Maternal social environment shapes yolk testosterone allocation and embryonic neural gene expression in tree swallows

Offspring from females breeding in competitive social environments are often exposed to more testosterone (T) during embryonic development, which can affect traits from growth to behavior in potentially adaptive ways. Despite the important role of maternally derived steroids in shaping offspring development, the molecular mechanisms driving these processes are currently unclear. Here, we use tree swallows (Tachycineta bicolor) to explore the effects of the maternal social environment on yolk T concentrations and genome-wide patterns of neural gene expression in embryos. We measured aggressive interactions among females breeding at variable densities and collected their eggs at two timepoints, including the day laid to measure yolk T concentrations and on embryonic day 11 to measure gene expression in whole brain samples. We found that females breeding in high-density sites experienced elevated rates of physical aggression and their eggs had higher yolk T concentrations. A differential gene expression and weighted gene co-expression network analysis indicated that embryos from high-density sites experienced an upregulation of genes involved in hormone, circulatory, and immune processes, and these gene expression patterns were correlated with yolk T levels and aggression. Genes implicated in neural development were additionally downregulated in embryos from high-density sites. These data highlight how early neurogenomic processes may be affected by the maternal social environment, giving rise to phenotypic plasticity in offspring.

Profiling of BCLxL Protein Complexes in Non-Small Cell Lung Cancer Cells via Multiplexed Single-Molecule Pull-Down and Co-Immunoprecipitation

We introduce multiplexed single-molecule pull-down and co-immunoprecipitation, named m-SMPC, an analysis tool for profiling multiple protein complexes within a single reaction chamber using single-molecule fluorescence imaging. We employed site-selective conjugation of biotin and fluorescent dye directly onto the monoclonal antibodies, which completed an independent sandwich immunoassay without the issue of host cross-reactivity. We applied this technique to profile endogenous B-cell lymphoma extra-large (BCLxL) complexes in non-small cell lung cancer (NSCLC) cells. Up to three distinct BCLxL complexes were successfully detected simultaneously within a single reaction chamber without fluorescence signal crosstalk. Notably, the NSCLC cell line EBC-1 exhibited high BCLxL-BAX and BCLxL-BAK levels, which closely paralleled a strong response to the BCLxL inhibitor A-1331852. This streamlined method offers the potential for quantitative biomarkers derived from protein complex profiling, paving the way for their application in protein complex-targeted therapies.

YTHDF2 governs muscle size through a targeted modulation of proteostasis

The regulation of proteostasis is fundamental for maintenance of muscle mass and function. Activation of the TGF-β pathway drives wasting and premature aging by favoring the proteasomal degradation of structural muscle proteins. Yet, how this critical post-translational mechanism is kept in check to preserve muscle health remains unclear. Here, we reveal the molecular link between the post-transcriptional regulation of m6A-modified mRNA and the modulation of SMAD-dependent TGF-β signaling. We show that the m6A-binding protein YTHDF2 is essential to determining postnatal muscle size. Indeed, muscle-specific genetic deletion of YTHDF2 impairs skeletal muscle growth and abrogates the response to hypertrophic stimuli. We report that YTHDF2 controls the mRNA stability of the ubiquitin ligase ASB2 with consequences on anti-growth gene program activation through SMAD3. Our study identifies a post-transcriptional to post-translational mechanism for the coordination of gene expression in muscle.

MotifHub: Detection of trans-acting DNA motif group with probabilistic modeling algorithm

BACKGROUND

Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting elements and regulate the transcription efficiency of target genes. The progressive development in high-throughput chromatin capture technology (e.g., Hi-C) enables the identification of chromatin-interacting sequence groups where trans-acting DNA motif groups can be discovered. The problem difficulty lies in the combinatorial nature of DNA sequence pattern matching and its underlying sequence pattern search space.

METHOD

Here, we propose to develop MotifHub for trans-acting DNA motif group discovery on grouped sequences. Specifically, the main approach is to develop probabilistic modeling for accommodating the stochastic nature of DNA motif patterns.

RESULTS

Based on the modeling, we develop global sampling techniques based on EM and Gibbs sampling to address the global optimization challenge for model fitting with latent variables. The results reflect that our proposed approaches demonstrate promising performance with linear time complexities.

CONCLUSION

MotifHub is a novel algorithm considering the identification of both DNA co-binding motif groups and trans-acting TFs. Our study paves the way for identifying hub TFs of stem cell development (OCT4 and SOX2) and determining potential therapeutic targets of prostate cancer (FOXA1 and MYC). To ensure scientific reproducibility and long-term impact, its matrix-algebra-optimized source code is released at http://bioinfo.cs.cityu.edu.hk/MotifHub.

Gene expression flux analysis reveals specific regulatory modalities of gene expression

The level of a given protein is determined by the synthesis and degradation rates of its mRNA and protein. While several studies have quantified the contribution of different gene expression steps in regulating protein levels, these are limited by using equilibrium approximations in out-of-equilibrium biological systems. Here, we introduce gene expression flux analysis to quantitatively dissect the dynamics of the expression level for specific proteins and use it to analyze published transcriptomics and proteomics datasets. Our analysis reveals distinct regulatory modalities shared by sets of genes with clear functional signatures. We also find that protein degradation plays a stronger role than expected in the adaptation of protein levels. These findings suggest that shared regulatory strategies can lead to versatile responses at the protein level and highlight the importance of going beyond equilibrium approximations to dissect the quantitative contribution of different steps of gene expression to protein dynamics.

Methylation status, mRNA and protein expression of the SMAD4 gene in patients with non-melanocytic skin cancers

BACKGROUND

SMAD4 is a potent tumor suppressor. SMAD4 loss increases genomic instability and plays a critical role in the DNA damage response that leads to skin cancer development. We aimed to investigate SMAD4 methylation effects on mRNA and protein expression of SMAD4 in cancer and healthy tissues from patients with basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and basosquamous skin cancer (BSC).

