Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution

Authors: David R. Nelson, Khaled M. Hazzouri, Kyle J. Lauersen, Ashish Jaiswal, Amphun Chaiboonchoe, Alexandra Mystikou, Weiqi Fu, Sarah Daakour, Bushra Dohai, Amnah Alzahmi, David Nobles, Mark Hurd, Julie Sexton, Michael J. Preston, Joan Blanchette, Michael W. Lomas, Khaled M.A. Amiri, Kourosh Salehi-Ashtiani Source: Cell Host & Microbe (2021) DOI: 10.1016/j.chom.2020.12.005
Topics: microalgal genomics viral integration and endogenous viral elements comparative genomics halotolerance and environmental adaptation horizontal gene transfer protein family evolution freshwater versus saltwater microalgae algal phylogenomics giant viruses gene ontology enrichment

Abstract

Being integral primary producers in diverse ecosystems, microalgal genomes could be mined for ecological insights, but representative genome sequences are lacking for many phyla. We cultured and sequenced 107 microalgae species from 11 different phyla indigenous to varied geographies and climates. This collection was used to resolve genomic differences between saltwater and freshwater microalgae. Freshwater species showed domain-centric ontology enrichment for nuclear and nuclear membrane functions, while saltwater species were enriched in organellar and cellular membrane functions. Further, marine species contained significantly more viral families in their genomes (p = 8e–4). Sequences from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, Tupanvirus, and other viruses were found integrated into the genomes of algal from marine environments. These viral-origin sequences were found to be expressed and code for a wide variety of functions. Together, this study comprehensively defines the expanse of protein-coding and viral elements in microalgal genomes and posits a unified adaptive strategy for algal halotolerance.

Summary

This study presents the sequencing and analysis of 107 new microalgal genomes spanning 11 phyla, sourced from culture collections (UTEX, NCMA) and a novel isolate collection from New York University Abu Dhabi. Combined with 67 previously available algal genomes, the resulting dataset of 174 assemblies was used to conduct large-scale comparative genomics across microalgae from diverse environments, including saltwater, freshwater, and euryhaline habitats. Protein family (PFAM) domain counts were analyzed using Pearson's correlation, hierarchical clustering, and dimensionality reduction methods, revealing that microalgae cluster by environmental habitat rather than strict phylogenetic affiliation, indicating convergent functional evolution across distantly related lineages.

A central finding of the study is the systematic identification of over 91,757 viral family domain-containing coding sequences (VFAM-CDSs) across the algal genome collection, representing endogenous viral-origin proteins (EVOPs) derived from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, Tupanvirus, and other virus families. Marine microalgae harbored significantly more viral family domains than freshwater species (p = 8e–4). Transcriptomic data from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) confirmed that the majority of these VFAM-containing sequences are actively expressed. Saltwater-specific EVOPs were enriched for ion transporter and membrane-related functions, while freshwater EVOPs were more diverse and enriched for sugar and amino acid metabolism, consistent with relaxed selection in freshwater environments.

The study further demonstrates that each microalgal phylum carries a distinct repertoire of viral-origin sequences, and that species from shared ecological niches—such as open ocean picoeukaryotes or coral-associated dinoflagellates—cluster together based on their VFAM profiles irrespective of deep phylogenetic relationships. These patterns suggest that viral sequence acquisition has repeatedly shaped niche-specific biological processes in microalgae, including the reinforcement of cellular membranes in saltwater lineages. The data and assemblies are made publicly available, providing a resource for future ecological, evolutionary, and biotechnological research on microalgae.

Key Findings

• 107 new microalgal genomes from 11 phyla were sequenced, substantially expanding the available genomic resources for microalgae.
• Marine microalgae contained significantly more viral family (VFAM) domains in their genomes than freshwater species (p = 8e–4), with sequences from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, and Tupanvirus identified in algal genomes.
• Over 91,757 viral family domain-containing coding sequences (VFAM-CDSs) were identified across 184 algal genomes, and transcriptomic data confirmed that a majority of these are expressed under natural conditions.
• Saltwater species showed convergent enrichment in membrane-related protein families and ion transporter functions, while freshwater species were enriched in nuclear and nuclear membrane-related protein families, suggesting environment-driven functional divergence.
• Each microalgal phylum harbored a distinct collection of viral-origin sequences, and species sharing environmental niches clustered together by VFAM domain counts regardless of phylogenetic affiliation, indicating niche-driven viral sequence acquisition.

Methods

• Whole-genome sequencing using short reads, long reads, and linked reads
• De novo genome assembly (Platanus, ABySS)
• BUSCO genome quality assessment
• Hidden Markov Model (HMM)-based protein family (PFAM) and viral family (VFAM) annotation
• Pearson's correlation and hierarchical bi-clustering of domain count arrays
• t-distributed stochastic neighbor embedding (tSNE) and UMAP dimensionality reduction
• Gene ontology (GO) enrichment analysis
• BLASTP-based contamination screening
• Transcriptomic validation using MMETSP data
• Phylogenetic analysis of viral-origin genes

Organisms

Microalgae (diverse phyla), Chlorophyta, Ochrophyta, Rhodophyta, Haptophyta, Cercozoa, Dinophyta (Myzozoa), Euglenophyta, Heterokonta, Streptophyta, Chromerida, Cryptophyta, Pelagophyta, Emiliania huxleyi, Micromonas pusilla, Fragilariopsis cylindrus, Chlamydomonas, Chlorella autotrophica, Chlamydomonas nivalis, Porphyridium purpureum, Porphyra umbilicalis

Advances in microalgal research and engineering development

Authors: Weiqi Fu, David R Nelson, Alexandra Mystikou, Sarah Daakour, Kourosh Salehi-Ashtiani Source: Current Opinion in Biotechnology (2019) DOI: 10.1016/j.copbio.2019.05.013
Topics: microalgal genomics and transcriptomics genome editing in microalgae CRISPR/Cas9 and Cpf1 technologies photosynthesis engineering and optimization microalgal metabolic engineering value-added bioproduct production ORFeome resources and chemical DNA synthesis microalgal mutant libraries lipid and carotenoid biosynthesis non-model microalgae biotechnology

Abstract

Microalgae have been investigated for the photosynthetic production of natural products with industrial and biomedical applications. Their rapid growth offers an advantage over higher plants, while their complex metabolic capacities allow for the production of various molecules. Despite their potentials, molecular techniques are underdeveloped in microalgae compared to higher plants, fungi, and bacteria. However, recent advances in genome sequencing, strain development, and genome editing technologies, are providing thrust to enhance research on microalgal species that have branched out from several focal model organisms to encompass a great diversity of species. In this review, we highlight the recent, significant advances in microalgal research, with a focus on the development of new resources that can enhance work on model and non-model species.

Summary

This review surveys recent developments in microalgal research infrastructure, genome editing technologies, and bioengineering applications as of 2019. The authors outline three major genome sequencing initiatives that are substantially expanding the number of available microalgal genome sequences beyond the estimated 40-60 publicly available at the time of publication: the completed MMETSP transcriptome project covering over 140 marine species, the authors' own ALG-ALL-CODE project providing over 120 new genome assemblies, and the ongoing 10KP project targeting at least 3000 microalgal genomes. Complementing these genomic resources, the Chlamydomonas Library Project (CLiP) offers an insertional mutant collection enabling high-throughput reverse genetic studies, and advances in chemical DNA synthesis have enabled the near-complete ORFeome synthesis of Prochlorococcus marinus with a 99% success rate, outperforming conventional PCR-based cloning approaches.

Regarding genome editing, the review highlights that CRISPR-Cpf1 systems offer substantially improved editing efficiency in green microalgae such as C. reinhardtii compared to CRISPR-Cas9, achieving approximately 10% on-target homologous replacement versus 0.02% with Cas9-mediated non-homologous end-joining. In diatoms such as P. tricornutum, both TALE nucleases and CRISPR/Cas9 have been used to generate stable lipid-overproducing strains through targeted disruption of UDP-glucose pyrophosphorylase and other loci. The authors note that off-target effects and low efficiency remain challenges specific to green microalgae when using Cas9, whereas Cpf1 variants show more reliable performance in this group.

On the bioengineering side, the review describes approaches to improve photosynthetic efficiency and the production of value-added compounds. Truncated light-harvesting antennae achieved growth improvements of 30-44.5% in C. reinhardtii and Chlorella vulgaris, respectively, while intracellular spectral recompositioning in P. tricornutum yielded a 50% increase in photosynthetic efficiency by redirecting excess blue light to wavelengths captured by fucoxanthin. For value-added products, metabolic engineering efforts in diatoms and green algae have enhanced the production of omega-3 polyunsaturated fatty acids (DHA and EPA), carotenoids including fucoxanthin, and diterpenoids. The authors conclude that while carbon flux engineering into storage lipids and pigments has advanced considerably, improvements to fundamental photosynthetic efficiency remain comparatively underdeveloped, and that expanding genomic and genetic resources for non-model species will increasingly allow researchers to select production strains based on industrial suitability rather than experimental tractability.

