Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female's response to sex peptide.

Sex peptide (SP), a seminal fluid protein of Drosophila melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses including increased egg laying, activity, and food intake and a decreased remating rate. While it is known that SP achieves this, at least in part, by altering neuronal signaling in females, the genetic architecture and temporal dynamics of the female's response to SP remain elusive. We used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 h after mating to learn about the genetic architecture, at the gene and exon levels, of the female's response to SP. We find that SP is not essential to trigger early aspects of a virgin-to-mated transcriptional switch, which includes changes in a metabolic gene regulatory network. However, SP is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that SP alters rhythmic gene expression in females and suggests that SP's disruption of the female's circadian rhythm might be key to its widespread effects.

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy.

Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response.

BACKGROUND: Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. RESULTS: To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. CONCLUSIONS: This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.

Iterative random forests to discover predictive and stable high-order interactions.

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with the same order of computational cost as the RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human-derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, third-order interactions, e.g., between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF rediscovered a central role of H3K36me3 in chromatin-mediated splicing regulation and identified interesting fifth- and sixth-order interactions, indicative of multivalent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens additional avenues of inquiry into the molecular mechanisms underlying genome biology.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

MOTIVATION: Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. RESULTS: Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATION: http://metscape.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer.

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.

Integrative Analysis of Differentially Expressed Genes in Time-Course Multi-Omics Data with MINT-DE.

Background: Time-course multi-omics experiments have been highly informative for obtaining a comprehensive understanding of the dynamic relationships between molecules in a biological process, especially if the different profiles are obtained from the same samples. A fundamental step in analyzing time-course multi-omics data involves selecting a short list of genes or gene regions ("sites") that warrant further study. Two important criteria for site selection are the magnitude of change and the temporal dynamic consistency. However, existing methods only consider one of these criteria, while neglecting the other. Results: In our study, we propose a framework called MINT-DE (Multi-omics INtegration of Time-course for Diffierential Expression analysis) to address this limitation. MINT-DE is capable of selecting sites based on summarized measures of both aforementioned aspects. We calculate evidence measures assessing the extent of differential expression for each assay and for the dynamical similarity across assays. Then based on the summary of the evidence assessment measures, sites are ranked. To evaluate the performance of MINT-DE, we apply it to analyze a time-course multi-omics dataset of Drosophila development. We compare the selection obtained from MINT-DE with those obtained from other existing methods. The analysis reveal that MINT-DE is able to identify differentially expressed time-course pairs with the highest correlations. Their corresponding genes are significantly enriched for known biological functions, as measured by gene-gene interaction networks and the Gene Ontology enrichment. Conclusions: These findings suggest the effectiveness of MINT-DE in selecting sites that are both differentially expressed within at least one assay and temporally related across assays. This highlights the potential of MINT-DE to identify biologically important sites for downstream analysis and provide a complementarity of sites that is neglected by existing methods.

Syndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study.

Background: Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing. Objective: This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively. Methods: We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests. Results: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005). Conclusions: The real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.

Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages.

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is simplest in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples.

Learning Financial Networks with High-frequency Trade Data.

Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, complex dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests, a state-of-the-art machine learning algorithm which offers excellent prediction accuracy without expensive hyperparameter optimization. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure such as the realized volatility of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.

RandomForestsGLS: An R package for Random Forests for dependent data.

With the modern advances in geographical information systems, remote sensing technologies, and low-cost sensors, we are increasingly encountering datasets where we need to account for spatial or serial dependence. Dependent observations (y 1, y 2, …, yn ) with covariates (x1, ..., x n ) can be modeled non-parametrically as yi = m(x i ) + ϵi , where m(x i ) is mean component and ∈i accounts for the dependency in data. We assume that dependence is captured through a covariance function of the correlated stochastic process ∈i (second order dependence). The correlation is typically a function of "spatial distance" or "time-lag" between two observations. Unlike linear regression, non-linear Machine Learning (ML) methods for estimating the regression function m can capture complex interactions among the variables. However, they often fail to account for the dependence structure, resulting in sub-optimal estimation. On the other hand, specialized software for spatial/temporal data properly models data correlation but lacks flexibility in modeling the mean function m by only focusing on linear models. RandomForestsGLS bridges the gap through a novel rendition of Random Forests (RF) - namely, RF-GLS - by explicitly modeling the spatial/serial data correlation in the RF fitting procedure to substantially improve the estimation of the mean function. Additionally, RandomForestsGLS leverages kriging to perform predictions at new locations for geo-spatial data.