METHODS AND RESULTS

The study included 17 BCC, 24 cSCC and nine BSC patients. DNA and RNA were isolated from cancerous and healthy tissues following punch biopsy. Methylation-specific polymerase chain reaction (PCR) and real-time quantitative PCR methods were used to examine SMAD4 promoter methylation and SMAD4 mRNA levels, respectively. The percentage and intensity of staining of the SMAD4 protein were determined by immunohistochemistry. The percentage of SMAD4 methylation was increased in the patients with BCC (p = 0.007), cSCC (p = 0.004), and BSC (p = 0.018) compared to the healthy tissue. SMAD4 mRNA expression was decreased in the patients with BCC (p˂0.001), cSCC (p˂0.001), and BSC (p = 0.008). The staining characteristic of SMAD4 protein was negative in the cancer tissues of the patients with cSCC (p = 0.00). Lower SMAD4 mRNA levels were observed in the poorly differentiated cSCC patients (p = 0.001). The staining characteristics of the SMAD4 protein were related to age and chronic sun exposure.

CONCLUSIONS

Hypermethylation of SMAD4 and reduced SMAD4 mRNA expression were found to play a role in the pathogenesis of BCC, cSCC, and BSC. A decrease in SMAD4 protein expression level was observed only in cSCC patients. This suggests that epigenetic alterations to the SMAD4 gene are associated with cSCC.

TRIAL REGISTRATION

The name of the trial register: SMAD4 Methylation and Expression Levels in Non-melanocytic Skin Cancers; SMAD4 Protein Positivity. The registration number: NCT04759261 ( https://clinicaltrials.gov/ct2/results?term=NCT04759261 ).

Clustering single-cell multimodal omics data with jrSiCKLSNMF

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

An Evidence-Based Narrative Review of Mechanisms of Resistance Exercise-Induced Human Skeletal Muscle Hypertrophy

Skeletal muscle plays a critical role in physical function and metabolic health. Muscle is a highly adaptable tissue that responds to resistance exercise (RE; loading) by hypertrophying, or during muscle disuse, RE mitigates muscle loss. Resistance exercise training (RET)-induced skeletal muscle hypertrophy is a product of external (e.g., RE programming, diet, some supplements) and internal variables (e.g., mechanotransduction, ribosomes, gene expression, satellite cells activity). RE is undeniably the most potent nonpharmacological external variable to stimulate the activation/suppression of internal variables linked to muscular hypertrophy or countering disuse-induced muscle loss. Here, we posit that despite considerable research on the impact of external variables on RET and hypertrophy, internal variables (i.e., inherent skeletal muscle biology) are dominant in regulating the extent of hypertrophy in response to external stimuli. Thus, identifying the key internal skeletal muscle-derived variables that mediate the translation of external RE variables will be pivotal to determining the most effective strategies for skeletal muscle hypertrophy in healthy persons. Such work will aid in enhancing function in clinical populations, slowing functional decline, and promoting physical mobility. We provide up-to-date, evidence-based perspectives of the mechanisms regulating RET-induced skeletal muscle hypertrophy.

Quantification of Intracellular Proteins in Single Cells Based on Engineered Picoliter Droplets

Unlike conventional bulk measurements, single-cell protein analysis permits quantification of protein expression in individual cells. This has shed light on the cell-to-cell variation in heterogeneous biological systems, such as solid tumors, brain tissues, and developing embryos. Herein, a microfluidic method is developed to profile protein expression in individual cells by performing single-cell intracellular protein immunoassay in picoliter paired droplets. The high sensitivity of single-cell protein analysis on a chip is achieved by the confined reaction volume of picoliter droplets, efficient kinetic characteristics of the immunoassay through active mixing, and minimum single-cell protein loss by integrated operations. The abundance of an intracellular prostate specific antigen at the single-cell level is measured, and then the platform is applied to identify cell types and investigate heterogeneity within cell populations. Overall, a paired chip for single-cell immunoassay establishes a foundation for parallel, sensitive, and integrated protein quantification at the single-cell level and will find wide applications in the field of single-cell proteomics.

Identification of the genetic central dogma in osteogenic differentiation of MSCs by osteoinductive medium from transcriptional data sets

Background

The genetic central dogma (GCD) has been demonstrated its essential function in many biological processes and diseases. However, its roles in the process of osteogenic differentiation of mesenchymal stem cells (MSCs) remain unclear.

Methods

In this project, we analyzed an online database of osteogenic differentiation of MSCs after 14 days and 28 days by osteoinductive medium (GSE83770). The differentially expressed genes were screened by GEO2R, with further conducting of KEGG pathways using DAVID. In addition, protein–protein interactions of the enriched pathways were performed using STRING with marked hub genes measured by the CytoHubba. Hub genes were verified by quantitative reverse‐transcription polymerase chain reaction.

Results

Results showed that six pathways related to GCD, including DNA replication, Aminoacyl‐tRNA biosynthesis, Mismatch repair, Ribosome, Spliceosome, and RNA degradation pathways enriched in the early stage (14 days vs. undifferentiated MSCs) of osteogenesis. The Lysosome pathway was highly enriched in the late stage (28 vs. 14 days) of osteogenesis, and Ribosome pathway plays a key role throughout the entire process (28 days vs. undifferentiated MSCs) of osteogenesis.

Conclusion

Both DNA replication and protein translation were functionally worked in the early stage of osteogenesis, whereas the Lysosome pathway was the only GCD‐related one in the late stage of osteogenesis. The GCD‐related Ribosome pathway occupied the entire process of osteogenesis.

We identified six pathways associated with genetic central dogma (DNA replication, Aminoacyl‐tRNA biosynthesis, Mismatch repair, Ribosome, Spliceosome, and RNA degradation pathways), which participated in the early stage of osteogenesis (0−14 days).

