Search

Incident light is a central modulator of plant growth and development. However, there are still open questions surrounding wavelength-specific plant proteomic responses. Here we applied tandem mass tag based quantitative proteomics technology to acquire an in-depth view of proteome changes in Arabidopsis thaliana response to narrow wavelength blue (B; 450 nm), amber (A; 595 nm), and red (R; 650 nm) light treatments. A total of 16,707 proteins were identified with 9120 proteins quantified across all three light treatments in three biological replicates. This enabled examination of changes in the abundance for proteins with low abundance and important regulatory roles including transcription factors and hormone signaling. Importantly, 18% (1631 proteins) of the A. thaliana proteome is differentially abundant in response to narrow wavelength lights, and changes in proteome correlate well with different morphologies exhibited by plants. To showcase the usefulness of this resource, data were placed in the context of more than thirty published datasets, providing orthogonal validation and further insights into light-specific biological pathways, including Systemic Acquired Resistance and Shade Avoidance Syndrome. This high-resolution resource for A. thaliana provides baseline data and a tool for defining molecular mechanisms that control fundamental aspects of plant response to changing light conditions, with implications in plant development and adaptation. SIGNIFICANCE: Understanding of molecular mechanisms involved in wavelength-specific response of plant is question of widespread interest both to basic researchers and to those interested in applying such knowledge to the engineering of novel proteins, as well as targeted lighting systems. Here we sought to generate a high-resolution proteomic profile of plant leaves, based on exposure to specific narrow-wavelength lights. Although changes in plant physiology in response to light spectral composition is well documented, there is limited knowledge on the roles of specific light wavelengths and their impact. Most previous studies have utilized relatively broad wavebands in their experiments. Such multi-wavelengths lights trigger diverse and complex signaling networks that pose major challenges in inference of wavelength-specific molecular processes that underly the plant response. Moreover, most studies have compared the effect of blue and red wavelengths comparing with FL, as control. As FL light consists the mixed spectra composition of both red and blue as well as numerous other wavelengths, comparing undeniably results in inconsistent and overlapping responses that will hamper effects to elucidate the plant response to specific wavelengths [1, 2]. Monitoring plant proteome response to specific wavelengths and further contrasting the changes with one another, rather than comparing plants proteome to FL, is thus necessary to gain detailed insights on underlying biological pathways and their consequences in plant physiology. Here, we employed narrow wavelength LED lights in our design to eliminate a potential overlap in molecular responses by ensuring non-overlapping wavelengths in the light treatments. We further applied TMT-labeling technology to gain a high-resolution view on the proteome changes. Our proteomics data provides an in-depth coverage suitable for system-wide analyses, providing deep insights on plant molecular response particularly because of the tremendous increase in the coverage of identified proteins which outreach the other biological data.

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85–89% and AUC-PR scores of 84–92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

