Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes

Multiomics unravels potential molecular switches in the C3 to CAM transition of Mesembryanthemum crystallinum

Mesembryanthemum crystallinum (common ice plant), a facultative CAM plant, shifts from C3 to CAM photosynthesis under salt stress, enhancing water use efficiency. Here we used transcriptomics, proteomics, and targeted metabolomics to profile molecular changes during the diel cycle of C3 to CAM transition. The results confirmed expected changes associated with CAM photosynthesis, starch biosynthesis and degradation, and glycolysis/gluconeogenesis. Importantly, they yielded new discoveries: 1) Transcripts displayed greater circadian regulation than proteins. 2) Oxidative phosphorylation and inositol methylation may play important roles in initiating the transition. 3) V-type H+-ATPases showed consistent transcriptional regulation, aiding in vacuolar malate uptake. 4) A protein phosphatase 2C, a major component in the ABA signaling pathway, may trigger the C3 to CAM transition. Our work highlights the potential molecular switches in the C3 to CAM transition, including the potential role of ABA signaling. SIGNIFICANCE: The common ice plant is a model facultative CAM plant, and under stress conditions it can shift from C3 to CAM photosynthesis within a three-day period. However, knowledge about the molecular changes during the transition and the molecular switches enabling the transition is lacking. Multi-omic analyses not only revealed the molecular changes during the transition, but also highlighted the importance of ABA signaling, inositol methylation, V-type H+-ATPase in initiating the shift. The findings may explain physiological changes and nocturnal stomatal opening, and inform future synthetic biology effort in improving crop water use efficiency and stress resilience.

Integrated multi-omics analysis reveals drought stress response mechanism in chickpea (Cicer arietinum L.)

Drought is one of the major constraints limiting chickpea productivity. To unravel complex mechanisms regulating drought response in chickpea, we generated transcriptomics, proteomics, and metabolomics datasets from root tissues of four contrasting drought-responsive chickpea genotypes: ICC 4958, JG 11, and JG 11+ (drought-tolerant), and ICC 1882 (drought-sensitive) under control and drought stress conditions. Integration of transcriptomics and proteomics data identified enriched hub proteins encoding isoflavone 4'-O-methyltransferase, UDP-d-glucose/UDP-d-galactose 4-epimerase, and delta-1-pyrroline-5-carboxylate synthetase. These proteins highlighted the involvement of pathways such as antibiotic biosynthesis, galactose metabolism, and isoflavonoid biosynthesis in activating drought stress response mechanisms. Subsequently, the integration of metabolomics data identified six metabolites (fructose, galactose, glucose, myoinositol, galactinol, and raffinose) that showed a significant correlation with galactose metabolism. Integration of root-omics data also revealed some key candidate genes underlying the drought-responsive "QTL-hotspot" region. These results provided key insights into complex molecular mechanisms underlying drought stress response in chickpea.

Multi-omics reveals largely distinct transcript- and protein-level responses to the environment in an intertidal mussel

Organismal responses to stressful environments are influenced by numerous transcript- and protein-level mechanisms, and the relationships between expression changes at these levels are not always straightforward. Here, we used paired transcriptomic and proteomic datasets from two previous studies from gill of the California mussel, Mytilus californianus, to explore how simultaneous transcript and protein abundance patterns may diverge under different environmental scenarios. Field-acclimatized mussels were sampled from two disparate intertidal sites; individuals from one site were subjected to three further treatments (common garden, low-intertidal or high-intertidal outplant) that vary in temperature and feeding time. Assessing 1519 genes shared between the two datasets revealed that both transcript and protein expression patterns differentiated the treatments at a global level, despite numerous underlying discrepancies. There were far more instances of differential expression between treatments in transcript only (1451) or protein only (226) than of the two levels shifting expression concordantly (68 instances). Upregulated expression of cilium-associated transcripts (likely related to feeding) was associated with relatively benign field treatments. In the most stressful treatment, transcripts, but not proteins, for several molecular chaperones (including heat shock proteins and endoplasmic reticulum chaperones) were more abundant, consistent with a threshold model for induction of translation of constitutively available mRNAs. Overall, these results suggest that the relative importance of transcript- and protein-level regulation (translation and/or turnover) differs among cellular functions and across specific microhabitats or environmental contexts. Furthermore, the degree of concordance between transcript and protein expression can vary across benign versus acutely stressful environmental conditions.