The Lysosome pathway plays a crucial role in the late stage of osteogenesis (14–28 days), whereas the Ribosome pathway participates in the entire process (28 days vs. undifferentiated mesenchymal stem cells) of osteogenesis.

COL18A1 genotypic associations with endostatin levels and clinical features in pulmonary arterial hypertension: a quantitative trait association study

Endostatin (ES) is a circulating peptide derived from collagen XVIII alpha 1 (COL18A1) known to inhibit angiogenesis [1, 2]. Decreased angiogenesis is a feature of pulmonary arterial hypertension (PAH) in animal models [3] and human subjects [4]. Our group has reported strong associations between circulating ES levels and haemodynamics and survival in PAH [5–7]. We have also reported that a missense variant in COL18A1, which encodes ES, confers lower ES and longer survival, suggesting that variation within the gene contributes to circulating levels [5]. In the current study, we assessed COL18A1 variant associations with clinical phenotypes and outcomes, including COL18A1 associations with circulating ES levels, in a large, multicentre PAH cohort in which we previously investigated ES as a prognostic biomarker [6].

Variation around the COL18A1 gene, which encodes the angiostatic peptide endostatin, may influence disease heterogeneity in pulmonary arterial hypertensionhttps://bit.ly/3shXrNR

An Integrative Sialomic Analysis Reveals Molecules From Triatoma sordida (Hemiptera: Reduviidae)

Triatomines have evolved salivary glands that produce versatile molecules with various biological functions, including those leading their interactions with vertebrate hosts’ hemostatic and immunological systems. Here, using high-throughput transcriptomics and proteomics, we report the first sialome study on the synanthropic triatomine Triatoma sordida. As a result, 57,645,372 reads were assembled into 26,670 coding sequences (CDS). From these, a total of 16,683 were successfully annotated. The sialotranscriptomic profile shows Lipocalin as the most abundant protein family within putative secreted transcripts. Trialysins and Kazal-type protease inhibitors have high transcript levels followed by ubiquitous protein families and enzyme classes. Interestingly, abundant trialysin and Kazal-type members are highlighted in this triatomine sialotranscriptome. Furthermore, we identified 132 proteins in T. sordida salivary gland soluble extract through LC-MS/MS spectrometry. Lipocalins, Hemiptera specific families, CRISP/Antigen-5 and Kazal-type protein inhibitors proteins were identified. Our study provides a comprehensive description of the transcript and protein compositions of the salivary glands of T. sordida. It significantly enhances the information in the Triatominae sialome databanks reported so far, improving the understanding of the vector’s biology, the hematophagous behaviour, and the Triatominae subfamily’s evolution.

Regulation of mRNA translation in stem cells; links to brain disorders

Translational control of gene expression is emerging as a cardinal step in the regulation of protein abundance. Especially for embryonic (ESC) and neuronal stem cells (NSC), regulation of mRNA translation is involved in the maintenance of pluripotency but also differentiation. For neuronal stem cells this regulation is linked to the various neuronal subtypes that arise in the developing brain and is linked to numerous brain disorders. Herein, we review translational control mechanisms in ESCs and NSCs during development and differentiation, and briefly discuss their link to brain disorders.

A reevaluation of the role of the ASIL trihelix transcription factors as repressors of the seed maturation program

Developmental transitions are typically tightly controlled at the transcriptional level. Two of these transitions involve the induction of the embryo maturation program midway through seed development and its repression during the vegetative phase of plant growth. Very little is known about the factors responsible for this regulation during early embryogenesis, and only a couple of transcription factors have been characterized as repressors during the postgerminative phase. Arabidopsis 6b-INTERACTING PROTEIN-LIKE1 (ASIL1), a trihelix transcription factor, has been proposed to repress maturation both embryonically and postembryonically. Preliminary data also suggested that its closest paralog, ASIL2, might play a role as well. We used a transcriptomic approach, coupled with phenotypical observations, to test the hypothesis that ASIL1 and ASIL2 redundantly turn off maturation during both phases of growth. Our results indicate that, contrary to what was previously published, neither of the ASIL genes plays a role in the regulation of maturation, at any point during plant development. Analyses of gene ontology (GO)-enriched terms and published transcriptomic datasets suggest that these genes might be involved in responses during the vegetative phase to certain biotic and abiotic stresses.

Regulation of gene expression via translational buffering

Translation of an mRNA represents a critical step during the expression of protein-coding genes. As mechanisms governing post-transcriptional regulation of gene expression are progressively unveiled, it is becoming apparent that transcriptional programs are not fully reflected in the proteome. Herein, we highlight a previously underappreciated post-transcriptional mode of regulation of gene expression termed translational buffering. In principle, translational buffering opposes the impact of alterations in mRNA levels on the proteome. We further describe three types of translational buffering: compensation, which maintains protein levels e.g. across species or individuals; equilibration, which retains pathway stoichiometry; and offsetting, which acts as a reversible mechanism that maintains the levels of selected subsets of proteins constant despite genetic alteration and/or stress-induced changes in corresponding mRNA levels. While mechanisms underlying compensation and equilibration have been reviewed elsewhere, the principal focus of this review is on the less-well understood mechanism of translational offsetting. Finally, we discuss potential roles of translational buffering in homeostasis and disease.