Key Findings

• The number of publicly available microalgal sequenced genomes has reached an estimated 40-60, with three major sequencing initiatives underway including the MMETSP transcriptome project, the ALG-ALL-CODE project covering over 120 genomes, and the 10KP project targeting at least 3000 microalgal genomes.
• The CRISPR-Cpf1 system achieves approximately 10% on-target DNA replacement efficiency in Chlamydomonas reinhardtii, substantially higher than the 0.02% efficiency observed with CRISPR-Cas9 non-homologous end-joining in the same organism.
• Intracellular spectral recompositioning of light (ISR) in engineered Phaeodactylum tricornutum, achieved by expressing GFP to convert excess blue light to green light, resulted in a 50% increase in photosynthetic efficiency and biomass productivity.
• Chemical DNA synthesis of the nearly complete ORFeomes of Prochlorococcus marinus strains MED4 and NATL1A was completed with a 99% success rate, compared to approximately 70% success with conventional PCR-based ORFeome generation for Chlamydomonas.
• The Chlamydomonas Library Project (CLiP) insertional mutant library has enabled high-throughput reverse genetic screens, including the discovery of novel genes involved in lipid biosynthetic pathways.

Methods

• CRISPR/Cas9 genome editing
• CRISPR-Cpf1 (Cas12a) genome editing
• TALE nucleases and meganucleases
• Whole-genome sequencing and transcriptome sequencing
• Chemical DNA synthesis and ORFeome generation
• Insertional mutagenesis and mutant library construction
• RNAi-mediated gene knockdown
• Ribonucleoprotein (RNP) complex delivery
• Heterologous gene overexpression
• Genome-scale metabolic modeling
• Photobioreactor cultivation

Organisms

Chlamydomonas reinhardtii, Phaeodactylum tricornutum, Thalassiosira pseudonana, Nannochloropsis oceanica, Nannochloropsis gaditana, Ostreococcus tauri, Ostreococcus lucimarinus, Micromonas pusilla, Volvox carterii, Emiliania huxleyi, Chlorella vulgaris, Chlorella pyrenoidosa, Dunaliella bardawil, Prochlorococcus marinus, Tisochrysis lutea, Chromochloris zofingiensis, Nannochloris eukaryotum, Coccomyxa subellipsoidea, Trebouxia gelatinosa

DNA Methylation and Expression of the Genes Coding for Lactate Dehydrogenases A and C during Rodent Spermatogenesis

Authors: Acacia A. Alcivar, Jacquetta M. Trasler, Laura E. Hake, Kourosh Salehi-Ashtiani, Erwin Goldberg, Norman B. Hecht Source: Biology of Reproduction (1991) Topics: DNA methylation spermatogenesis lactate dehydrogenase isozymes gene expression regulation translational regulation testis-specific gene expression germ cell differentiation mRNA expression profiling epigenetics male reproductive biology

Abstract

The testis chromatin undergoes profound structural alterations and functional changes during spermatogenesis. Changes in DNA methylation have been correlated with gene expression in a number of systems, but the relationship between methylation and gene expression for testicular genes is unclear. To address this question, DNA methylation patterns and mRNA expression for a somatic form of lactate dehydrogenase (LDH), LDH-A, were compared with those of the testis-specific form, LDH-C, in preparations from testes of prepubertal and sexually mature mice, from isolated testicular cells, and from somatic tissues. At specific sites, LDH-A was less methylated in adult testis than in spleen DNA; the decreased methylation in the testicular DNA occurred as early as type A spermatogonia. In contrast, DNA methylation patterns for LDH-C did not differ between spleen and testis DNAs. In Northern blots, the levels of LDH-A transcripts were low in total testis RNA obtained from 6-12-day-old mice, and in type A and B spermatogonia from 8-day-old mice. LDH-A mRNA levels increased gradually in testes from 16-45-day-old mice. LDH-C transcripts were first detectable in the testes of 12-day-old mice and increased as spermatogenesis proceeded. Both LDH-A and LDH-C mRNA levels were low in preleptotene spermatocytes and leptotene/zygotene spermatocytes and increased substantially in pachytene spermatocytes and round spermatids. Reduced levels of LDH-A and LDH-C mRNAs were found in the residual bodies/cytoplasts fraction. Analysis of polysomal gradients from total testis indicated that although both LDH-A and LDH-C mRNAs are translationally regulated, a greater proportion of the LDH-C mRNA was present in polysomes. In summary, our results indicate that whereas DNA methylation of LDH-A and LDH-C at the 5'-CCGG-3' sites monitored here do not change markedly during testis development, both genes are temporally expressed.

Summary

This study investigated the relationship between DNA methylation and gene expression for two lactate dehydrogenase genes—the somatic LDH-A and the testis-specific LDH-C—during mouse spermatogenesis. Using Southern blot analysis with the methylation-sensitive restriction enzyme HpaII and its methylation-insensitive isoschizomer MspI, the authors examined methylation status at 5'-CCGG-3' sites in DNA isolated from prepubertal and adult testes, somatic tissues, and enriched populations of defined testicular cell types. For LDH-A, specific sites in the 5' and 3' flanking regions were found to be hypomethylated in testicular DNA relative to spleen DNA, and this difference was present as early as type A spermatogonia, indicating that the differential methylation is established prior to meiosis and is not a stage-specific event tied to transcriptional onset. For LDH-C, no differences in methylation were detected between any testicular cell type and somatic tissue across multiple enzyme combinations, demonstrating that this testis-specific gene is not regulated through differential methylation at the sites examined.

Northern blot analysis of RNA from staged prepubertal testes and isolated germ cell populations revealed that both LDH-A and LDH-C transcripts increase substantially during meiosis, with peak levels in pachytene spermatocytes and round spermatids and reduced levels in the residual bodies/cytoplasts fraction. LDH-A was detected at low levels in type A and B spermatogonia, whereas LDH-C transcripts were absent in spermatogonia and first detected at low levels around day 12 postnatally, coinciding with the appearance of primary spermatocytes. Two transcript size variants were observed for each gene, with differential abundance across cell types. In situ hybridization on adult testis sections confirmed the cell-type distribution of both mRNAs and showed that LDH-A expression is higher in primary spermatocytes than in spermatogonia or elongated spermatids.

Polysomal gradient fractionation from total testis extracts showed that both LDH-A and LDH-C mRNAs are subject to translational control, consistent with known post-meiotic translational regulation during spermatogenesis. A greater proportion of LDH-C mRNA was found in the polysomal fraction compared to LDH-A, suggesting gene-specific differences in translational efficiency. Taken together, these findings indicate that temporal regulation of LDH-A and LDH-C expression during spermatogenesis operates largely at transcriptional and translational levels, and that changes in DNA methylation at the 5'-CCGG-3' sites examined are not a primary mechanism driving the differential or developmental regulation of these two genes in the testis.

Key Findings

• LDH-A gene shows reduced methylation at specific 5'-CCGG-3' sites in testicular DNA compared to spleen, with this hypomethylation detectable as early as type A spermatogonia and persisting throughout spermatogenesis, yet this differential methylation does not directly correlate with transcriptional activation.
• LDH-C, the testis-specific lactate dehydrogenase gene, shows no detectable differences in DNA methylation patterns between testicular cell types and somatic tissue (spleen), indicating that hypomethylation is not a prerequisite for its tissue-specific expression.
• Both LDH-A and LDH-C mRNA levels are low in spermatogonia and early spermatocytes and peak in pachytene spermatocytes and round spermatids, with levels declining in the residual bodies/cytoplasts fraction.
• Polysomal gradient analysis demonstrates that both LDH-A and LDH-C mRNAs are subject to translational regulation, with a greater proportion of LDH-C mRNA associated with polysomes compared to LDH-A mRNA.
• In situ hybridization confirmed cell-type-specific expression patterns, showing higher LDH-A mRNA in primary spermatocytes compared to spermatogonia and elongated spermatids, and similar cell-type enrichment for LDH-C.