F-cell levels are altered with erythrocyte density in sickle cell disease.

Lighter cells from density fractionated erythrocytes of sickle cell disease (SCD) patients carry higher amount of externalized phosphatidylserine (PS) and cell surface glycophorins compared to the denser counterparts. Further analysis also revealed that the denser cells contained higher levels of fetal hemoglobin (HbF) compared to the lighter cells, supported by the presence of larger number of F-cells in these populations. In this report, we have found direct evidence on the higher survival of the HbF rich erythrocytes in SCD.

Eryptosis in hereditary spherocytosis and thalassemia: role of glycoconjugates.

The present work is aimed to study the mechanism of faster erythrocyte clearance in hereditary spherocytosis (HS), a heterogeneous disorders characterized by alterations in the proteins of the red cell membrane skeleton along with different kinds of thalassemia. The maximum exposure of phosphatidylserine (PS) is found in HS compared to those in both α- and ß-thalassemia. Interestingly, in HS more PS exposed cells were found in younger erythrocytes compared to normal and the thalassemics where aged cells showed higher loss of PS asymmetry. Loss of sialic acid and GlcNAc bearing glycoconjugates, presumably the glycophorins, was also found upon aging. The loss of PS asymmetry together with the cell surface glycoproteins mediated by membrane vesiculation, seemed to play key role in early clearance of erythrocytes from circulation following a mechanism similar to HbEß-thalassemia.

Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism.

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.

Methionine-Homocysteine Pathway in African-American Prostate Cancer.

African American (AA) men have a 60% higher incidence and two times greater risk of dying of prostate cancer (PCa) than European American men, yet there is limited insight into the molecular mechanisms driving this difference. To our knowledge, metabolic alterations, a cancer-associated hallmark, have not been reported in AA PCa, despite their importance in tumor biology. Therefore, we measured 190 metabolites across ancestry-verified AA PCa/benign adjacent tissue pairs (n = 33 each) and identified alterations in the methionine-homocysteine pathway utilizing two-sided statistical tests for all comparisons. Consistent with this finding, methionine and homocysteine were elevated in plasma from AA PCa patients using case-control (AA PCa vs AA control, methionine: P = .0007 and homocysteine: P < .0001), biopsy cohorts (AA biopsy positive vs AA biopsy negative, methionine: P = .0002 and homocysteine: P < .0001), and race assignments based on either self-report (AA PCa vs European American PCa, methionine: P = .001, homocysteine: P < .0001) or West African ancestry (upper tertile vs middle tertile, homocysteine: P < .0001; upper tertile vs low tertile, homocysteine: P = .002). These findings demonstrate reprogrammed metabolism in AA PCa patients and provide a potential biological basis for PCa disparities.

Loss of phospholipid membrane asymmetry and sialylated glycoconjugates from erythrocyte surface in haemoglobin E beta-thalassaemia.

This study aimed to investigate any correlation between the extent of phosphatidylserine (PS) asymmetry and sialylated glycoconjugate levels with the faster clearance of circulating erythrocytes in haemoglobin E (HbE) beta-thalassaemia. Erythrocytes from peripheral blood samples of different HbEbeta-thalassaemia patients showed loss of PS asymmetry measured by annexin V binding using flow cytometry. Maximum PS exposure was found when HbE was 50-60% and HbF was <20% indicating a possible correlation with severity of the disease. Separation of erythrocytes into aged and younger cells showed higher loss of PS asymmetry in the younger erythrocytes of HbEbeta-thalassaemia patients when compared with normal blood, where PS asymmetry was lost only in the older cells. Sialylated glycoconjugate measurement using the lectins wheatgerm agglutinin and pokeweed mitogen showed loss of sialic acid and N-acetyl-D-glucosamine-bearing glycoproteins in the order normal

Porous red cell ultrastructure and loss of membrane asymmetry in a novel case of hemolytic anemia.