A mean-field approach for modeling the propagation of perturbations in biochemical reaction networks

Often, the time evolution of a biochemical reaction network is crucial for determining the effects of combining multiple pharmaceuticals. Here we illustrate a mathematical framework for modeling the dominant temporal behaviour of a complicated molecular pathway or biochemical reaction network in response to an arbitrary perturbation, such as resulting from the administration of a therapeutic agent. The method enables the determination of the temporal evolution of a target protein as the perturbation propagates through its regulatory network. The mathematical approach is particularly useful when the experimental data that is available for characterizing or parameterizing the regulatory network is limited or incomplete. To illustrate the method, we consider the examples of the regulatory networks for the target proteins c-Myc and Chop, which play an important role in venetoclax resistance in acute myeloid leukemia. First we show how the networks that regulate each target protein can be reduced to a mean-field model by identifying the distinct effects that groups of proteins in the regulatory network have on the target protein. Then we show how limited protein-level data can be used to further simplify the mean-field model to pinpoint the dominant effects of the network perturbation on the target protein. This enables a further reduction in the number of parameters in the model. The result is an ordinary differential equation model that captures the temporal evolution of the expression of a target protein when one or more proteins in its regulatory network have been perturbed. Finally, we show how the dominant effects predicted by the mathematical model agree with RNA sequencing data for the regulatory proteins comprising the molecular network, despite the model not having a priori knowledge of this data. Thus, while the approach gives a simplified model for the expression of the target protein, it allows for the interpretation of the effects of the perturbation on the regulatory network itself. This method can be easily extended to sets of target proteins to model components of a larger systems biology model, and provides an approach for partially integrating RNA sequencing data and protein expression data. Moreover, it is a general approach that can be used to study drug effects on specific protein(s) in any disease or condition.

Mistranslation Drives Alterations in Protein Levels and the Effects of a Synonymous Variant at the Fibroblast Growth Factor 21 Locus

Fibroblast growth factor 21 (FGF21) is a liver-derived hormone with pleiotropic beneficial effects on metabolism. Paradoxically, FGF21 levels are elevated in metabolic diseases. Interventions that restore metabolic homeostasis reduce FGF21. Whether abnormalities in FGF21 secretion or resistance in peripheral tissues is the initiating factor in altering FGF21 levels and function in humans is unknown. A genetic approach is used to help resolve this paradox. The authors demonstrate that the primary event in dysmetabolic phenotypes is the elevation of FGF21 secretion. The latter is regulated by translational reprogramming in a genotype- and context-dependent manner. To relate the findings to tissues outcomes, the minor (A) allele of rs838133 is shown to be associated with increased hepatic inflammation in patients with metabolic associated fatty liver disease. The results here highlight a dominant role for translation of the FGF21 protein to explain variations in blood levels that is at least partially inherited. These results provide a framework for translational reprogramming of FGF21 to treat metabolic diseases.

Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States

Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by ∼40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3' and 5' untranslated regions (UTRs). Our strategy detects ∼3-4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

High-Fat Diet Alters the Expression of Reference Genes in Male Mice

Quantitative PCR (qPCR), the most accurate and sensitive technique for quantifying mRNA expression, and choice of appropriate reference genes for internal error controlling in qPCR are essential to understanding the molecular mechanisms that drive the obesity epidemic and its comorbidities. In this study, using the high-fat diet (HFD)-induced obese mouse model, we assessed the expression of 10 commonly used reference genes to validate gene-expression stability in adipose tissue, liver, and muscle across different time points (4, 8, 12, and 16 weeks after HFD feeding) during the process of obesity. The data were analyzed by the GeNorm, NormFinder, BestKeeper, and Delta-Ct method, and the results showed that the most stable reference genes were different for a specific organ or tissue in a specific time point; however, PPIA, RPLP0, and YWHAZ were the top three most stable reference genes in qPCR experiments on adipose, hepatic tissues, and muscles of mice in diet-induced obesity. In addition, the mostly used genes ACTB and GAPDH were more unstable in the fat and liver, the ACTB mRNA levels were increased in four adipose tissues, and the GAPDH mRNA levels were decreased in four adipose tissues and liver after HFD feeding. These results suggest that PPIA, RPLP0, or YWHAZ may be more appropriate to be used as reference gene than ACTB and GAPDH in the adipose tissue and liver of mice during the process of high-fat diet-induced obesity.

Transcriptional regulation of prolactin in a euryhaline teleost: Characterisation of gene promoters through in silico and transcriptome analyses

The sensitivity of prolactin (Prl) cells of the Mozambique tilapia (Oreochromis mossambicus) pituitary to variations in extracellular osmolality enables investigations into how osmoreception underlies patterns of hormone secretion. Through the actions of their main secretory products, Prl cells play a key role in supporting hydromineral balance of fishes by controlling the major osmoregulatory organs (ie, gill, intestine and kidney). The release of Prl from isolated cells of the rostral pars distalis (RPD) occurs in direct response to physiologically relevant reductions in extracellular osmolality. Although the particular signal transduction pathways that link osmotic conditions to Prl secretion have been identified, the processes that underlie hyposmotic induction of prl gene expression remain unknown. In this short review, we describe two distinct tilapia gene loci that encode Prl177 and Prl188 . From our in silico analyses of prl177 and prl188 promoter regions (approximately 1000 bp) and a transcriptome analysis of RPDs from fresh water (FW)- and seawater (SW)-acclimated tilapia, we propose a working model for how multiple transcription factors link osmoreceptive processes with adaptive patterns of prl177 and prl188 gene expression. We confirmed via RNA-sequencing and a quantitative polymerase chain reaction that multiple transcription factors emerging as predicted regulators of prl gene expression are expressed in the RPD of tilapia. In particular, gene transcripts encoding pou1f1, stat3, creb3l1, pbxip1a and stat1a were highly expressed; creb3l1, pbxip1a and stat1a were elevated in fish acclimated to SW vs FW. Combined, our in silico and transcriptome analyses set a path for resolving how adaptive patterns of Prl secretion are achieved via the integration of osmoreceptive processes with the control of prl gene transcription.

Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature

RNA-Protein binding is involved in many different biological processes. With the progress of technology, more and more data are available for research. Based on these data, many prediction methods have been proposed to predict RNA-Protein binding preference. Some of these methods use only RNA sequence features for prediction, and some methods use multiple features for prediction. But, the performance of these methods is not satisfactory. In this study, we propose an improved capsule network to predict RNA-protein binding preferences, which can use both RNA sequence features and structure features. Experimental results show that our proposed method iCapsule performs better than three baseline methods in this field. We used both RNA sequence features and structure features in the model, so we tested the effect of primary capsule layer changes on model performance. In addition, we also studied the impact of model structure on model performance by performing our proposed method with different number of convolution layers and different kernel sizes.