Methods

• Southern blot analysis
• Restriction enzyme digestion with methylation-sensitive enzymes (HpaII and MspI)
• Northern blot analysis
• In situ hybridization
• Unit gravity sedimentation for testicular cell isolation
• Polysomal gradient fractionation
• Random priming radiolabeled probe preparation
• Agarose gel electrophoresis
• Autoradiography
• Videometric grain counting for quantification of in situ hybridization

Organisms

Mus musculus (mouse), Rattus norvegicus (rat, source of LDH-A cDNA probe)

Microalgal metabolic network model refinement through high-throughput functional metabolic profiling

Authors: Amphun Chaiboonchoe, Bushra Saeed Dohai, Hong Cai, David R. Nelson, Kenan Jijakli, Kourosh Salehi-Ashtiani Source: Frontiers in Bioengineering and Biotechnology (2014) DOI: 10.3389/fbioe.2014.00068
Topics: genome-scale metabolic modeling phenotype microarray technology Chlamydomonas reinhardtii metabolism flux balance analysis metabolic network reconstruction microalgae metabolic phenotyping D-amino acid metabolism dipeptide and tripeptide nitrogen sources COBRA toolbox systems biology of algae

Abstract

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

Summary

This study presents a methodology for using Biolog OmniLog phenotype microarray (PM) technology to characterize the metabolic capabilities of the green microalga Chlamydomonas reinhardtii and to refine its genome-scale metabolic network model. The PM platform, previously applied only to bacteria and fungi, was adapted for algal use by optimizing inoculum concentration, dye selection, and pre-inoculation conditions. C. reinhardtii strain CC-503 was assayed across seven PM plates testing carbon, nitrogen, phosphorus, sulfur, and peptide sources, with respiration detected via tetrazolium dye reduction. Results were analyzed using the OPM package in R, and reproducibility was confirmed through duplicate experiments.

From the PM data, 128 metabolites not present in the existing iRC1080 model were identified as utilizable by C. reinhardtii, including 8 D-amino acids, 108 L-dipeptides, 5 L-tripeptides, and novel phosphorus sources including cysteamine-S-phosphate. A bioinformatics pipeline was developed to associate positive PM results with enzymatic reactions via EC numbers from KEGG and MetaCyc, followed by genomic evidence recovery from Phytozome, JGI, AUGUSTUS, and KEGG annotations, supplemented by PSI-BLAST searches against C. reinhardtii protein sequences where direct annotation was absent. Metabolites lacking genomic support (e.g., tetrathionate, thiophosphate, ethylamine) were excluded from model addition.

The iRC1080 model was expanded into iBD1106 by incorporating 254 new reactions and associated transport reactions, along with 20 new genes. The updated model contains 2,445 reactions, 1,959 metabolites, and 1,106 genes. Flux balance analyses under light and dark conditions were used to evaluate the impact of these additions on network flux distribution via shadow price comparisons. The pipeline developed here provides a reproducible framework for functionally characterizing the metabolism of C. reinhardtii and other microalgae, and for using that data to systematically improve the accuracy and completeness of genome-scale metabolic models.

Key Findings

• Phenotype microarray (PM) assays were successfully adapted for metabolic phenotyping of the green microalga Chlamydomonas reinhardtii, representing the first reported application of this technology to microalgae.
• 128 new metabolites not present in the existing iRC1080 model were identified, including 8 D-amino acids, 108 dipeptides, 5 tripeptides, and several novel phosphorus and sulfur sources such as cysteamine-S-phosphate.
• The existing C. reinhardtii genome-scale metabolic model iRC1080 was expanded into iBD1106 by adding 254 reactions (20 amino acid reactions, 108 dipeptide reactions, 5 tripeptide reactions, and 120 transport reactions), increasing the model to 2,445 reactions, 1,959 metabolites, and 1,106 genes.
• Acetic acid was confirmed as the sole positive carbon source under the assay conditions, consistent with known C. reinhardtii heterotrophic metabolism, validating the specificity of the PM approach.
• A bioinformatics pipeline integrating PM assay results with KEGG, MetaCyc, PSI-BLAST, and multiple genomic annotation databases was established to systematically link phenotypic observations to gene-reaction associations for model refinement.

Methods

• Biolog OmniLog phenotype microarray (PM) assays
• Flux balance analysis (FBA)
• COBRA Toolbox for metabolic modeling
• OmniLog Phenotype Microarray (OPM) R software package
• PSI-BLAST homology searches
• KEGG and MetaCyc database queries
• Phytozome and JGI genomic annotation databases
• EMBL-EBI Pfam and InterPro protein domain prediction
• Gas chromatography time-of-flight (GC-TOF) metabolomics (for comparison)
• Spline-based curve fitting for kinetic data analysis

Organisms

Chlamydomonas reinhardtii

Saffron-Based Crocin Prevents Early Lesions of Liver Cancer: In vivo, In vitro and Network Analyses

Authors: Amr Amin, Alaaeldin A. Hamza, Sayel Daoud, Kamal Khazanehdari, Ala'a Al Hrout, Badriya Baig, Amphun Chaiboonchoe, Thomas E. Adrian, Nazar Zaki, Kourosh Salehi-Ashtiani Source: Recent Patents on Anti-Cancer Drug Discovery (2016) Topics: hepatocellular carcinoma chemoprevention crocin and saffron bioactivity NF-kB signaling in liver cancer apoptosis and cell cycle regulation inflammation and tumor microenvironment diethylnitrosamine-induced hepatocarcinogenesis gene expression network analysis HDAC activity in cancer natural product anticancer agents HepG2 cell line studies

Abstract

Background: The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carcinoma (HCC). Only recently patents of VEGF receptors-3 inhibitors are developed. Thus, a novel approach against HCC is essential for a better therapeutic outcome. Objective: The aims of this study were to examine the chemopreventive action of saffron's main biomolecule, crocin, against chemically-induced liver cancer in rats, and to explore the mechanisms by which crocin employs its anti-tumor effects. Method: We investigated the anti-cancer effect of crocin on an experimental carcinogenesis model of liver cancer by studying the anti-oxidant, anti-inflammatory, anti-proliferation, pro-apoptotic activities of crocin in vivo. In addition, we provided a network analysis of differentially expressed genes in tissues of animals pre-treated with crocin in comparison to induced-HCC animals' tissues. To further support our results, in vitro analysis was carried out. We assessed the effects of crocin on HepG2 cells viability by treating them with various concentrations of crocin; in addition, effects of crocin on cell cycle distribution of HepG2 cells were investigated. Results: Findings reported herein demonstrated the anti-proliferative and pro-apoptotic properties of crocin when administrated in induced-HCC model. Crocin exhibited anti-inflammatory properties where NF-kB, among other inflammatory markers, was inhibited. In vitro analysis confirmed crocin's effect in HepG2 by arresting the cell cycle at S and G2/M phases, inducing apoptosis and down regulating inflammation. Network analysis identified NF-kB as a potential regulatory hub, and therefore, a candidate therapeutic drug target. Conclusion: Taken together, our findings introduce crocin as a candidate chemopreventive agent against HCC.

Summary

This study examined the chemopreventive potential of crocin, the primary bioactive carotenoid of saffron (Crocus sativus), against hepatocellular carcinoma (HCC) using a chemically induced rat model, HepG2 cell culture experiments, and gene expression network analysis. In vivo, hepatocarcinogenesis was initiated with diethylnitrosamine (DEN) and promoted with 2-acetylaminofluorene (2-AAF) in adult male Wistar rats. Animals pre-treated with low (100 mg/kg) or high (200 mg/kg) doses of crocin showed marked reductions in pre-neoplastic foci of altered hepatocytes, as evidenced by decreased GST-p positive foci and Ki-67 expression, along with increased M30 CytoDeath-positive apoptotic cells. Crocin also restored elevated HDAC activity to near-normal levels and suppressed inflammatory markers including TNF-α, COX-2, iNOS, NF-kB-p65 nuclear translocation, and macrophage/Kupffer cell activity markers.

In vitro experiments using HepG2 cells corroborated the in vivo findings. Crocin reduced cell viability in a dose-dependent manner, arrested the cell cycle at S and G2/M phases over 24–72 hours, decreased IL-8 secretion within 6 hours, and reduced TNFR1 protein levels by 48 hours. These results collectively indicate that crocin's anticancer effects involve modulation of both cell proliferation and inflammatory signaling pathways.

Network analysis of 29 differentially expressed genes, selected from a panel of 160 genes related to apoptosis and inflammation, identified NF-kB1 as a highly connected hub in the protein interaction network and CCL20 as the gene with the largest fold change in expression. Gene Ontology enrichment analysis revealed overrepresentation of immune response and stress response biological processes among the differentially expressed genes. These findings support a mechanistic role for NF-kB-mediated inflammatory signaling in the anticancer activity of crocin and suggest NF-kB as a candidate therapeutic target in HCC chemoprevention.