ImportanceASD diagnosis remains behavior-based and the median age of the first diagnosis remains unchanged at ∼52 months, which is nearly 5 years after its first trimester origin. Long delays between ASD’s prenatal onset and eventual diagnosis likely is a missed opportunity. However, accurate and clinically-translatable early-age diagnostic methods do not exist due to ASD genetic and clinical heterogeneity. There is a need for early-age diagnostic biomarkers of ASD that is robust against its heterogeneity.ObjectiveTo develop a single blood-based molecular classifier that accurately diagnoses ASD at the age of first symptoms.Design, Setting, and ParticipantsN=264 ASD, typically developing (TD), and language delayed (LD) toddlers with their clinical, diagnostic, and leukocyte RNA data collected. Datasets included Discovery (n=175 ASD, TD subjects), Longitudinal (n=33 ASD, TD subjects), and Replication (n=89 ASD, TD, LD subjects). We developed an ensemble of ASD classifiers by testing 42,840 models composed of 3,570 feature selection sets and 12 classification methods. Models were trained on the Discovery dataset with 5-fold cross validation. Results were used to construct a Bayesian model averaging-based (BMA) ensemble classifier model that was tested in Discovery and Replication datasets. Data were collected from 2007 to 2012 and analyzed from August 2019 to April 2021.Main Outcomes and MeasuresPrimary outcomes were (1) comparisons of the performance of 42,840 classifier models in correctly identifying ASD vs TD and LD in Discovery and Replication datasets; and (2) performance of the ensemble model composed of 1,076 models and weighted by Bayesian model averaging technique.ResultsOf 42,840 models trained in the Discovery dataset, 1,076 averaged AUC-ROC>0.8. These 1,076 models used 191 different feature routes and 2,764 gene features. Using weighted BMA of these features and routes, an ensemble classifier model was constructed which demonstrated excellent performance in Discovery and Replication datasets with ASD classification AUC-ROC scores of 84% to 88%. ASD classification accuracy was comparable against LD and TD subjects and in the Longitudinal dataset. ASD toddlers with ensemble scores above and below the ASD ensemble mean had similar diagnostic and psychometric scores, but those below the ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble features include genes with immune/inflammation, response to cytokines, transcriptional regulation, mitotic cell cycle, and PI3K-AKT, RAS, and Wnt signaling pathways.Conclusions and RelevanceAn ensemble ASD molecular classifier has high and replicable accuracy across the spectrum of ASD clinical characteristics and across toddlers aged 1 to 4 years, which has potential for clinical translation.Key PointsQuestionSince ASD is genetically and clinical heterogeneous, can a single blood-based molecular classifier accurately diagnose ASD at the age of first symptoms?FindingsTo address heterogeneity, we developed an ASD classifier method testing 42,840 models. An ensemble of 1,076 models using 191 different feature routes and 2,764 gene features, weighted by Bayesian model averaging, demonstrated excellent performance in Discovery and Replication datasets producing ASD classification with the area under the receiver operating characteristic curve (AUC-ROC) scores of 84% to 88%. Features include genes with immune/inflammation, response to cytokines, transcriptional regulation, mitotic cell cycle, and PI3K-AKT, RAS and Wnt signaling pathways.MeaningAn ensemble gene expression ASD classifier has high accuracy across the spectrum of ASD clinical characteristics and across toddlers aged 1 to 4 years.

Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1-4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.

Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep learning methods now allow one to encode this rich existing knowledge in the architecture of the learning procedure, thus providing the models with the knowledge that is absent in the training data. By encoding biological networks in the architecture, one can develop flexible deep models that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility in the architecture can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an ultimate impact on cell phenotype. Furthermore, such models could require fewer training samples, are more generalizable across diverse biological contexts, and can make predictions that are more consistent with the current understanding on the inner-working of biological systems.

Background Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging. Results Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3′ shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation. Conclusion The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. Graphical abstract

Objectives To examine the impact of a new approach, Get SET Early, on the rates of early autism spectrum disorder (ASD) detection and factors that influence the screen-evaluate-treat chain. Study design After attending Get SET Early training, 203 pediatricians administered 57 603 total screens using the Communication and Symbolic Behavior Scales Infant-Toddler Checklist at 12-, 18-, and 24-month well-baby examinations, and parents designated presence or absence of concern. For screen-positive toddlers, pediatricians specified if the child was being referred for evaluation, and if not, why not. Results Collapsed across ages, toddlers were evaluated and referred for treatment at a median age of 19 months, and those screened at 12 months (59.4% of sample) by 15 months. Pediatricians referred one-third of screen-positive toddlers for evaluation, citing lack of confidence in the accuracy of screen-positive results as the primary reason for nonreferral. If a parent expressed concerns, referral probability doubled, and the rate of an ASD diagnosis increased by 37%. Of 897 toddlers evaluated, almost one-half were diagnosed as ASD, translating into an ASD prevalence of 1%. Conclusions The Get SET Early model was effective at detecting ASD and initiating very early treatment. Results also underscored the need for change in early identification approaches to formally operationalize and incorporate pediatrician judgment and level of parent concern into the process.

Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., < 24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predicts 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predicts rate of skill growth, accounting for 13% of the variance treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism post-mortem cortical tissue, and are normatively highly expressed in variety of subcortical and cortical areas important for social-communication and language development. This work indicates for the first time that gene expression can predict the rate of early intervention response and that a key biological factor linked to treatment outcome could be the susceptibility for epigenetic change via mechanisms such as histone acetylation.