NLRP3 inflammasome is involved in nerve recovery after sciatic nerve injury

The activation of the inflammasome plays an important role in the central nervous system. However, only a few studies have investigated the effects of inflammasome activation in the peripheral nerve, especially in the sciatic nerve, and the mechanism of this activation remains elusive. Moreover, how interleukin-1 beta (IL-1β) is produced after sciatic nerve injury is also unknown. In our study, we aimed to investigate whether the nucleotide-binding oligomerization domain-like pyrin domain containing protein 3 (NLRP3) inflammasome is activated after sciatic nerve injury and to explore its role in sciatic nerve injury. The results of immunoblotting and immunofluorescence microscopy indicate that the NLRP3 inflammasome was activated after sciatic nerve injury in wild-type (WT) mice, as demonstrated by upregulated inflammasome-related components, e.g., NLRP3, procaspase-1 and ASC. Furthermore, upregulated inflammasome-related components cis-cleavage precursor IL-1β (proIL-1β) and precursor interleukin-18 (proIL-18) to IL-1β and IL-18, contributing to the inflammatory response. Consequently, the inflammatory response after sciatic nerve injury in NLRP3 knockout (NLRP3-KO) mice was less severe than that in WT mice. Moreover, NLRP3-KO mice exhibited an increased sciatic functional index (SFI), which was determined by footprint analysis, suggesting that NLRP3 deficiency is beneficial to sciatic nerve recovery after injury. Therefore, our results indicate that NLRP3 is involved in the recovery from sciatic nerve injury and mediates the production of inflammatory factors, such as IL-1β, after sciatic nerve injury.

Effects of short-term high-intensity interval and continuous exercise training on body composition and cardiac function in obese sarcopenic rats

AIM

We investigated the effects of high-intensity interval and continuous short-term exercise on body composition and cardiac function after myocardial ischemia-reperfusion injury (IRI) in obese rats.

METHODS

Rats fed with a standard chow diet (SC) or high-fat diet (HFD) for 20 weeks underwent systolic blood pressure (SBP), glycemia and dual-energy X-ray absorptiometry analyses. Then, animals fed with HFD were subdivided into three groups: sedentary (HFD-SED); moderate-intensity continuous training (HFD-MICT); and high-intensity interval training (HFD-HIIT). Exercised groups underwent four isocaloric aerobic exercise sessions, in which HFD-MICT maintained the intensity continuously and HFD-HIIT alternated it. After exercise sessions, all groups underwent global IRI and myocardial infarct size (IS) was determined histologically. Fat and muscle mass were weighted, and protein levels involved in muscle metabolism were assessed in skeletal muscle.

RESULTS

HFD-fed versus SC-fed rats reduced lean body mass by 31% (P < 0.001), while SBP, glycemia and body fat percentage were increased by 10% (P = 0.04), 30% (P = 0.006) and 54% (P < 0.001); respectively. HFD-induced muscle atrophy was restored in exercised groups, as only HFD-SED presented lower gastrocnemius (32%; P = 0.001) and quadriceps mass (62%; P < 0.001) than SC. PGC1-α expression was 2.7-fold higher in HFD-HIIT versus HFD-SED (P = 0.04), whereas HFD-HIIT and HFD-MICT exhibited 1.7-fold increase in p-mTOR^Ser2481 levels compared to HFD-SED (P = 0.04). Although no difference was detected among groups for IS (P = 0.30), only HFD-HIIT preserved left-ventricle developed pressure after IRI (+0.7 mmHg; P = 0.9).

SIGNIFICANCE

Short-term exercise, continuous or HIIT, restored HFD-induced muscle atrophy and increased mTOR expression, but only HIIT maintained myocardial contractility following IRI in obese animals.

RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

The translational landscape of ground state pluripotency

Translational control plays a central role in regulation of gene expression and can lead to significant divergence between mRNA- and protein-abundance. Here, we used genome-wide approaches combined with time-course analysis to measure the mRNA-abundance, mRNA-translation rate and protein expression during the transition of naïve-to-primed mouse embryonic stem cells (ESCs). We find that the ground state ESCs cultured with GSK3-, MEK-inhibitors and LIF (2iL) display higher ribosome density on a selective set of mRNAs. This set of mRNAs undergo strong translational buffering to maintain stable protein expression levels in 2iL-ESCs. Importantly, we show that the global alteration of cellular proteome during the transition of naïve-to-primed pluripotency is largely accompanied by transcriptional rewiring. Thus, we provide a comprehensive and detailed overview of the global changes in gene expression in different states of ESCs and dissect the relative contributions of mRNA-transcription, translation and regulation of protein stability in controlling protein abundance.

Translational control of gene expression can lead to significant divergence between mRNA and protein abundance. Here, the authors describe transcriptional rewiring and translational buffering during transition from naïve to primed pluripotency through quantitation of mRNA-abundance, translation rate and protein expression.

Transcriptome profiling of buffalo endometrium reveals molecular signature distinct to early pregnancy

Buffalo reproduction struggles with a high incidence of early embryonic mortality. Effective treatment and prevention strategies for this condition are not available due to lack of understanding of molecular pathways in early pregnancy of this species. In the present study, we have attempted to understand these molecular pathways by characterizing the endometrial transcriptomic profiles of pregnant buffalos during early pregnancy. For the transcriptome profiling, buffalo endometrial tissues of 29-36 days of pregnancy and of nonpregnant luteal phase were collected from the local slaughterhouse. We confirmed the status of pregnancy based on the crown vertebral length of the foetus. Total RNA was isolated and sequencing was performed using the Illumina nextseq platform. The raw reads were filtered and mapped to the Bos taurus UMD 3.1 reference genome assembly. An average of 24,597 genes was investigated for differential expression between the two groups. Transcriptome data identified a total of 450 differentially expressed genes (using a cut off value of log₂ fold changes >2 and <-2) in early pregnancy in comparison to the nonpregnant group (P_adj < 0.05). Among these, 270 genes were significantly upregulated and 180 genes were downregulated. The most impacted pathways were related to secretion, transport, ionic homeostasis, mitosis and negative regulation of viral processes. In conclusion, our study characterized a unique set of DEGs, during the early pregnancy of buffalo, which potentially modulate the endometrial environment to establish and maintain a successful pregnancy.