Transmission electron microscopic study revealed large pores on the erythrocyte ghost membranes, disrupted cytoskeleton and microcytosis of circulating erythrocytes in a novel case of hemolytic anemia. Greater loss of phosphatidylserine (PS) asymmetry was observed in younger erythrocytes compared with the aged ones in contrast to the normal red cells. Levels of sialylated glycoconjugates, such as glycophorin, measured by the binding of wheat germ agglutinin, showed greater loss upon aging. Such drastic loss of PS asymmetry leads to faster eryptosis, mediated by shedding of glycophorin-containing microvesicles leaving highly PS-exposed erythrocytes accessible to the phagocytes.

Network Granger Causality with Inherent Grouping Structure.

The problem of estimating high-dimensional network models arises naturally in the analysis of many biological and socio-economic systems. In this work, we aim to learn a network structure from temporal panel data, employing the framework of Granger causal models under the assumptions of sparsity of its edges and inherent grouping structure among its nodes. To that end, we introduce a group lasso regression regularization framework, and also examine a thresholded variant to address the issue of group misspecification. Further, the norm consistency and variable selection consistency of the estimates are established, the latter under the novel concept of direction consistency. The performance of the proposed methodology is assessed through an extensive set of simulation studies and comparisons with existing techniques. The study is illustrated on two motivating examples coming from functional genomics and financial econometrics.

Differential expression of red cell proteins in hemoglobinopathy.

Red blood cell proteome has not been studied well until recently, as the large abundance of hemoglobin posed challenge to the detection of other cytosolic proteins in the linear dynamic range. However, in the last couple of years, due to emergence of various novel hemoglobin depletion strategies and more state-of-the-art detection techniques, a number of works on erythrocyte proteome have appeared in the literature. As a result, we now have much deeper information about both the membrane as well as the cytosolic proteins of erythrocytes. In this review, we have discussed the role of red cell proteome on the two most well-studied hemoglobin disorders, sickle cell disease and thalassemia, emphasizing on the differential expression of the redox regulator proteins and chaperones, in particular. We have also touched upon the importance of the association of the varying levels of hemoglobin variants, particularly HbE on the clinical manifestation of composite diseases like HbEß thalassemia.

Erythrocyte membrane defects and asymmetry in paroxysmal nocturnal hemoglobinuria and myelodysplastic syndrome.

Paroxysmal nocturnal hemoglobinuria (PNH) and myelodysplastic syndromes (MDS) are clonal disorder of haematopoietic stem cells that may eventually lead to chronic anemia. The ultrastructural defects in erythrocyte membranes may have a role in early red cell destruction within circulation. The lifespan of the erythrocyte primarily correlates to externalization of phosphatidylserine (PS) and loss of glycophorins from the erythrocyte surface. The span of survival of mature erythrocytes in the circulation in case of MDS and PNH is yet unclear and has been studied by measuring simultaneous exposure of PS and loss of glycoconjugates, primarily glycophorins from membrane surface. The extent of the loss of PS asymmetry and cell surface glycophorins in density separated erythrocytes of six MDS and three PNH patients has been probed by fluorochrome conjugated annexin V and wheat germ agglutinin using flow cytometry. The cells with lighter density showed a higher amount of PS on the outer surface compared to those of heavier cells in all PNH and MDS cases, showing the opposite trend to that observed in normal erythrocytes. In addition, the lighter cells had more cell surface glycophorins compared to heavier cells in all the cases. Such lowering of glycophorin levels from the lighter to heavier cells was maximum in refractory anaemia (RA) and minimum in the normal cells studied. Greater loss of PS asymmetry and cell surface glycophorin in the lighter or younger erythrocytes together could be responsible for their faster destruction and removal (eryptosis) in PNH and MDS.