Cortical regional identities develop through anterior-posterior (A-P) and dorsal-ventral (D-V) prenatal genomic patterning gradients. Here we find that A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT) is intact in typically developing and autistic toddlers with good language outcome, but is absent in autistic toddlers with poor early language outcome. Genes driving this effect are prominent in midgestational A-P and D-V gene expression gradients and prenatal cell types driving SA and CT variation (e.g., progenitor cells versus excitatory neurons). These genes are also important for vocal learning, human-specific evolution, and prenatal co-expression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be linked to atypical genomic cortical patterning starting in prenatal periods and which impacts later development of regional functional specialization and circuit formation.One Sentence SummaryGenomic patterning of the cortex is atypical in autistic toddlers with poor early language outcome.

Numerous genes are associated with autism spectrum disorder (ASD); however, it remains unclear how most ASD risk genes influence neurodevelopment and result in similar traits. Recent genetic models of complex traits suggest non-tissue-specific genes converge on core disease genes; so we analyzed ASD genetics in this context. We found ASD risk genes partition cleanly into broadly-expressed and brain-specific genes. The two groups show sequential roles during neurodevelopment with broadly-expressed genes modulating chromatin remodeling, proliferation, and cell fate, while brain-specific risk genes are involved in neural maturation and synapse functioning. Broadly-expressed risk genes converge onto brain-specific risk genes and core neurodevelopmental genes through regulatory networks including PI3K/AKT, RAS/ERK, and WNT/β-catenin signaling pathways. Broadly-expressed and brain-specific risk genes show unique properties, wherein the broadly-expressed risk gene network is expressed prenatally and conserved in non-neuronal cells like microglia. However, the brain-specific gene network expression is limited to excitatory and inhibitory neurons, spanning prenatal to adulthood. Furthermore, the two groups are linked differently to comorbidities associated with ASD. Collectively, we describe here the organization of the genetic architecture of ASD as a hierarchy of broadly-expressed and brain-specific genes that disrupt successive stages of core neurodevelopmental processes.

Autism spectrum disorder (ASD) is a largely heritable, multistage prenatal disorder that impacts a child's ability to perceive and react to social information. Most ASD risk genes are expressed prenatally in many ASD-relevant brain regions and fall into two categories: broadly expressed regulatory genes that are expressed in the brain and other organs, and brain-specific genes. In trimesters one to three (Epoch-1), one set of broadly expressed (the majority) and brain-specific risk genes disrupts cell proliferation, neurogenesis, migration, and cell fate, while in trimester three and early postnatally (Epoch-2) another set (the majority being brain specific) disrupts neurite outgrowth, synaptogenesis, and the 'wiring' of the cortex. A proposed model is that upstream, highly interconnected regulatory ASD gene mutations disrupt transcriptional programs or signaling pathways resulting in dysregulation of downstream processes such as proliferation, neurogenesis, synaptogenesis, and neural activity. Dysregulation of signaling pathways is correlated with ASD social symptom severity. Since the majority of ASD risk genes are broadly expressed, many ASD individuals may benefit by being treated as having a broader medical disorder. An important future direction is the noninvasive study of ASD cell biology.

The intricate life cycle of Trypanosoma brucei requires extensive regulation of gene expression levels of the mtRNAs for adaptation. Post-transcriptional gene regulatory programs, including unencoded mtRNA 3′ tail additions, potentially play major roles in this adaptation process. Intriguingly, T. brucei mitochondrial transcripts possess two distinct unencoded 3′ tails, each with a differing functional role; i.e., while one type is implicated in RNA stability (in-tails), the other type appears associated with translation (ex-tails). We examined the degree to which tail characteristics differ among cytochrome c oxidase subunits I and III (CO1 and CO3), and NADH dehydrogenase subunit 1 (ND1) transcripts, and to what extent these characteristics differ developmentally. We found that CO1, CO3 and ND1 transcripts possess longer in-tails in the mammalian life stage. By mathematically modelling states of in-tail and ex-tail addition, we determined that the typical length at which an in-tail is extended to become an ex-tail differs by transcript and, in the case of ND1, by life stage. To the best of our knowledge, we provide the first evidence that developmental differences exist in tail length distributions of mtRNAs, underscoring the potential involvement of in-tail and ex-tail populations in mitochondrial post-transcriptional regulation mechanisms.