Key Findings

• Crocin significantly reduced the number of GST-p positive foci and Ki-67-expressing hepatocytes in DEN/2-AAF-induced hepatocarcinogenesis in rats, indicating suppression of early pre-neoplastic lesions.
• Crocin inhibited NF-kB translocation to the nucleus and reduced levels of inflammatory markers including TNF-α, COX-2, iNOS, and macrophage activity markers ED-1 and ED-2 in vivo.
• In vitro treatment of HepG2 cells with crocin caused dose-dependent reduction in cell viability, arrested the cell cycle at S and G2/M phases, decreased IL-8 secretion, and reduced TNFR1 protein levels.
• Crocin restored normal HDAC activity levels that had been elevated by chemical induction of hepatocarcinogenesis.
• Network analysis of 29 differentially expressed genes identified NF-kB1 as a key hub and CCL20 as the gene with the highest observable fold change (-4.91), linking inflammatory and apoptotic pathways affected by crocin treatment.

Methods

• DEN/2-AAF rat hepatocarcinogenesis model
• Hematoxylin and eosin histopathological staining
• Immunohistochemical staining (GST-p, Ki-67, M30 CytoDeath, NF-kB-p65, COX-2, iNOS, p-TNF-R1, ED-1, ED-2)
• ELISA for TNF-α quantification
• HDAC activity assay
• CellTiter-Glo cell viability assay
• Flow cytometry for cell cycle analysis
• Real-time gene expression profiling
• STRING database network and GO term enrichment analysis
• One-way ANOVA with Dunnett's t test

Organisms

Rattus norvegicus (Wistar rat), Homo sapiens (HepG2 hepatoma cell line)

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Authors: Roser Corominas, Xinping Yang, Guan Ning Lin, Shuli Kang, Yun Shen, Lila Ghamsari, Martin Broly, Maria Rodriguez, Stanley Tam, Shelly A. Trigg, Changyu Fan, Song Yi, Murat Tasan, Irma Lemmens, Xingyan Kuang, Nan Zhao, Dheeraj Malhotra, Jacob J. Michaelson, Vladimir Vacic, Michael A. Calderwood, Frederick P. Roth, Jan Tavernier, Steve Horvath, Kourosh Salehi-Ashtiani, Dmitry Korkin, Jonathan Sebat, David E. Hill, Tong Hao, Marc Vidal, Lilia M. Iakoucheva Source: Nature Communications (2014) DOI: 10.1038/ncomms4650
Topics: autism spectrum disorder genetics alternative splicing and isoforms protein-protein interaction networks copy number variations yeast two-hybrid screening brain-expressed transcripts disease interactome mapping spliceform-specific interactions neurodevelopmental disorders interactome network validation

Abstract

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

Summary

This study presents the Autism Spliceform Interaction Network (ASIN), a protein-protein interaction (PPI) network constructed by experimentally testing multiple brain-expressed alternatively spliced isoforms of 191 autism spectrum disorder (ASD) candidate genes. Using RNA from pooled fetal and adult human brain tissue, the authors cloned 422 splicing isoforms representing 168 genes, more than 60% of which were novel relative to existing sequence databases. These isoforms were screened by yeast two-hybrid assays against the human ORFeome (~15,000 ORFs) and against each other, producing 629 isoform-level interactions (506 gene-level PPIs), the vast majority of which were not previously reported in literature-curated interaction databases.

A key observation is that screening only canonical reference isoforms would have missed approximately 46% of isoform-level interactions, underscoring that isoform diversity substantially shapes the protein interaction landscape. For example, differential inclusion of specific exons in the A2BP1 gene directly correlated with distinct sets of interaction partners. The network was validated in a mammalian orthogonal assay (MAPPIT) at a precision of ~89%, and interacting pairs showed statistically significant enrichment for coexpression, shared transcription factor binding sites, shared Gene Ontology terms, and structural co-complex occurrence.

The authors further demonstrate that ASIN prey proteins are 1.5-fold enriched for genes located within de novo autism CNVs relative to a general human interactome, suggesting that proteins encoded at distinct CNV loci tend to physically interact with one another. This isoform-resolved interaction network provides a more detailed representation of the molecular connectivity among ASD risk factors than single-isoform or literature-derived networks, and illustrates how incorporating tissue-specific splice variants can reveal disease-relevant protein interactions that would otherwise remain undetected.

Key Findings

• A total of 422 brain-expressed splicing isoforms from 168 autism candidate genes were cloned and screened via yeast two-hybrid assays, yielding 629 isoform-level protein-protein interactions (506 gene-level PPIs), of which 91.5% were novel relative to literature-curated interaction datasets.
• Approximately 46% of isoform-level PPIs and 33% of gene-level PPIs would not have been detected had only the reference isoform of each gene been screened, demonstrating that non-reference isoforms substantially expand the interaction network.
• Over 60% of the cloned brain-expressed isoforms were novel relative to six public sequence databases, with the majority generated through bounded or shuffled exon usage.
• Proteins encoded by de novo autism copy number variation (CNV) loci were 1.5-fold enriched among ASIN interaction partners compared with a general human interactome dataset, indicating physical connectivity between distinct CNV risk loci.
• ASIN interactions were validated at a rate comparable to a positive reference set in the orthogonal mammalian MAPPIT assay, and interacting pairs were significantly enriched for coexpression, coregulation, shared Gene Ontology annotations, and structural co-complex membership.

Methods

• High-throughput splice isoform discovery and cloning from pooled fetal and adult human brain RNA
• Deep-well next-generation sequencing for isoform identification
• Yeast two-hybrid (Y2H) screening against human ORFeome 5.1 (~15,000 ORFs)
• Sanger sequencing confirmation of Y2H hits
• Pairwise Y2H retesting for interaction validation
• Mammalian protein-protein interaction trap assay (MAPPIT) for orthogonal validation
• Gene Ontology enrichment analysis
• Structural co-complex analysis using experimentally solved and homology-modelled structures
• Coexpression and coregulation analysis
• Fisher's exact test and Wilcoxon rank sum tests for statistical comparisons

Organisms

Homo sapiens

Bioactive Compounds From Microalgae: Current Development and Prospects

Authors: W. Fu, D.R. Nelson, Z. Yi, M. Xu, B. Khraiwesh, K. Jijakli, A. Chaiboonchoe, A. Alzahmi, D. Al-Khairy, S. Brynjolfsson, K. Salehi-Ashtiani Source: Studies in Natural Products Chemistry, Vol. 54 (Elsevier) (2017) DOI: 10.1016/B978-0-444-63929-5.00006-1
Topics: microalgae-derived bioactive compounds carotenoid biosynthesis and extraction polyunsaturated fatty acids (PUFAs) from microalgae bioprospecting for new algal species antioxidant, antimicrobial, antiviral, and anticancer screening assays extraction methods for microalgal natural products medicinal natural products from cyanobacteria and green algae immunomodulatory compounds from microalgae metabolic phenotyping of microalgae pharmaceutical applications of algal secondary metabolites

Abstract

Microalgae have drawn great attention as a promising source for the sustainable production of various bioactive compounds, including fatty acids, phycobiliproteins, chlorophylls, carotenoids, and vitamins that can be widely used in pharmaceuticals, cosmetics, food additives and ingredients. The natural bioactive compounds from microalgae are attractive as research targets and may have great possibilities for commercialization due to their potential therapeutic activities, including antioxidant, antiviral, antibacterial, antifungal, anti-inflammatory, antitumor, and antimalarial effects. This chapter covers common pathways for biosynthesis of bioactive compounds such as carotenoids and their derivatives in representative species of green algae and diatoms, discusses biological activities with a focus on human nutrition and health, reviews structural properties and dose-activity relationships, and concludes that more efforts should be invested into algal research focused on developing bioactive compounds for human health.

Summary

This book chapter provides a structured review of bioactive compounds derived from microalgae, covering the principal taxonomic groups of interest, key compounds, extraction methodologies, and bioassay platforms used for primary screening. The authors discuss major genera including Haematococcus, Dunaliella, Chlorella, Chlamydomonas, and diatoms such as Phaeodactylum, cataloguing their capacity to produce commercially relevant compounds including carotenoids (astaxanthin, beta-carotene, fucoxanthin, lutein), polyunsaturated fatty acids (EPA and DHA), polysaccharides, and various secondary metabolites with demonstrated biological activities. A comparative table of production species and compound yields is provided, alongside discussion of biosynthetic pathways distinguishing primary from secondary carotenoids and contrasting pathways for astaxanthin synthesis across different algal genera.