Long live the host! Proteomic analysis reveals possible strategies for parasitic manipulation of its social host

Parasites with complex life cycles often manipulate the phenotype of their intermediate hosts to increase the probability of transmission to their definitive hosts. Infection with Anomotaenia brevis, a cestode that uses Temnothorax nylanderi ants as intermediate hosts, leads to a multiple-fold extension of host lifespan and to changes in behaviour, morphology and colouration. The mechanisms behind these changes are unknown, as is whether the increased longevity is achieved through parasite manipulation. Here, we demonstrate that the parasite releases proteins into its host with functions that might explain the observed changes. These parasitic proteins make up a substantial portion of the proteome of the hosts' haemolymph, and thioredoxin peroxidase and superoxide dismutase, two antioxidants, exhibited the highest abundances among them. The largest part of the secreted proteins could not be annotated, indicating they are either novel or severely altered during recent coevolution to function in host manipulation. We also detected shifts in the hosts' proteome with infection, in particular an overabundance of vitellogenin-like A in infected ants, a protein that regulates division of labour in Temnothorax ants, which could explain the observed behavioural changes. Our results thus suggest two different strategies that might be employed by this parasite to manipulate its host: secreting proteins with immediate influence on the host's phenotype and altering the host's translational activity. Our findings highlight the intricate molecular interplay required to influence the phenotype of a host and point to potential signalling pathways and genes involved in parasite-host communication.

Human milk inhibits some enveloped virus infections, including SARS-CoV-2, in an intestinal model

Human milk is important for antimicrobial defense in infants and has well demonstrated antiviral activity. We evaluated the protective ability of human milk against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a human fetal intestinal cell culture model. We found that, in this model, human milk blocks SARS-CoV-2 replication, irrespective of the presence of SARS-CoV-2 spike-specific antibodies. Complete inhibition of both enveloped Middle East respiratory syndrome coronavirus and human respiratory syncytial virus infections was also observed, whereas no inhibition of non-enveloped enterovirus A71 infection was seen. Transcriptome analysis after 24 h of the intestinal monolayers treated with human milk showed large transcriptomic changes from human milk treatment, and subsequent analysis suggested that <i>ATP1A1</i> down-regulation by milk might be of importance. Inhibition of ATP1A1 blocked SARS-CoV-2 infection in our intestinal model, whereas no effect on EV-A71 infection was seen. Our data indicate that human milk has potent antiviral activity against particular (enveloped) viruses by potentially blocking the ATP1A1-mediated endocytic process.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

Comprehensive surfaceome profiling to identify and validate novel cell-surface targets in osteosarcoma

Immunoconjugates targeting cell-surface antigens have demonstrated clinical activity to enable regulatory approval in several solid and hematologic malignancies. We hypothesize that a rigorous and comprehensive surfaceome profiling approach to identify osteosarcoma-specific cell-surface antigens can similarly enable development of effective therapeutics in this disease. Herein, we describe an integrated proteomic and transcriptomic surfaceome profiling approach to identify cell-surface proteins that are highly expressed in osteosarcoma but minimally expressed on normal tissues. Using this approach, we identified targets that are highly expressed in osteosarcoma. Three targets, MT1-MMP, CD276, and MRC2, were validated as overexpressed in osteosarcoma. Further, we tested BT1769, an MT1-MMP-targeted Bicycle toxin conjugate, in osteosarcoma PDX models. The results showed BT1769 had encouraging anti-tumor activity, high affinity for its target and a favorable pharmacokinetic profile. This confirms the hypothesis that our approach identifies novel targets with significant therapeutic potential in osteosarcoma.

Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes

Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.

The AI for Scientific Discovery Network. PATTERNS (NEW YORK, N.Y.) 2021;2:100162. [PMID: 33511363 PMCID: PMC7815949 DOI: 10.1016/j.patter.2020.100162]

The Artificial Intelligence and Augmented Intelligence for Automated Investigation for Scientific Discovery Network+ (AI3SD) was established in response to the UK Engineering and Physical Sciences Research Council (EPSRC) late-2017 call for a Network+ to promote cutting-edge research in artificial intelligence to accelerate groundbreaking scientific discoveries. This article provides the philosophical, scientific, and technical underpinnings of the Network+, the history of the different domains represented in the Network+, and the specific focus of the Network+. The activities, collaborations, and research covered in the first year of the Network+ have highlighted the significant challenges in the chemistry and augmented and artificial intelligence space. These challenges are shaping the future directions of the Network+. The article concludes with a summary of the lessons learned in running this Network+ and introduces our plans for the future in a landscape redrawn by COVID-19, including rebranding into the AI 4 Scientific Discovery Network (www.ai4science.network).

Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality

The application of machine learning to inference problems in biology is dominated by supervised learning problems of regression and classification, and unsupervised learning problems of clustering and variants of low-dimensional projections for visualization. A class of problems that have not gained much attention is detecting outliers in datasets, arising from reasons such as gross experimental, reporting or labelling errors. These could also be small parts of a dataset that are functionally distinct from the majority of a population. Outlier data are often identified by considering the probability density of normal data and comparing data likelihoods against some threshold. This classical approach suffers from the curse of dimensionality, which is a serious problem with omics data which are often found in very high dimensions. We develop an outlier detection method based on structured low-rank approximation methods. The objective function includes a regularizer based on neighbourhood information captured in the graph Laplacian. Results on publicly available genomic data show that our method robustly detects outliers whereas a density-based method fails even at moderate dimensions. Moreover, we show that our method has better clustering and visualization performance on the recovered low-dimensional projection when compared with popular dimensionality reduction techniques.

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis

BACKGROUND

Analysis of high-throughput multi-'omics interactions across the hierarchy of expression has wide interest in making inferences with regard to biological function and biomarker discovery. Expression levels across different scales are determined by robust synthesis, regulation and degradation processes, and hence transcript (mRNA) measurements made by microarray/RNA-Seq only show modest correlation with corresponding protein levels.

RESULTS

In this work we are interested in quantitative modelling of correlation across such gene products. Building on recent work, we develop computational models spanning transcript, translation and protein levels at different stages of the H. sapiens cell cycle. We enhance this analysis by incorporating 25+ sequence-derived features which are likely determinants of cellular protein concentration and quantitatively select for relevant features, producing a vast dataset with thousands of genes. We reveal insights into the complex interplay between expression levels across time, using machine learning methods to highlight outliers with respect to such models as proteins associated with post-translationally regulated modes of action.

CONCLUSIONS

We uncover quantitative separation between modified and degraded proteins that have roles in cell cycle regulation, chromatin remodelling and protein catabolism according to Gene Ontology; and highlight the opportunities for providing biological insights in future model systems.

Type I and II PRMTs regulate catabolic as well as detoxifying processes in Aspergillus nidulans

In filamentous fungi, arginine methylation has been implicated in morphogenesis, mycotoxin biosynthesis, pathogenicity, and stress response although the exact role of this posttranslational modification in these processes remains obscure. Here, we present the first genome-wide transcriptome analysis in filamentous fungi that compared expression levels of genes regulated by type I and type II protein arginine methyltransferases (PRMTs). In Aspergillus nidulans, three conserved type I and II PRMTs are present that catalyze asymmetric or symmetric dimethylation of arginines. We generated a double type I mutant (ΔrmtA/rmtB) and a combined type I and type II mutant (ΔrmtB/rmtC) to perform genome-wide comparison of their effects on gene expression, but also to monitor putative overlapping activities and reciprocal regulations of type I and type II PRMTs in Aspergillus. Our study demonstrates, that rmtA and rmtC as type I and type II representatives act together as repressors of proteins that are secreted into the extracellular region as the majority of up-regulated genes are mainly involved in catabolic pathways that constitute the secretome of Aspergillus. In addition to a strong up-regulation of secretory genes we found a significant enrichment of down-regulated genes involved in processes related to oxidation-reduction, transmembrane transport and secondary metabolite biosynthesis. Strikingly, nearly 50% of down-regulated genes in both double mutants correspond to redox reaction/oxidoreductase processes, a remarkable finding in light of our recently observed oxidative stress phenotypes of ΔrmtA and ΔrmtC. Finally, analysis of nuclear and cytoplasmic extracts for mono-methylated proteins revealed the presence of both, common and specific substrates of RmtA and RmtC. Thus, our data indicate that type I and II PRMTs in Aspergillus seem to co-regulate the same biological processes but also specifically affect other pathways in a non-redundant fashion.