We propose to integrate the existing and new experimental data with computational tools to model interaction networks for the most prominent kinetoplastid pathogens. These interaction networks will vastly expand the functional annotation of the kinetoplastid genomes, which in turn are critical for identifying new routes of disease intervention.

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms., Author summary Methods to predict protein function based on sequences enable the rapid annotation of newly sequenced genomes. However, as most of these methods rely on homology-based approaches, non-conserved proteins in trypanosomatids remain elusive for annotation, rendering approximately half of the sequenced proteins uncharacterized. In this study, we developed a user friendly integrated database, TrypsNetDB, which fills multiple gaps in the field by depositing the current interactome knowledge on trypanosomatid proteins and combining this information with other available resources accompanied by related statistical analyses. The database allows automatic inter-species mapping of available data to better characterize the queried proteins in the species of interest. The database is built on fast and reliable ASP.Net framework and provides (i) a significant increase in the genome-wide functional annotation of trypanosomatid proteins, (ii) potential novel targets for therapeutics against trypanosomatids, and (iii) a robust methodology that can be adapted for the functional annotation of other non-model organisms.

Morphological and metabolic changes in the life cycle of Trypanosoma brucei are accomplished by precise regulation of hundreds of genes. In the absence of transcriptional control, RNA-binding proteins (RBPs) shape the structure of gene regulatory maps in this organism, but our knowledge about their target RNAs, binding sites, and mechanisms of action is far from complete. Although recent technological advances have revolutionized the RBP-based approaches, the main framework for the RNA regulatory element (RRE)-based approaches has not changed over the last two decades in T. brucei. In this Opinion, after highlighting the current challenges in RRE inference, we explain some genome-wide solutions that can significantly boost our current understanding about gene regulatory networks in T. brucei.

This study examines the diagnostic stability of autism spectrum disorder in a large cohort of toddlers starting at 12 months of age and compares this stability with that of toddlers with other disorders., Key Points Question Is an autism spectrum disorder diagnosis stable by 18 months, the earliest age of American Academy of Pediatrics recommended screening? Findings In a cohort study of 1269 toddlers with and without autism spectrum disorder who received their first diagnostic evaluation between 12 and 36 months, overall stability of an autism spectrum diagnosis was 0.84, which was higher than in other groups. Meaning Accurate diagnosis of autism spectrum disorder at earlier than 18 months is feasible, and there may be opportunities to test the usefulness of autism spectrum disorder treatment at an early age., Importance Universal early screening for autism spectrum disorder (ASD) in primary care is becoming increasingly common and is believed to be a pivotal step toward early treatment. However, the diagnostic stability of ASD in large cohorts from the general population, particularly in those younger than 18 months, is unknown. Changes in the phenotypic expression of ASD across early development compared with toddlers with other delays are also unknown. Objectives To examine the diagnostic stability of ASD in a large cohort of toddlers starting at 12 months of age and to compare this stability with that of toddlers with other disorders, such as developmental delay. Design, Setting, and Participants In this prospective cohort study performed from January 1, 2006, to December 31, 2018, a total of 2241 toddlers were referred from the general population through a universal screening program in primary care or community referral. Eligible toddlers received their first diagnostic evaluation between 12 and 36 months of age and had at least 1 subsequent evaluation. Exposures Diagnosis was denoted after each evaluation visit as ASD, ASD features, language delay, developmental delay, other developmental issue, typical sibling of an ASD proband, or typical development. Main Outcomes and Measures Diagnostic stability coefficients were calculated within 2-month age bands, and logistic regression models were used to explore the associations of sex, age, diagnosis at first visit, and interval between first and last diagnosis with stability. Toddlers with a non-ASD diagnosis at their first visit diagnosed with ASD at their last were designated as having late-identified ASD. Results Among the 1269 toddlers included in the study (918 [72.3%] male; median age at first evaluation, 17.6 months [interquartile range, 14.0-24.4 months]; median age at final evaluation, 36.2 months [interquartile range, 33.4-40.9 months]), the overall diagnostic stability for ASD was 0.84 (95% CI, 0.80-0.87), which was higher than any other diagnostic group. Only 7 toddlers (1.8%) initially considered to have ASD transitioned into a final diagnosis of typical development. Diagnostic stability of ASD within the youngest age band (12-13 months) was lowest at 0.50 (95% CI, 0.32-0.69) but increased to 0.79 by 14 months and 0.83 by 16 months (age bands of 12 vs 14 and 16 years; odds ratio, 4.25; 95% CI, 1.59-11.74). A total of 105 toddlers (23.8%) were not designated as having ASD at their first visit but were identified at a later visit. Conclusions and Relevance The findings suggest that an ASD diagnosis becomes stable starting at 14 months of age and overall is more stable than other diagnostic categories, including language or developmental delay. After a toddler is identified as having ASD, there may be a low chance that he or she will test within typical levels at 3 years of age. This finding opens the opportunity to test the impact of very early-age treatment of ASD.