The chapter addresses the full workflow from organism discovery to bioactive compound characterization. Bioprospecting approaches for identifying new strains are discussed, including the use of the Biolog Phenotype Microarray system for high-throughput metabolic phenotyping. Extraction method selection is treated in detail, with comparisons among supercritical fluid extraction, pressurized fluid extraction, microwave-assisted extraction, ultrasound-assisted extraction, and conventional solvent-based approaches. The authors emphasize that solvent and method selection must be optimized on a matrix- and compound-specific basis, with ethanol emerging as a particularly effective solvent for fucoxanthin across multiple extraction platforms. Screening assays for antioxidant, antimicrobial, antiviral, anticancer, and immunomodulatory activities are described with reference to their mechanistic bases and practical limitations.

The authors situate microalgae as an underutilized resource relative to terrestrial plants, noting that the estimated chemical diversity of algal secondary metabolites exceeds that of land plants by more than an order of magnitude, while only a small fraction of extant species have been cultured or chemically characterized. Specific compounds of pharmaceutical relevance are highlighted, including cyanovirin-N for anti-HIV activity, dolastatin 10 and its synthetic analog auristatin PE for anticancer applications, and sulfated polysaccharides with broad antiviral properties. The chapter concludes that continued investment in algal bioprospecting, metabolic modeling, extraction optimization, and bioassay development is needed to realize the potential of microalgae as sources of health-relevant natural products.

Key Findings

• The diversity of bioactive compounds produced by algal species is estimated to exceed that of land plants by more than 10-fold, yet microalgae remain largely underexplored as a source of medicinally important natural products.
• Key carotenoids including astaxanthin (from Haematococcus pluvialis, up to 8% dry weight), beta-carotene (from Dunaliella salina, up to 10% dry weight), and fucoxanthin (from Phaeodactylum tricornutum at 16.5 mg/g and Odontella aurita at 18.5 mg/g dry weight) represent commercially significant microalgal products with documented antioxidant, anti-inflammatory, antiobesity, antidiabetic, and antimalarial activities.
• Polyunsaturated fatty acids EPA and DHA from diatoms can account for 0.7–6.1% and 17.5–30.2% of total fatty acids respectively, with total lipid content reaching up to 57.8% of dry cell weight, positioning microalgae as a sustainable alternative to fish oil-based PUFA production.
• Advanced extraction techniques including supercritical fluid extraction, pressurized fluid extraction, ultrasound-assisted extraction, and microwave-assisted extraction offer improved selectivity, reduced solvent consumption, and higher efficiency compared to conventional methods, with ethanol consistently identified as an effective solvent for fucoxanthin recovery.
• Multiple bioassay platforms—including antioxidant (FRAP, TEAC, AIOLA), antimicrobial (agar diffusion), antiviral (plaque formation), anticancer (MTT, sulforhodamine B), and immunomodulatory (macrophage/cytokine) assays—have been applied to characterize microalgal extracts, with compounds such as cyanovirin-N, calcium spirulan, dolastatin 10, and sulfated polysaccharides demonstrating notable bioactivity.

Methods

• Supercritical fluid extraction (SFE) with SC-CO2
• Pressurized fluid extraction (PFE)
• Ultrasound-assisted extraction
• Microwave-assisted extraction
• Maceration-based solvent extraction
• Soxhlet extraction
• Gas chromatography–mass spectrometry (GC-MS)
• High performance liquid chromatography with diode array detector (HPLC-DAD)
• Liquid chromatography–mass spectrometry (LC-MS)
• Nuclear magnetic resonance (NMR) spectroscopy
• Biolog Phenotype Microarray (PM) for metabolic phenotyping
• Agar diffusion antimicrobial assay
• MTT and sulforhodamine B cytotoxicity assays
• Lactate dehydrogenase enzymatic assay
• Antioxidant assays (FRAP, TEAC, AIOLA)
• Bioassay-guided fractionation

Organisms

Chlamydomonas reinhardtii, Haematococcus pluvialis, Dunaliella salina, Chlorella pyrenoidosa, Chlorella zofingiensis, Chlamydomonas nivalis, Phaeodactylum tricornutum, Arthrospira platensis (Spirulina), Nannochloropsis gaditana, Crypthecodinium cohnii, Odontella aurita, Cylindrotheca closterium, Skeletonema costatum, Pseudonitzschia sp., Nostoc ellipsosporum, Nostoc flagelliforme, Synechocystis sp., Synechococcus sp., Symploca sp., Isochrysis sp., Monodus subterraneus, Porphyridium cruentum, Rhizosolenia setigera, Thalassiosira stellaris, Chondria armata

Bioinspired cell silicification of the model diatom Phaeodactylum tricornutum and its effects on cell metabolism

Authors: Jiwei Chen, Cheng Qian, Yuexuan Shu, Kourosh Salehi-Ashtiani, Jin Shang, Hangjin Jiang, Weiqi Fu Source: Sustainable Horizons (2025) DOI: 10.1016/j.horiz.2024.100127
Topics: diatom biosilicification single-cell transcriptomics Phaeodactylum tricornutum cell morphology biomimetic silica encapsulation photosynthesis and pigment metabolism iron metabolism in microalgae environmental stress resistance cell differentiation and trajectory analysis hybrid living materials R5 peptide-catalyzed silicification

Abstract

Biosilicification enhances the mechanical strength and chemical stability of organisms. Diatoms are the natural model for studying cell silicification, with the model diatom Phaeodactylum tricornutum being known as the only species that could transition from slightly silicified cells to silicified cells under environmental stress. In this study, single-cell sequencing was employed to investigate the wild-type P. tricornutum strain (WT-Pt) without cell silicification and the engineered strain (SG-Pt) with silicified cells. Our results indicate that SG-Pt exhibits clearly cellular clustering and enhanced iron metabolic function compared to WT-Pt. We further utilize biomimetic techniques to explore the impact of artificial silicification on P. tricornutum. The silicified cells show enhanced resistance to freezing and UVC irradiation conditions. Transcriptomic analysis demonstrated the up-regulation of photosynthesis with pigment accumulation in silicified cells. This work reveals key characteristics of diatoms under artificial biosilicification and provides critical insights into cell metabolism for promoting the development of hybrid living materials, which aligns with the United Nations sustainable development goal (SDG) 12 (Responsible Consumption and Production) by promoting sustainable biomaterials, and SDG 13 (Climate Action) by enhancing carbon sequestration efforts.

Summary

This study investigates the transcriptomic and metabolic consequences of cell silicification in the marine diatom Phaeodactylum tricornutum using both a genetically engineered silicified strain (SG-Pt, overexpressing GPCR1A) and wild-type cells (WT-Pt) subjected to artificial biosilicification. Single-cell RNA sequencing of approximately 11,600 total cells revealed three distinct transcriptional clusters, with SG-Pt cells predominantly occupying a separate cluster characterized by downregulated photosynthesis, respiration, and protein synthesis, alongside elevated expression of iron starvation-inducible proteins—a finding not previously captured by bulk RNA-seq. Cellular trajectory analysis further reconstructed a differentiation continuum from WT-Pt toward SG-Pt, with LHCF15 (a light-harvesting complex gene) showing progressive downregulation, consistent with prior reports of chloroplast disintegration in oval-form P. tricornutum.

To examine the functional consequences of exogenous silicification, the authors coated WT-Pt cells with biosilica by adsorbing the silaffin-derived R5 peptide onto the negatively charged cell surface, followed by R5-catalyzed hydrolysis and polymerization of tetramethoxysilane (TMOS). SEM-EDS confirmed deposition of nanospherical silica aggregates, with silicon content reaching 4.43 ± 0.64% (w/w). These artificially silicified cells demonstrated significantly improved survival and quantum yield retention under freezing (−20°C) and UVC irradiation stress relative to uncoated controls. Notably, bulk transcriptomic analysis of artificially silicified cells revealed upregulation of photosynthesis genes and increased pigment accumulation, a response distinct from the photosynthetic downregulation seen in the genetically silicified SG-Pt strain, suggesting that acute exogenous silica coating and chronic genetic silicification elicit different cellular responses.

Collectively, this work demonstrates that the mode of silicification—genetic versus biomimetic—differentially affects diatom metabolism, with artificial silicification conferring stress protection while maintaining or enhancing photosynthetic capacity. The application of single-cell sequencing to microalgae enabled resolution of cell-type heterogeneity and rare cell populations not detectable by conventional bulk approaches. The findings have implications for the development of silica-based encapsulation strategies for microalgal biotechnology, including potential applications in sustainable biomaterial production and carbon sequestration.