Perioperative inhibition of β-adrenergic and COX2 signaling in a clinical trial in breast cancer patients improves tumor Ki-67 expression, serum cytokine levels, and PBMCs transcriptome

Catecholamines and prostaglandins are secreted abundantly during the perioperative period in response to stress and surgery, and were shown by translational studies to promote tumor metastasis. Here, in a phase-II biomarker clinical trial in breast cancer patients (n = 38), we tested the combined perioperative use of the β-blocker, propranolol, and the COX2-inhibitor, etodolac, scheduled for 11 consecutive perioperative days, starting 5 days before surgery. Blood samples were taken before treatment (T1), on the mornings before and after surgery (T2&T3), and after treatment cessation (T4). Drugs were well tolerated. Results based on a-priori hypotheses indicated that already before surgery (T2), serum levels of pro-inflammatory IL-6, CRP, and IFNγ, and anti-inflammatory, cortisol and IL-10, increased. At T2 and/or T3, drug treatment reduced serum levels of the above pro-inflammatory cytokines and of TRAIL, as well as activity of multiple inflammation-related transcription factors (including NFκB, STAT3, ISRE), but not serum levels of cortisol, IL-10, IL-18, IL-8, VEGF and TNFα. In the excised tumor, treatment reduced the expression of the proliferation marker Ki-67, and positively affected its transcription factors SP1 and AhR. Exploratory analyses of transcriptome modulation in PBMCs revealed treatment-induced improvement at T2/T3 in several transcription factors that in primary tumors indicate poor prognosis (CUX1, THRa, EVI1, RORa, PBX1, and T3R), angiogenesis (YY1), EMT (GATA1 and deltaEF1/ZEB1), proliferation (GATA2), and glucocorticoids response (GRE), while increasing the activity of the oncogenes c-MYB and N-MYC. Overall, the drug treatment may benefit breast cancer patients through reducing systemic inflammation and pro-metastatic/pro-growth biomarkers in the excised tumor and PBMCs.

Metabolic Reprogramming of Clostridioides difficile During the Stationary Phase With the Induction of Toxin Production

The obligate anaerobe, spore forming bacterium Clostridioides difficile (formerly Clostridium difficile) causes nosocomial and community acquired diarrhea often associated with antibiotic therapy. Major virulence factors of the bacterium are the two large clostridial toxins TcdA and TcdB. The production of both toxins was found strongly connected to the metabolism and the nutritional status of the growth environment. Here, we systematically investigated the changes of the gene regulatory, proteomic and metabolic networks of C. difficile 630Δerm underlying the adaptation to the non-growing state in the stationary phase. Integrated data from time-resolved transcriptome, proteome and metabolome investigations performed under defined growth conditions uncovered multiple adaptation strategies. Overall changes in the cellular processes included the downregulation of ribosome production, lipid metabolism, cold shock proteins, spermine biosynthesis, and glycolysis and in the later stages of riboflavin and coenzyme A (CoA) biosynthesis. In contrast, different chaperones, several fermentation pathways, and cysteine, serine, and pantothenate biosynthesis were found upregulated. Focusing on the Stickland amino acid fermentation and the central carbon metabolism, we discovered the ability of C. difficile to replenish its favored amino acid cysteine by a pathway starting from the glycolytic 3-phosphoglycerate via L-serine as intermediate. Following the growth course, the reductive equivalent pathways used were sequentially shifted from proline via leucine/phenylalanine to the central carbon metabolism first to butanoate fermentation and then further to lactate fermentation. The toxin production was found correlated mainly to fluxes of the central carbon metabolism. Toxin formation in the supernatant was detected when the flux changed from butanoate to lactate synthesis in the late stationary phase. The holistic view derived from the combination of transcriptome, proteome and metabolome data allowed us to uncover the major metabolic strategies that are used by the clostridial cells to maintain its cellular homeostasis and ensure survival under starvation conditions.

Proteomic Evidence for In-Frame and Out-of-Frame Alternatively Spliced Isoforms in Human and Mouse

In order to find evidence for translation of alternatively spliced transcripts, especially those that result in a change in reading frame, we collected exon-skipping cases previously found by RNA-Seq and applied a computational approach to screen millions of mass spectra. These spectra came from seven human and six mouse tissues, five of which are the same between the two organisms: liver, kidney, lung, heart, and brain. Overall, we detected 4 percent of all exon-skipping events found in RNA-seq data, regardless of their effect on reading frame. The fraction of alternative isoforms detected did not differ between out-of-frame and in-frame events. Moreover, the fraction of identified alternative exon-exon junctions and constitutive junctions were similar. Together, our results suggest that both in-frame and out-of-frame translation may be actively used to regulate protein activity or localization.