Trypanosoma brucei is a vector-borne parasite with intricate life cycle that can cause serious diseases in humans and animals. This pathogen relies on fine regulation of gene expression to respond and adapt to variable environments, with implications in transmission and infectivity. However, the involved regulatory elements and their mechanisms of actions are largely unknown. Here, benefiting from a new graph-based approach for finding functional regulatory elements in RNA (GRAFFER), we have predicted 88 new RNA regulatory elements that are potentially involved in the gene regulatory network of T. brucei. We show that many of these newly predicted elements are responsive to both transcriptomic and proteomic changes during the life cycle of the parasite. Moreover, we found that 11 of predicted elements strikingly resemble previously identified regulatory elements for the parasite. Additionally, comparison with previously predicted motifs on T. brucei suggested the superior performance of our approach based on the current limited knowledge of regulatory elements in T. brucei.

Post-transcriptionally added RNA 3′ nucleotide extensions, or tails, impose numerous regulatory effects on RNAs, including effects on RNA turnover and translation. However, efficient methods for in-depth tail profiling of a transcript of interest are still lacking, hindering available knowledge particularly of tail populations that are highly heterogeneous. Here, we developed a targeted approach, termed circTAIL-seq, to quantify both major and subtle differences of heterogeneous tail populations. As proof-of-principle, we show that circTAIL-seq quantifies the differences in tail qualities between two selected Trypanosoma brucei mitochondrial transcripts. The results demonstrate the power of the developed method in identification, discrimination, and quantification of different tail states that the population of one transcript can possess. We further show that circTAIL-seq can detect the tail characteristics for variants of transcripts that are not easily detectable by conventional approaches, such as degradation intermediates. Our findings are not only well supported by previous knowledge, but they also expand this knowledge and provide experimental evidence for previous hypotheses. In the future, this approach can be used to determine changes in tail qualities in response to environmental or internal stimuli, or upon silencing of genes of interest in mRNA-processing pathways. In summary, circTAIL-seq is an effective tool for comparing nonencoded RNA tails, especially when the tails are extremely variable or transcript of interest is low abundance.

Pegah Khosravi and Vahid H Gazestani contributed equally The online version of the original article can be found under doi:10.1186/s13015-015-0054-4.

Prostate cancer is a serious genetic disease known to be one of the most widespread cancers in men, yet the molecular changes that drive its progression are not fully understood. The availability of high-throughput gene expression data has led to the development of various computational methods for the identification of key processes involved.