Key Findings

• Single-cell transcriptomic analysis revealed that the silicified SG-Pt strain clustered separately from the wild-type WT-Pt strain, with SG-Pt cells displaying a dormant-like metabolic state characterized by downregulated photosynthesis, cellular respiration, and protein synthesis, and elevated expression of iron starvation-inducible proteins (ISIP1).
• Cellular trajectory analysis identified four distinct cell groups and reconstructed a differentiation path from WT-Pt cells toward SG-Pt cells, also uncovering intracellular differentiation within the WT-Pt population, with LHCF15 showing clear downregulation during this transition.
• Artificial biosilicification of P. tricornutum cells using R5 peptide-catalyzed TMOS hydrolysis deposited nanospherical silica clusters on the cell surface (silicon content 4.43 ± 0.64% w/w), and these silica-coated cells exhibited significantly enhanced resistance to freezing at −20°C and UVC irradiation compared to uncoated controls.
• Transcriptomic analysis of artificially silica-coated P. tricornutum cells showed upregulation of photosynthesis-related genes and increased pigment accumulation relative to uncoated cells, contrasting with the downregulation of photosynthesis observed in the genetically silicified SG-Pt strain.
• High expression of iron starvation-inducible proteins in SG-Pt cells was detected by single-cell sequencing but had not been identified in prior bulk RNA-seq analyses, demonstrating the added resolution of single-cell approaches for characterizing heterogeneous microalgal populations.

Methods

• Single-cell RNA sequencing (10x Genomics Chromium platform)
• Seurat-based scRNA-seq data processing and UMAP dimensionality reduction
• Cellular trajectory analysis using dyno package with Slingshot model
• Gene set enrichment analysis (GSEA) and Gene Ontology (GO) term analysis
• R5 peptide-catalyzed biosilicification using TMOS as silica precursor
• Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS)
• Zeta potential measurement
• Bulk transcriptomic (RNA-seq) analysis
• Photosynthetic quantum yield measurement
• UV irradiation and freeze/drying stress assays
• Pigment content quantification via spectrophotometry
• Neutral red dye cell viability staining

Organisms

Phaeodactylum tricornutum

Closing the Gap between Bio-Based and Petroleum-Based Plastic through Bioengineering

Authors: Dina Al-Khairy, Weiqi Fu, Amnah Salem Alzahmi, Jean-Claude Twizere, Shady A. Amin, Kourosh Salehi-Ashtiani, Alexandra Mystikou Source: Microorganisms (2022) DOI: 10.3390/microorganisms10122320
Topics: bioplastics production polyhydroxyalkanoates (PHA/PHB) biosynthesis polylactic acid (PLA) production metabolic and genome engineering bioplastic biodegradation microalgae and cyanobacteria as production platforms transgenic plants for PHA production microplastic pollution and environmental impact synthetic biology for bio-based polymers enzymatic degradation of biopolymers

Abstract

Bioplastics, which are plastic materials produced from renewable bio-based feedstocks, have been investigated for their potential as an attractive alternative to petroleum-based plastics. Despite the harmful effects of plastic accumulation in the environment, bioplastic production is still underdeveloped. Recent advances in strain development, genome sequencing, and editing technologies have accelerated research efforts toward bioplastic production and helped to advance its goal of replacing conventional plastics. In this review, we highlight bioengineering approaches, new advancements, and related challenges in the bioproduction and biodegradation of plastics. We cover different types of polymers, including polylactic acid (PLA) and polyhydroxyalkanoates (PHAs and PHBs) produced by bacterial, microalgal, and plant species naturally as well as through genetic engineering. Moreover, we provide detailed information on pathways that produce PHAs and PHBs in bacteria. Lastly, we present the prospect of using large-scale genome engineering to enhance strains and develop microalgae as a sustainable production platform.

Summary

This review examines the current state of bioplastic production and biodegradation with a focus on polyhydroxyalkanoates (PHAs, PHB) and polylactic acid (PLA), surveying native biosynthetic capabilities across bacteria, plants, and microalgae, as well as engineering strategies to improve yields. The paper contextualizes the subject within the broader problem of plastic waste accumulation, noting that 390.7 million tons of plastic were produced globally in 2021 and that projections suggest approximately 12,000 million metric tons of plastic waste will accumulate in landfills and natural environments by 2050. The authors clarify a common public misconception by distinguishing bio-based origin from biodegradability, explaining that these properties are independent and governed by polymer chemistry and specific environmental conditions.

The review details the three-enzyme PHB biosynthesis pathway in Cupriavidus necator H16 and describes how this pathway has been introduced into heterologous hosts such as E. coli, diatoms, and green microalgae. It also covers efforts in transgenic plant systems, where PHB accumulation in chloroplasts has yielded levels up to 40% dry weight in Arabidopsis and up to 18.8% dry weight in tobacco. PLA production through microbial fermentation and one-step metabolic engineering in E. coli is discussed, including specific gene disruptions and overexpressions that direct carbon flux toward D-lactate and subsequent polymer synthesis. PHA synthase (PhaC) classification into four classes based on substrate specificity and chain-length preference is described, along with evidence from mutational studies that conserved residues are critical for enzyme activity.

The review also addresses the enzymatic and microbial mechanisms underlying bioplastic degradation, identifying specific organisms and enzymes responsible for breaking down PHA, PHB, PLA, PBS, PCL, and starch-based polymers. Four sequential stages of biodegradation—biodeterioration, biofragmentation, bioassimilation, and mineralization—are outlined, and the influence of abiotic environmental parameters on degradation rates is noted. The authors identify microalgae and cyanobacteria as promising future production platforms due to their phototropic growth, minimal nutrient requirements, and demonstrated capacity for PHB accumulation under nutrient-limiting conditions, and they advocate for large-scale genome engineering approaches to make bioplastic production economically competitive with petroleum-based alternatives.

Key Findings

• The PHB biosynthesis pathway in Cupriavidus necator H16 proceeds through three enzymatic steps—condensation of two acetyl-CoA molecules by β-ketothiolase (PhaA), reduction by acetoacetyl-CoA reductase (PhaB), and polymerization by PHA synthase (PhaC)—and this pathway has been successfully transferred to heterologous hosts including E. coli and microalgae.
• PHB production in transgenic plants has been achieved at levels up to 40% dry weight in Arabidopsis thaliana chloroplasts and up to 18.8% dry weight in tobacco leaves, demonstrating the feasibility of plant-based PHA production using existing agricultural infrastructure.
• The diatom Phaeodactylum tricornutum has been engineered to produce PHB at levels up to 10.6% of dry algal weight by introducing the PHB biosynthetic pathway from Ralstonia eutropha under control of a nitrogen reductase inducible promoter.
• Not all bioplastics are biodegradable; biodegradability depends on polymer chemistry rather than feedstock origin, and the ISO 14855:1999 standard requires at least 90% degradation within six months without toxic residues for a material to be classified as biodegradable.
• Bioplastic degradation is mediated by diverse bacterial and fungal species producing specific depolymerases and other enzymes, with degradation rates influenced by abiotic factors including UV irradiation, temperature, pH, oxygen, salinity, and chemical environment.

Methods

• Metabolic engineering and pathway engineering
• Recombinant gene expression (e.g., phbCAB operon transfer)
• Genome editing and transgenic plant/microbe construction
• Inducible promoter systems for controlled transgene expression
• Gas chromatography-mass spectrometry (GC-MS) for PHB detection
• Transmission electron microscopy (TEM) for granule visualization
• Literature review and bibliometric analysis (Google search trend data)
• Enzyme characterization and mutational analysis of PHA synthases

Organisms

Cupriavidus necator H16 (formerly Ralstonia eutropha H16), Escherichia coli, Pseudomonas aeruginosa, Pseudomonas putida, Pseudomonas oleovorans, Bacillus subtilis, Aeromonas hydrophila, Alcaligenes latus, Streptomyces ascomycinicus, Amycolatopsis spp., Lactobacillus spp., Rhizopus arrhizus, Rhizopus oryzae, Clostridium propionicum, Synechocystis sp. CCALA192, Synechocystis sp. PCC6803, Phaeodactylum tricornutum, Cylindrotheca fusiformis, Chlamydomonas reinhardtii, Arabidopsis thaliana, Nicotiana tabacum (tobacco), Oryza sativa (rice), Camelina sativa, Panicum virgatum (switchgrass), Tritirachium album, Aspergillus spp., Leptothrix spp.