Functional omics analyses reveal only minor effects of microRNAs on human somatic stem cell differentiation

The contribution of microRNA-mediated posttranscriptional regulation on the final proteome in differentiating cells remains elusive. Here, we evaluated the impact of microRNAs (miRNAs) on the proteome of human umbilical cord blood-derived unrestricted somatic stem cells (USSC) during retinoic acid (RA) differentiation by a systemic approach using next generation sequencing analysing mRNA and miRNA expression and quantitative mass spectrometry-based proteome analyses. Interestingly, regulation of mRNAs and their dedicated proteins highly correlated during RA-incubation. Additionally, RA-induced USSC demonstrated a clear separation from native USSC thereby shifting from a proliferating to a metabolic phenotype. Bioinformatic integration of up- and downregulated miRNAs and proteins initially implied a strong impact of the miRNome on the XXL-USSC proteome. However, quantitative proteome analysis of the miRNA contribution on the final proteome after ectopic overexpression of downregulated miR-27a-5p and miR-221-5p or inhibition of upregulated miR-34a-5p, respectively, followed by RA-induction revealed only minor proportions of differentially abundant proteins. In addition, only small overlaps of these regulated proteins with inversely abundant proteins in non-transfected RA-treated USSC were observed. Hence, mRNA transcription rather than miRNA-mediated regulation is the driving force for protein regulation upon RA-incubation, strongly suggesting that miRNAs are fine-tuning regulators rather than active primary switches during RA-induction of USSC.

Recent advances in understanding resistance exercise training-induced skeletal muscle hypertrophy in humans

Skeletal muscle plays a pivotal role in the maintenance of physical and metabolic health and, critically, mobility. Accordingly, strategies focused on increasing the quality and quantity of skeletal muscle are relevant, and resistance exercise is foundational to the process of functional hypertrophy. Much of our current understanding of skeletal muscle hypertrophy can be attributed to the development and utilization of stable isotopically labeled tracers. We know that resistance exercise and sufficient protein intake act synergistically and provide the most effective stimuli to enhance skeletal muscle mass; however, the molecular intricacies that underpin the tremendous response variability to resistance exercise-induced hypertrophy are complex. The purpose of this review is to discuss recent studies with the aim of shedding light on key regulatory mechanisms that dictate hypertrophic gains in skeletal muscle mass. We also aim to provide a brief up-to-date summary of the recent advances in our understanding of skeletal muscle hypertrophy in response to resistance training in humans.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors

Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleotide polymorphisms on its protein-coding sequences could result in undesired consequences in human. Therefore, it is necessary to develop methods for predicting any abnormality among those non-synonymous single nucleotide polymorphisms. To address it, we have developed and compared different strategies to predict deleterious non-synonymous single nucleotide polymorphisms (also known as missense mutations) on the protein-coding sequences of human TFs. Taking advantage of evolutionary conservation signals, we have developed and compared different classifiers with different feature sets as computed from different evolutionarily related sequence collections. The results indicate that the classic ensemble algorithm, Adaboost with decision stumps, with orthologous sequence collection, has performed the best (namely, TFmedic). We have further compared TFmedic with other state-of-the-arts methods (i.e., PolyPhen-2 and SIFT) on PolyPhen-2's own datasets, demonstrating that TFmedic can outperform the others. As applications, we have further applied TFmedic to all possible missense mutations on all human transcription factors; the proteome-wide results reveal interesting insights, consistent with the existing physiochemical knowledge. A case study with the actual 3D structure is conducted, revealing how TFmedic can be contributed to protein-DNA binding complex studies.

New Technologies to Image Tumors

The premise of this book is the importance of the tumor microenvironment (TME). Until recently, most research on and clinical attention to cancer biology, diagnosis, and prognosis were focused on the malignant (or premalignant) cellular compartment that could be readily appreciated using standard morphology-based imaging.

Peptides encoded by noncoding genes: challenges and perspectives

In recent years, noncoding gene (NCG) translation events have been frequently discovered. The resultant peptides, as novel findings in the life sciences, perform unexpected functions of increasingly recognized importance in many fundamental biological and pathological processes. The emergence of these novel peptides, in turn, has advanced the field of genomics while indispensably aiding living organisms. The peptides from NCGs serve as important links between extracellular stimuli and intracellular adjustment mechanisms. These peptides are also important entry points for further exploration of the mysteries of life that may trigger a new round of revolutionary biotechnological discoveries. Insights into NCG-derived peptides will assist in understanding the secrets of life and the causes of diseases, and will also open up new paths to the treatment of diseases such as cancer. Here, a critical review is presented on the action modes and biological functions of the peptides encoded by NCGs. The challenges and future trends in searching for and studying NCG peptides are also critically discussed.

Homeostasis in the Central Dogma of molecular biology: the importance of mRNA instability

Cell survival requires the control of biomolecule concentration, i.e. biomolecules should approach homeostasis. With information-carrying macromolecules, the particular concentration variation ranges depend on each type: DNA is not buffered, but mRNA and protein concentrations are homeostatically controlled, which leads to the ribostasis and proteostasis concepts. In recent years, we have studied the particular features of mRNA ribostasis and proteostasis in the model organism S. cerevisiae. Here we extend this study by comparing published data from three other model organisms: E. coli, S. pombe and cultured human cells. We describe how mRNA ribostasis is less strict than proteostasis. A constant ratio appears between the average decay and dilution rates during cell growth for mRNA, but not for proteins. We postulate that this is due to a trade-off between the cost of synthesis and the response capacity. This compromise takes place at the transcription level, but is not possible at the translation level as the high stability of proteins, versus that of mRNAs, precludes it. We hypothesize that the middle-place role of mRNA in the Central Dogma of Molecular Biology and its chemical instability make it more suitable than proteins for the fast changes needed for gene regulation.