The human Y chromosome contains a small number of genes that play a critical role in the determination of male-specific organs. Today's advances have provided valuable resources for defining the functions of this chromosome in both normal and cancerous prostates. Despite the fact that generation of high-throughput expression data is becoming usual; the systematic methods of data analysis in a biological context are still an impediment.Here we have shown that constructing co-expression networks using Y-chromosome genes provides an alternative strategy for the detection of new candidate genes involved in prostate cancer. In our approach, independent co-expression networks from normal and cancerous stages are reconstructed using a reverse engineering approach. We then highlight crucial pathways, biological processes, and genes involved in the prostate cancer by analyzing each network individually and in concert. Thus, we have identified 18 critical pathways and processes related to prostate cancer, many of which have previously been shown to be involved in cancer. In particular, we identify 22 Y-chromosome genes putatively linked to prostate cancer, 13 of which have been already verified experimentally.Our novel network-based approach is useful for accurate inference of processes and essential regulators that mediate molecular changes during cancer progression. We developed an integrative network-based framework for prostate cancer.We examined the role of Y-chromosome genes through different states.The new definition of modulation score is proposed for detecting novel pathways and processes.Candidate genes are introduced for future research in the field of cancer studies as key factors.

Background Prostate cancer, a serious genetic disease, has known as the first widespread cancer in men, but the molecular changes required for the cancer progression has not fully understood. Availability of high-throughput gene expression data has led to the development of various computational methods, for identification of the critical genes, have involved in the cancer. Methods In this paper, we have shown the construction of co-expression networks, which have been using Y-chromosome genes, provided an alternative strategy for detecting of new candidate, might involve in prostate cancer. In our approach, we have constructed independent co-expression networks from normal and cancerous stages have been using a reverse engineering approach. Then we have highlighted crucial Y chromosome genes involved in the prostate cancer, by analyzing networks, based on party and date hubs. Results Our results have led to the detection of 19 critical genes, related to prostate cancer, which 12 of them have previously shown to be involved in this cancer. Also, essential Y chromosome genes have searched based on reconstruction of sub-networks which have led to the identification of 4 experimentally established as well as 4 new Y chromosome genes might be linked putatively to prostate cancer. Conclusion Correct inference of master genes, which mediate molecular, has changed during cancer progression would be one of the major challenges in cancer genomics. In this paper, we have shown the role of Y chromosome genes in finding of the prostate cancer susceptibility genes. Application of our approach to the prostate cancer has led to the establishment of the previous knowledge about this cancer as well as prediction of other new genes.

Background Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. Results This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. Conclusions SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type. Electronic supplementary material The online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users.

The functions of the majority of trypanosomatid-specific proteins are unknown, hindering our understanding of the biology and pathogenesis of Trypanosomatida. While protein-protein interactions are highly informative about protein function, a global map of protein interactions and complexes is still lacking for these important human parasites. Here, benefiting from in-depth biochemical fractionation, we systematically interrogated the co-complex interactions of more than 3354 protein groups in procyclic life stage of Trypanosoma brucei, the protozoan parasite responsible for human African trypanosomiasis. Using a rigorous methodology, our analysis led to identification of 128 high-confidence complexes encompassing 716 protein groups, including 635 protein groups that lacked experimental annotation. These complexes correlate well with known pathways as well as for proteins co-expressed across the T. brucei life cycle, and provide potential functions for a large number of previously uncharacterized proteins. We validated the functions of several novel proteins associated with the RNA-editing machinery, identifying a candidate potentially involved in the mitochondrial post-transcriptional regulation of T. brucei. Our data provide an unprecedented view of the protein complex map of T. brucei, and serve as a reliable resource for further characterization of trypanosomatid proteins. The presented results in this study are available at: www.TrypsNetDB.org., Author Summary Due to high evolutionary divergence of trypanosomatid pathogens from other eukaryotes, accurate prediction of functional roles for most of their proteins is not feasible based on homology-based approaches. Although protein co-complex maps provide a compelling tool for the functional annotation of proteins, as subunits of a complex are expected to be involved in similar biological processes, the current knowledge about these maps is still rudimentary. Here, we systematically examined the protein co-complex membership of more than one third of T. brucei proteome using two orthogonal fractionation approaches. A high-confidence network of co-complex relationships predicts the network context of 866 proteins, including many hypothetical and experimentally unannotated proteins. To our knowledge, this study presents the largest proteomics-based interaction map of trypanosomatid parasites to date, providing a useful resource for formulating new biological hypothesises and further experimental leads.

Funder: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH), Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e.