamese CNVG population was unusual in that 2 main lineages circulated at the same time. The Vietnamese CNVG population occupies a middle ground between Thailand/Laos in the west and China in the east. The differences in population structure moving from West to East are probably due to ecological differences. Disease in HIV uninfected patients was almost always due to members of a single family of strains (ST5).

molecular biology↗

bioRxiv2017

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCO) offer that standard, and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the \"Open-Stand.org principles for collaborative open standards development\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., FDA), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next generation sequencing workflow exchange, reporting, and regulatory reviews.

scientific communication and education↗

bioRxiv2017

Sepsis: Partial least squares structural equation modelling (PLS-SEM) suggests a critical role for anti-inflammatory responses in clinical severity

BackgorundDespite major advances in medicine, Sepsis remains one of the major killers in critical care wards around the world. For several years it was widely believed that an early pro-inflammatory host response is followed by an overwhelming anti-inflammatory phase. The hypo-inflammatory status, termed as Compensatory anti-inflammatory response syndrome (CARS), was proposed to be the primary cause of sepsis-associated mortality. However, this paradiam changed in recent years since there was little evidence to support the linear model of host response and pathogenesis in sepsis. Currently held view is that both inflammatory and anti-inflammatory host responses are stimulated in an overlapping manner. In this study a robust statistical model to study the complex interplay of host cytokines in human sepsis has been developed to evaluate host responses in sepsis that contribute significantly to clinical pathology.\n\nMethodsTwentyseven cytokines/ chemokines were quantified in 139 sepsis patients and multivariate analysis of variance (MANOVA) was performed to assess differences in host responses in different categories of clinical severity. Partial least squares regression based structural equation modelling (PLS-SEM) was used to assess interactions between different groups of cytokines and their contribution to clinical pathology. An array of 23 cytokines was analysed in a mouse model of endotoxemia and a similar mathematical model was constructed.\n\nResultsThe results of MANOVA demonstrated the ability of combined cytokine response to discriminate sepsis patients according to clinical severity or outcome. Structural equation modelling revealed strong positive association between inflammatory and anti-inflammatory cytokines. In human sepsis, anti-inflammatory cytokines emerged as a significant entity associated with clinical severity as assessed by APACHE II scores.\n\nConclusionPLS-SEM modeling of cytokine responses and APACHE II score in human sepsis revealed that anti-inflammatory molecules contribute significantly towards clinical severity. More critically, the model offers emperical evidence for failures of clinical trials conducted during the last two decades in which antagonists of inflammatory host responses for human Sepsis were used for sepsis. The model also provides credence to the notion that inflammatory and anti-inflammatory host responses occur concurrently in both experimental endotoxemia and in human sepsis.

immunology↗

bioRxiv2017

Plasmodium falciparum infection during pregnancy impairs fetal head growth: prospective and populational-based retrospective studies

BackgroundMalaria in pregnancy is associated with adverse effects on the fetus and newborns. However, the outcome on a newborns head circumference (HC) is still unclear. Here, we show the relation of malaria during pregnancy with fetal head growth.\n\nMethodsClinical and anthropometric data were collected from babies in two cohort studies of malaria-infected and non-infected pregnant women, in the Brazilian Amazon. One enrolled prospectively (PCS, Jan. 2013 to April 2015) through volunteer sampling, and followed until delivery, 600 malaria-infected and non-infected pregnant women. The other assembled retrospectively (RCS, Jan. 2012 to Dec. 2013) clinical and malaria data from 4697 pregnant women selected through population-based sampling. The effects of malaria during pregnancy in the newborns were assessed using a multivariate logistic regression. According with World Health Organization guidelines babies were classified in small head (HC < 1 SD below the median) and microcephaly (HC < 2 SD below the median) using international HC standards.\n\nResultsAnalysis of 251 (PCS) and 232 (RCS) malaria-infected, and 158 (PCS) and 3650 (RCS) non-infected women with clinical data and anthropometric measures of their babies was performed. Among the newborns, 70 (17.1%) in the PCS and 934 (24.1%) in the RCS presented with a small head (SH). Of these, 15 (3.7%) and 161 (4.2%), respectively, showed microcephaly (MC). The prevalence of newborns with a SH (30.7% in PCS and 36.6% in RCS) and MC (8.1% in PCS and 7.3% in RCS) was higher among babies born from women infected with Plasmodium falciparum during pregnancy. Multivariate logistic regression analyses revealed that P. falciparum infection during pregnancy represents a significant increased odds for the occurrence of a SH in newborns (PCS: OR 3.15, 95% CI 1.52-6.53, p=0.002; RCS: OR 1.91, 95% CI 1.21-3.04, p=0.006). Similarly, there is an increased odds of MC in babies born from mothers that were P. falciparum-infected (PCS: OR 5.09, 95% CI 1.12-23.17, p=0.035). Moreover, characterization of placental pathology corroborates the association analysis, particularly through the occurrence of more syncytial nuclear aggregates and inflammatory infiltrates in placentas from babies with the reduced head circumference.\n\nConclusionsThis work indicates that falciparum-malaria during pregnancy presents an increased likelihood of occurring reduction of head circumference in newborns, which is associated with placental malaria.\n\nTrial Registrationregistered as RBR-3yrqfq in the Brazilian Clinical Trials Registry

epidemiology↗