Genome-Wide Posttranscriptional Dysregulation by MicroRNAs in Human Asthma as Revealed by Frac-seq

MicroRNAs are small noncoding RNAs that inhibit gene expression posttranscriptionally, implicated in virtually all biological processes. Although the effect of individual microRNAs is generally studied, the genome-wide role of multiple microRNAs is less investigated. We assessed paired genome-wide expression of microRNAs with total (cytoplasmic) and translational (polyribosome-bound) mRNA levels employing subcellular fractionation and RNA sequencing (Frac-seq) in human primary bronchoepithelium from healthy controls and severe asthmatics. Severe asthma is a chronic inflammatory disease of the airways characterized by poor response to therapy. We found genes (i.e., isoforms of a gene) and mRNA isoforms differentially expressed in asthma, with novel inflammatory and structural pathophysiological mechanisms related to bronchoepithelium disclosed solely by polyribosome-bound mRNAs (e.g., IL1A and LTB genes or ITGA6 and ITGA2 alternatively spliced isoforms). Gene expression (i.e., isoforms of a gene) and mRNA expression analysis revealed different molecular candidates and biological pathways, with differentially expressed polyribosome-bound and total mRNAs also showing little overlap. We reveal a hub of six dysregulated microRNAs accounting for ∼90% of all microRNA targeting, displaying preference for polyribosome-bound mRNAs. Transfection of this hub in bronchial epithelial cells from healthy donors mimicked asthma characteristics. Our work demonstrates extensive posttranscriptional gene dysregulation in human asthma, in which microRNAs play a central role, illustrating the feasibility and importance of assessing posttranscriptional gene expression when investigating human disease.

Omics Approaches for Identifying Physiological Adaptations to Genome Instability in Aging

DNA damage causally contributes to aging and age-related diseases. The declining functioning of tissues and organs during aging can lead to the increased risk of succumbing to aging-associated diseases. Congenital syndromes that are caused by heritable mutations in DNA repair pathways lead to cancer susceptibility and accelerated aging, thus underlining the importance of genome maintenance for withstanding aging. High-throughput mass-spectrometry-based approaches have recently contributed to identifying signalling response networks and gaining a more comprehensive understanding of the physiological adaptations occurring upon unrepaired DNA damage. The insulin-like signalling pathway has been implicated in a DNA damage response (DDR) network that includes epidermal growth factor (EGF)-, AMP-activated protein kinases (AMPK)- and the target of rapamycin (TOR)-like signalling pathways, which are known regulators of growth, metabolism, and stress responses. The same pathways, together with the autophagy-mediated proteostatic response and the decline in energy metabolism have also been found to be similarly regulated during natural aging, suggesting striking parallels in the physiological adaptation upon persistent DNA damage due to DNA repair defects and long-term low-level DNA damage accumulation occurring during natural aging. These insights will be an important starting point to study the interplay between signalling networks involved in progeroid syndromes that are caused by DNA repair deficiencies and to gain new understanding of the consequences of DNA damage in the aging process.

Post-translational processing targets functionally diverse proteins in Mycoplasma hyopneumoniae

Mycoplasma hyopneumoniae is a genome-reduced, cell wall-less, bacterial pathogen with a predicted coding capacity of less than 700 proteins and is one of the smallest self-replicating pathogens. The cell surface of M. hyopneumoniae is extensively modified by processing events that target the P97 and P102 adhesin families. Here, we present analyses of the proteome of M. hyopneumoniae-type strain J using protein-centric approaches (one- and two-dimensional GeLC–MS/MS) that enabled us to focus on global processing events in this species. While these approaches only identified 52% of the predicted proteome (347 proteins), our analyses identified 35 surface-associated proteins with widely divergent functions that were targets of unusual endoproteolytic processing events, including cell adhesins, lipoproteins and proteins with canonical functions in the cytosol that moonlight on the cell surface. Affinity chromatography assays that separately used heparin, fibronectin, actin and host epithelial cell surface proteins as bait recovered cleavage products derived from these processed proteins, suggesting these fragments interact directly with the bait proteins and display previously unrecognized adhesive functions. We hypothesize that protein processing is underestimated as a post-translational modification in genome-reduced bacteria and prokaryotes more broadly, and represents an important mechanism for creating cell surface protein diversity.