Integrative Analysis of Transcriptomic and Proteomic Profiling in Inflammatory Bowel Disease Colon Biopsies

BACKGROUND

Crohn's disease (CD) and ulcerative colitis (UC) are intestinal chronic inflammatory conditions characterized by altered epithelial barrier function and tissue damage. Despite significant efforts to understanding the biological mechanisms responsible for gut inflammation, the pathophysiology of CD and UC remains poorly understood.

METHODS

To help elucidate the potential mechanisms responsible for gut inflammation in CD and UC, transcriptomic and proteomic profiling of human colon biopsy specimens was performed. Dysregulated genes and proteins in disease tissues compared with normal tissues were characterized from the expression profiles and further subjected to pathway analysis to identify altered biological processes and signaling pathways.

RESULTS

Sample analysis showed 4250 genes with matched protein expression and a wide range of correlation of RNA-protein abundance across samples. Pathway analysis of dysregulated genes and proteins in CD and UC showed alterations in immune and inflammatory responses, complement cascade, and the suppression of metabolic processes and PPAR signaling. In CD, increased T-helper cell differentiation and elevated toll-like receptor and JAK/STAT signaling were observed. Interestingly, increased MAPK signaling was only observed in UC. Weighted gene co-expression network analysis suggested a possible role of epigenetic regulation in UC. Of note, a large discrepancy between regulation of RNA and protein levels in inflamed colon samples was detected for previously identified biomarkers including MMP14 and LAMP1.

CONCLUSIONS

With the analysis of dysregulated genes and pathways, the present study unravels key mechanisms contributing to CD and UC pathogenesis and emphasizes that integrative analysis of multi-omics data sets can provide more insight into understanding complex disease mechanisms.

High expression level of peptidylprolyl isomerase A is correlated with poor prognosis of liver hepatocellular carcinoma

Peptidylprolyl isomerase A (PPIA) has been reported to be correlated with cancer. The present study investigated the prognostic values of PPIA expression levels in cancer by comparing different types of cancer using databases. High expression levels of PPIA were observed in 17 out of 17 cancer types compared with normal adjacent tissues. High expression levels of PPIA were associated with decreased overall survival in low grade glioma, acute myeloid leukemia, lung adenocarcinoma, skin cutaneous melanoma and liver hepatocellular carcinoma (LIHC). The prognostic effect of PPIA expression in LIHC was independent of tumor grade. High expression levels of PPIA were of particular prognostic value in stage 3, American Joint Committee on Cancer Tumor 3, hepatitis B virus negative and sorafenib-administered subgroups in LIHC. The expression level of PPIA was significantly associated with levels of basigin and signal transducer and activator of transcription 3, which may be major effectors of PPIA in the progression of the cancer.

Heterodimeric DNA motif synthesis and validations

Bound by transcription factors, DNA motifs (i.e. transcription factor binding sites) are prevalent and important for gene regulation in different tissues at different developmental stages of eukaryotes. Although considerable efforts have been made on elucidating monomeric DNA motif patterns, our knowledge on heterodimeric DNA motifs are still far from complete. Therefore, we propose to develop a computational approach to synthesize a heterodimeric DNA motif from two monomeric DNA motifs. The approach is sequentially divided into two components (Phases A and B). In Phase A, we propose to develop the inference models on how two DNA monomeric motifs can be oriented and overlapped with each other at nucleotide level. In Phase B, given the two monomeric DNA motifs oriented, we further propose to develop DNA-binding family-specific input-output hidden Markov models (IOHMMs) to synthesize a heterodimeric DNA motif. To validate the approach, we execute and cross-validate it with the experimentally verified 618 heterodimeric DNA motifs across 49 DNA-binding family combinations. We observe that our approach can even "rescue" the existing heterodimeric DNA motif pattern (i.e. HOXB2_EOMES) previously published on Nature. Lastly, we apply the proposed approach to infer previously uncharacterized heterodimeric motifs. Their motif instances are supported by DNase accessibility, gene ontology, protein-protein interactions, in vivo ChIP-seq peaks, and even structural data from PDB. A public web-server is built for open accessibility and scientific impact. Its address is listed as follows: http://motif.cs.cityu.edu.hk/custom/MotifKirin.

Quantitative principles of cis-translational control by general mRNA sequence features in eukaryotes

Background

General translational cis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements include mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate to control translation rates are not well understood.

Results

Here, we show that these sequence features specify 42–81% of the variance in translation rates in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Mus musculus, and Homo sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25–60 nucleotide segments within mRNA 5′ regions, that changes in tri-nucleotide frequencies between highly and poorly translated 5′ regions are correlated between all species, and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA.

Conclusions

Our work shows that general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that directs translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and within cis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell.

Electronic supplementary material

The online version of this article (10.1186/s13059-019-1761-9) contains supplementary material, which is available to authorized users.

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR.

The level at which a gene is expressed can have important influence on the phenotype of an organism, including its predisposition to develop diseases. One way to estimate gene expression is by quantifying the abundance of RNA. RNA-seq has become the method of choice to provide such estimates at the genomewide scale. However, the application of RNA-seq to HLA genes —key players in the immune adaptive response— has remained a rarely explored approach. This is due to the problem of mapping bias, which causes deficient read alignment at genes which are very polymorphic and different from the reference genome. This has motivated approaches that replace the single reference genome with personalized sequences, comprised of the individual’s specific HLA genotype. Here we explore the use of computational frameworks to obtain reliable expression levels for HLA genes from RNA-seq datasets. We present a pipeline in which the quantification of HLA expression is carried out using methods which account for HLA diversity, avoiding the biases of standard approaches. We then evaluate the impact of this form of quantifying HLA expression on downstream analyses. The pipeline also allows us to integrate information on eQTLs with expression levels at the HLA allele-level, which can help disentangle different contributions to disease phenotypes and help understand the regulatory architecture at the HLA region.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.