Algal Cell Factories: Approaches, Applications, and Potentials

Authors: Weiqi Fu, Amphun Chaiboonchoe, Basel Khraiwesh, David R. Nelson, Dina Al-Khairy, Alexandra Mystikou, Amnah Alzahmi, Kourosh Salehi-Ashtiani Source: Marine Drugs (2016) DOI: 10.3390/md14120225
Topics: microalgal strain improvement mutagenesis in microalgae adaptive laboratory evolution genetic engineering of microalgae genome-scale metabolic modeling systems biology in algae synthetic biology bioactive compound production carotenoid and lipid biosynthesis macroalgae biotechnology

Abstract

With the advent of modern biotechnology, microorganisms from diverse lineages have been used to produce bio-based feedstocks and bioactive compounds. Many of these compounds are currently commodities of interest, in a variety of markets and their utility warrants investigation into improving their production through strain development. In this review, we address the issue of strain improvement in a group of organisms with strong potential to be productive 'cell factories': the photosynthetic microalgae. Microalgae are a diverse group of phytoplankton, involving polyphyletic lineage such as green algae and diatoms that are commonly used in the industry. The photosynthetic microalgae have been under intense investigation recently for their ability to produce commercial compounds using only light, CO2, and basic nutrients. However, their strain improvement is still a relatively recent area of work that is under development. Importantly, it is only through appropriate engineering methods that we may see the full biotechnological potential of microalgae come to fruition. Thus, in this review, we address past and present endeavors towards the aim of creating productive algal cell factories and describe possible advantageous future directions for the field.

Summary

This review examines strategies for developing microalgae as cell factories for producing bioactive compounds, including fatty acids, carotenoids, vitamins, and other secondary metabolites. The authors organize the discussion around four principal approaches: mutagenesis (UV, gamma irradiation, and chemical agents), adaptive laboratory evolution, genetic engineering, and systems biology combined with in silico metabolic modeling. Each approach is surveyed with specific examples from the literature, including UV-induced improvements in lipid accumulation in Chlamydomonas and Chlorella, ALE-driven carotenoid enhancement in Dunaliella salina and Phaeodactylum tricornutum, and the application of CRISPR/Cas9 and TALE-based tools in C. reinhardtii and P. tricornutum respectively. The GRAS regulatory status of several microalgal species is noted as a practical advantage that reduces downstream processing requirements for certain products.

The review also covers genome-scale metabolic modeling efforts across green microalgae, diatoms, and cyanobacteria, cataloging reconstructed models for organisms including C. reinhardtii, Chlorella spp., P. tricornutum, and Synechocystis sp. PCC 6803. Computational tools such as COBRA-based flux balance analysis and strain optimization algorithms (OptORF, Optknock) are described as means to identify gene deletion or overexpression targets for improved product yields. The authors note that while these models exist, their application specifically to bioactive compound production in microalgae remains limited and represents an area for further development.

Beyond microalgae, the review briefly addresses macroalgae and the moss Physcomitrella patens as supplementary photosynthetic platforms, noting the diversity of bioactive compounds produced by brown and red macroalgae and the relative tractability of P. patens for genetic manipulation. The authors conclude that while each individual approach has demonstrated utility, the integration of mutagenesis, adaptive evolution, genetic engineering, and systems biology modeling into a unified strain development workflow presents the most direct path toward realizing the full productive potential of algal cell factories.

Key Findings

• Mutagenesis approaches including UV irradiation, gamma ray irradiation, and chemical mutagens such as NTG and EMS have been successfully applied to improve lipid, carotenoid, and fatty acid accumulation in various microalgal species.
• Adaptive laboratory evolution has been used to generate microalgal strains with improved biomass production and enhanced accumulation of compounds such as carotenoids and chlorophylls, though the underlying genetic mechanisms often remain uncharacterized.
• Genetic engineering tools including microprojectile bombardment, electroporation, Agrobacterium-mediated transformation, and emerging genome editing technologies such as ZFN, TALEs, and CRISPR/Cas9 have been applied in microalgae, though efficiency and species coverage remain limited.
• Genome-scale metabolic models have been reconstructed for several microalgal and cyanobacterial species, including Chlamydomonas reinhardtii, Phaeodactylum tricornutum, Chlorella spp., and Synechocystis sp., enabling computational prediction of metabolic engineering strategies.
• Macroalgae and the moss Physcomitrella patens represent additional photosynthetic platforms for cell factory development, with stable and transient transformation systems established for several species.

Methods

• UV mutagenesis
• gamma ray irradiation
• chemical mutagenesis (NTG, EMS)
• fluorescence-activated cell sorting (FACS)
• confocal Raman microscopy
• adaptive laboratory evolution (ALE)
• genome re-sequencing
• microprojectile bombardment
• electroporation
• Agrobacterium-mediated transformation
• zinc-finger nuclease (ZFN) editing
• TALE-based genome editing
• CRISPR/Cas9 genome editing
• flux balance analysis (FBA)
• genome-scale metabolic modeling (COBRA, Pathway Tools)
• strain optimization tools (OptORF, EDGE, Optknock)
• transcriptomics, proteomics, metabolomics

Organisms

Chlamydomonas reinhardtii, Dunaliella bardawil, Dunaliella salina, Phaeodactylum tricornutum, Pavlova lutheri, Chlorella spp., Chlorella protothecoides, Chlorella variabilis, Chlorella vulgaris, Haematococcus pluvialis, Scenedesmus dimorphus, Ostreococcus tauri, Ostreococcus lucimarinus, Emiliania huxleyi, Parachlorella kessleri, Schizochytrium limacinum, Arthrospira platensis (Spirulina), Synechococcus elongatus PCC7942, Synechocystis sp. PCC 6803, Chlorella zofingiensis, Physcomitrella patens, Laminaria saccharina, Agardhiella subulata

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Authors: Amphun Chaiboonchoe, Lila Ghamsari, Bushra Dohai, Patrick Ng, Basel Khraiwesh, Ashish Jaiswal, Kenan Jijakli, Joseph Koussa, David R. Nelson, Hong Cai, Xinping Yang, Roger L. Chang, Jason Papin, Haiyuan Yu, Santhanam Balaji, Kourosh Salehi-Ashtiani Source: Molecular BioSystems (2016) DOI: 10.1039/c6mb00237d
Topics: genome-scale metabolic network reconstruction phylogenetic co-conservation Chlamydomonas reinhardtii metabolism constraint-based modeling and flux balance analysis synthetic lethal interactions network topology and evolution transcriptomics and gene expression metabolic engineering and synthetic biology coupled reaction sets eukaryotic evolutionary genomics

Abstract

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Summary

This study integrates topological, evolutionary, and transcriptomic analyses of the genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080), a green alga relevant to biofuel research. The authors transformed the existing metabolite-centric network into a gene-centric representation of 1086 nodes and 11,094 edges, then characterized evolutionary affinities of network genes against over 250 annotated genomes spanning 13 major eukaryotic lineages. Using mutual information and profile distance metrics validated against 1000 randomized networks, they defined two modes of gene co-conservation: dynamic co-conservation (gene pairs sharing similar but not universally distributed phylogenetic profiles) and static co-conservation (gene pairs conserved across nearly all lineages). Approximately 42% and 21% of network genes participate in dynamically and statically co-conserved pairs, respectively, demonstrating a significant association between network topology and evolutionary history.

To examine the relationship between functional interactions and evolutionary distance, the authors performed in silico double-gene deletion analysis across more than 500,000 gene pairs under two growth conditions (dark with acetate and autotrophic light) using flux balance analysis. Synthetically lethal and sick gene pairs, as well as genes involved in correlated reaction sets (co-sets), were found to be enriched for both unusually short and unusually long phylogenetic profile distances relative to random expectation. This contrasts with topologically adjacent gene pairs, which tend toward shorter phylogenetic distances. The authors also generated transcriptomic data via 454 sequencing under corresponding growth conditions to complement the computational findings.

The integrated results support a model in which the C. reinhardtii metabolic network is organized such that topologically neighboring genes are drawn from evolutionarily similar lineages, while functionally coupled genes span a broader range of phylogenetic distances. The authors interpret this architecture as potentially increasing network robustness to environmental variation. The identified evolutionary constraints on gene pairings may inform strategies for metabolic engineering and synthetic biology aimed at optimizing metabolite production in this organism.

Key Findings

• Network connectivity in the C. reinhardtii metabolic network shows significant concordance with gene co-conservation, with approximately 42% of network genes (455 of 1081) participating in dynamically co-conserved pairs and 21% in statically co-conserved pairs.
• A distinction between topological and functional evolutionary relationships exists: genes that are topologically adjacent in the network tend to minimize phylogenetic profile distances, whereas functionally interacting genes (those involved in synthetic interactions or coupled reactions) show enrichment for both shorter and longer phylogenetic distances.
• Dynamic and static modes of co-conservation were defined and identified: dynamic co-conservation captures gene pairs sharing similar but not universally conserved profiles (detected via mutual information), while static co-conservation captures gene pairs conserved across most or all of the 13 queried eukaryotic lineages (detected via low evolutionary profile distances).
• In silico double-gene deletion analysis across more than 500,000 pairs identified synthetic lethal and synthetic sick interactions whose associated gene pairs are enriched for atypically short and long phylogenetic distances compared to random expectation.
• The architecture of the C. reinhardtii metabolic network appears organized to minimize phylogenetic distances among topologically neighboring genes while expanding such distances among functionally coupled genes, potentially conferring robustness to varied environmental challenges.