RiboAbacus: a model trained on polyribosome images predicts ribosome density and translational efficiency from mammalian transcriptomes

Fluctuations in mRNA levels only partially contribute to determine variations in mRNA availability for translation, producing the well-known poor correlation between transcriptome and proteome data. Recent advances in microscopy now enable researchers to obtain high resolution images of ribosomes on transcripts, providing precious snapshots of translation in vivo. Here we propose RiboAbacus, a mathematical model that for the first time incorporates imaging data in a predictive model of transcript-specific ribosome densities and translational efficiencies. RiboAbacus uses a mechanistic model of ribosome dynamics, enabling the quantification of the relative importance of different features (such as codon usage and the 5′ ramp effect) in determining the accuracy of predictions. The model has been optimized in the human Hek-293 cell line to fit thousands of images of human polysomes obtained by atomic force microscopy, from which we could get a reference distribution of the number of ribosomes per mRNA with unmatched resolution. After validation, we applied RiboAbacus to three case studies of known transcriptome-proteome datasets for estimating the translational efficiencies, resulting in an increased correlation with corresponding proteomes. RiboAbacus is an intuitive tool that allows an immediate estimation of crucial translation properties for entire transcriptomes, based on easily obtainable transcript expression levels.

Core oxidative stress response in Aspergillus nidulans

BACKGROUND

The b-Zip transcription factor AtfA plays a key role in regulating stress responses in the filamentous fungus Aspergillus nidulans. To identify the core regulons of AtfA, we examined genome-wide expression changes caused by various stresses in the presence/absence of AtfA using A. nidulans microarrays. We also intended to address the intriguing question regarding the existence of core environmental stress response in this important model eukaryote.

RESULTS

Examination of the genome wide expression changes caused by five different oxidative stress conditions in wild type and the atfA null mutant has identified a significant number of stereotypically regulated genes (Core Oxidative Stress Response genes). The deletion of atfA increased the oxidative stress sensitivity of A. nidulans and affected mRNA accumulation of several genes under both unstressed and stressed conditions. The numbers of genes under the AtfA control appear to be specific to a stress-type. We also found that both oxidative and salt stresses induced expression of some secondary metabolite gene clusters and the deletion of atfA enhanced the stress responsiveness of additional clusters. Moreover, certain clusters were down-regulated by the stresses tested.

CONCLUSION

Our data suggest that the observed co-regulations were most likely consequences of the overlapping physiological effects of the stressors and not of the existence of a general environmental stress response. The function of AtfA in governing various stress responses is much smaller than anticipated and/or other regulators may play a redundant or overlapping role with AtfA. Both stress inducible and stress repressive regulations of secondary metabolism seem to be frequent features in A. nidulans.

Outlier detection at the transcriptome-proteome interface

BACKGROUND

In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation.

RESULTS

Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii

BACKGROUND

Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii.

RESULTS

We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal.

CONCLUSIONS

In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.

Leveraging the complementary nature of RNA-Seq and shotgun proteomics data

RNA sequencing (RNA-Seq) and MS-based shotgun proteomics are powerful high-throughput technologies for identifying and quantifying RNA transcripts and proteins, respectively. With the increasing affordability of these technologies, many projects have started to apply both to the same samples to achieve a more comprehensive understanding of biological systems. A major analytical challenge for such integrative projects is how to effectively leverage the complementary nature of RNA-Seq and shotgun proteomics data. RNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration. Sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in cell and tissue samples and thus improve protein identification. Meanwhile, proteomics data provide essential confirmation of the validity and functional relevance of novel findings from RNA-Seq data. At the quantitative level, mRNA and protein levels are only modestly correlated, suggesting strong involvement of posttranscriptional regulation in controlling gene expression. Here, we review recent studies at the interface of RNA-Seq and proteomics data. We discuss goals, accomplishments, and challenges in RNA-Seq-based proteogenomics. We also examine the current status and future potential of parallel transcriptome and proteome quantification in revealing posttranscriptional regulatory mechanisms.