Translational Control in Stem Cells

Simultaneous measurements of mRNA and protein abundance and turnover in mammalian cells, have revealed that a significant portion of the cellular proteome is controlled by mRNA translation. Recent studies have demonstrated that both embryonic and somatic stem cells are dependent on low translation rates to maintain an undifferentiated state. Conversely, differentiation requires increased protein synthesis and failure to do so prevents differentiation. Notably, the low translation in stem cell populations is independent of the cell cycle, indicating that stem cells use unique strategies to decouple these fundamental cellular processes. In this chapter, we discuss different mechanisms used by stem cells to control translation, as well as the developmental consequences of translational deregulation.

Transcriptome Sequencing Unravels Potential Biomarkers at Different Stages of Cerebral Ischemic Stroke

Ischemic stroke, which accounts for 87% of all strokes, constitutes the leading cause of morbidity and mortality in China. Although the genetics and epigenetics of stroke have been extensively investigated, few studies have examined their relationships at different stages of stroke. This study assessed the characteristics of transcriptome changes at different stages of ischemic stroke using a mouse model of transient middle cerebral artery occlusion (tMCAO) and bioinformatics analyses. Cerebral cortex tissues from tMCAO mice at days 1, 3, 7, 14, and 28 were removed for RNA-Seq and small RNA-Seq library construction, sequencing, and bioinformatics analysis. We identified differentially expressed (DE) genes and miRNAs and revealed an association of the up-regulated or down-regulated DEmiRNAs with the correspondingly altered DEgene targets at each time point. In addition, different biological pathways were activated at different time points; thus, three groups of miRNAs were verified that may represent potential clinical biomarkers corresponding to days 1, 3, and 7 after ischemic stroke. Notably, this represents the first functional association of some of these miRNAs with stroke, e.g., miR-2137, miR-874-5p, and miR-5099. Together, our findings lay the foundation for the transition from a single-point, single-drug stroke treatment approach to multiple-time-point multi-drug combination therapies.

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias

Motivation

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins.

Results

We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology.

Availability and implementation

The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

The developing eye lens presents an exceptional paradigm for spatial transcriptomics. It is composed of highly organized long, slender transparent fiber cells, which differentiate from the edges of the anterior epithelium of the lens (equator), attended by high expression of crystallins, which generates transparency. Every fiber cell, therefore, is an optical unit whose refractive properties derive from its gene activity. Here, we probe this tangible relationship between the gene activity and the phenotype by studying the expression of all known 17 crystallins and 77 other non-crystallin genes in single fiber cells isolated from three states/regions of differentiation, allowing us to follow molecular progression at the single-cell level. The data demonstrate highly variable gene activity in cortical fibers, interposed between the nascent and the terminally differentiated fiber cell transcription. These data suggest that the so-called stochastic, highly heterogeneous gene activity is a regulated intermediate in the realization of a functional phenotype.

Seasonally sympatric but allochronic: differential expression of hypothalamic genes in a songbird during gonadal development

Allochrony, the mismatch of reproductive schedules, is one mechanism that can mediate sympatric speciation and diversification. In songbirds, the transition into breeding condition and gonadal growth is regulated by the hypothalamic-pituitary-gonadal (HPG) axis at multiple levels. We investigated whether the difference in reproductive timing between two seasonally sympatric subspecies of dark-eyed juncos (Junco hyemalis) was related to gene expression along the HPG axis. During the sympatric pre-breeding stage, we measured hypothalamic and testicular mRNA expression of candidate genes via qPCR in captive male juncos. For hypothalamic mRNA, we found our earlier breeding subspecies had increased expression of gonadotropin-releasing hormone (GnRH) and decreased expression of androgen receptor, oestrogen receptor alpha and mineralocorticoid receptor (MR). Subspecies did not differ in expression of hypothalamic gonadotropin-inhibitory hormone (GnIH) and glucocorticoid receptor (GR). While our earlier breeding subspecies had higher mRNA expression of testicular GR, subspecies did not differ in testicular luteinizing hormone receptor, follicle-stimulating hormone receptor or MR mRNA expression levels. Our findings indicate increased GnRH production and decreased hypothalamic sensitivity to sex steroid negative feedback as factors promoting differences in the timing of gonadal recrudescence between recently diverged populations. Differential gene expression along the HPG axis may facilitate species diversification under seasonal sympatry.

Ultrasensitive Quantification of Cytokine Proteins in Single Lymphocytes From Human Blood Following ex-vivo Stimulation

In this study we demonstrate the feasibility of direct, quantitative measurement of cytokine proteins in single human CD8 lymphocytes from fresh peripheral blood of healthy donors following a brief ex vivo stimulation. Cytokine-secreting cells were identified using cell surface “catch” reagents and single cell data were obtained by sorting of individual cytokine-secreting cells into 96 well plates containing lysis buffer followed by analysis using ultrasensitive immunoassays for interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). CD8 cells negative for cytokine production, as determined by the cell surface catch reagents were used as negative controls. Furthermore, studies were undertaken to compare the mean fluorescence intensity (MFI) values of cytokine staining by flow cytometry with the quantification of cytokines using the current method. This study demonstrates that it is feasible to quantify cytokines from individual primary cells. A shift from qualitative to quantitative determinations of cytokine protein levels in single cells will permit more precise and reproducible studies of heterogeneity in the immune system and can be accomplished with readily available instrumentation.