Methods

• Genome-scale metabolic network reconstruction and analysis (iRC1080)
• Protein-centric network transformation from metabolite-centric network
• BLAST-based evolutionary affinity assignment across 13 eukaryotic lineages (>250 genomes)
• Mutual information (MI) analysis for dynamic co-conservation detection
• Euclidean and profile distance (PD) calculation for static co-conservation detection
• Network randomization with 1000 random trials for statistical thresholding
• Constraint-based modeling using COBRA Toolbox v.2 (flux balance analysis)
• In silico double-gene deletion for synthetic interaction prediction (>500,000 pairs)
• Kolmogorov-Smirnov and hypergeometric statistical tests
• Coupled reaction set (co-set) identification
• RNA isolation and 454 FLX transcriptome sequencing (Roche GS FLX Titanium)
• RPKM-based gene expression quantification
• Differential expression analysis using NOIseq
• Gene Ontology enrichment analysis using BiNGO
• Interolog analysis for metabolic network rewiring in yeast and Arabidopsis

Organisms

Chlamydomonas reinhardtii, Saccharomyces cerevisiae, Arabidopsis thaliana

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

Authors: Roger L Chang, Lila Ghamsari, Ani Manichaikul, Erik FY Hom, Santhanam Balaji, Weiqi Fu, Yun Shen, Tong Hao, Bernhard Ø Palsson, Kourosh Salehi-Ashtiani, Jason A Papin Source: Molecular Systems Biology (2011) DOI: 10.1038/msb.2011.52
Topics: genome-scale metabolic network reconstruction Chlamydomonas reinhardtii metabolism flux balance analysis photosynthesis modeling light spectral modeling lipid metabolism algal biofuels transcript verification metabolic engineering evolutionary loss of metabolic pathways

Abstract

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.

Summary

This study presents iRC1080, a genome-scale metabolic network reconstruction of the green alga Chlamydomonas reinhardtii, encompassing 1080 genes, 2190 reactions, 1068 metabolites, and 83 subsystems distributed across 10 compartments. The network was constructed by integrating genomic sequence data, published biochemical literature, and functional genome annotation, and represents a more comprehensive coverage of C. reinhardtii metabolic genes than previously available reconstructions. A distinctive feature of the network is the explicit treatment of photon absorption through a novel 'prism reaction' framework, which encodes the spectral composition of different light sources and distributes photon flux across wavelength-specific metabolic reactions, enabling quantitative prediction of growth under defined illumination conditions including sunlight, fluorescent bulbs, and LEDs.

The network places particular emphasis on lipid metabolism, providing full specification of acyl-chain length, double bond number, position, and stereoisomerism for fatty acyl, glycerolipid, glycerophospholipid, and sphingolipid metabolites—a level of chemical detail absent from prior reconstructions. Comparative analysis across multiple organisms' reconstructions confirmed that iRC1080 contains a substantially expanded lipid metabolic scope. Reconstruction also revealed putative evolutionary losses in C. reinhardtii, including the apparent absence of a functional VLCFA elongase and ceramide synthetase, supported by the absence of candidate genes in functional annotation, lack of detected lipid species in the literature, and incomplete transcript verification for sphingolipid pathway genes.

Model validation was performed through simulation of 30 environmental growth conditions and 14 published gene knockout phenotypes, with environmental simulations showing close agreement with experimental data, including accurate prediction of photosynthetic energy conversion efficiency. Experimental transcript verification using sequencing confirmed 92% of network transcripts to at least partial coverage and more than 75% to greater than 90% sequence coverage, providing substantial experimental support for network gene content. Discrepancies in gene knockout predictions were examined and attributed to possible errors in subcellular localization annotation, missing thermodynamic constraints, or gaps in regulatory constraint data, identifying areas for future experimental investigation.

Key Findings

• A genome-scale metabolic network for Chlamydomonas reinhardtii, designated iRC1080, was reconstructed accounting for 1080 genes, 2190 reactions, 1068 unique metabolites, and 83 subsystems across 10 compartments, covering an estimated 43% or more of genes with metabolic functions.
• A light-modeling approach using 'prism reactions' was developed to integrate spectral composition and photon flux from different light sources into the metabolic network, enabling quantitative growth prediction under specific lighting conditions including solar light, various bulbs, and LEDs.
• Comprehensive lipid pathway reconstruction revealed that C. reinhardtii likely lacks very long-chain fatty acids, very long-chain polyunsaturated fatty acids, and ceramides, suggesting evolutionary loss of VLCFA elongase and ceramide synthetase activities.
• Experimental transcript verification confirmed more than 75% of network-included transcripts at greater than 90% sequence coverage, with 92% of tested transcripts at least partially validated, representing a large-scale genome-level transcript validation effort.
• Simulation of 30 environmental growth conditions yielded close agreement with experimental results, and the photosynthetic model accurately predicted O2-PAR energy conversion efficiency of approximately 2%, consistent with the experimentally observed range of 1.3–4.5%.

Methods

• Genome-scale metabolic network reconstruction
• Flux balance analysis (FBA)
• Transcript sequencing and verification
• Prism reaction formulation for light spectral modeling
• Boolean regulatory constraint modeling
• Gene knockout simulation
• Functional genome annotation
• Lipid pathway comparative analysis
• Photobioreactor growth experiments

Organisms

Chlamydomonas reinhardtii

Characterizing algal blooms in a shallow & a deep channel

Authors: Maryam R. Al-Shehhi, David Nelson, Rashed Farzanah, Rashid Alshihi, Kourosh Salehi-Ashtiani Source: Ocean and Coastal Management (2021) DOI: 10.1016/j.ocecoaman.2021.105840
Topics: algal blooms harmful algal blooms chlorophyll-a dynamics sea surface temperature shallow versus deep coastal waters Arabian Gulf oceanography Sea of Oman oceanography water quality monitoring seasonal and interannual variability empirical orthogonal function analysis

Abstract

The outbreaks of algal blooms occur in both shallow and deep-water bodies with varied frequencies in different seasons. To investigate the occurrences of algal blooms in these two different water bodies, we consider the Arabian Gulf (shallow) and the Sea of Oman (deep) as examples for this study. In this work, we have used a recent unique and comprehensive in-situ dataset of the frequent algal blooms collected over the last decade in the Arabian Gulf and Oman Sea. This data includes algal blooms' frequencies as well as seawater properties including sea surface temperature (SST), Dissolved Oxygen (DO), salinity and pH. In addition, we have used satellite SST and chlorophyll-a (Chl-a) data, ocean reanalysis data of water currents and bathymetry data. These data have been analyzed through statistical methods including descriptive analysis, trend analysis, and empirical orthogonal function. The results obtained demonstrate a general decreasing trend of the algal bloom events from 2010 to 2018 in the shallow waters while in the deep waters the trend is increasing. We reveal a clear seasonality with the highest frequency and Chl-a concentration of algal blooms during winter and spring. The frequent occurrences of algal blooms during winter is due to favorable SST between 24°C and 32°C in shallow waters and up to 28°C in deep waters. Although salinity differs in the shallow waters (~39 psu) and deep waters (~37), but algal blooms are found to be tolerant to these different salinity ranges as well as at pH of 8 in both regions. Chl-a commonly exceeds 10 mg m−3 in the shallow waters (<100 m) and at water currents between 0.1 and 0.2 m/s. And it is less than 10 mg m−3 in deeper waters (>100 m) and at water currents exceeding 0.2 m/s. Even at optimum levels of SST and water depth algal blooms cannot occur if there is insufficient supply of nutrients.

Summary

This study characterizes algal bloom occurrences in the shallow Arabian Gulf and the deeper Sea of Oman using a decade-long (2008–2018) in situ dataset collected by UAE government environmental agencies (MOCCAE and EAD), supplemented by satellite remote sensing (VIIRS, MODIS Aqua) and ocean reanalysis data. The study employed descriptive statistics, linear trend analysis, moving average filtering, and empirical orthogonal function analysis to examine interannual, seasonal, and spatial variability of bloom frequency and chlorophyll-a concentrations across both regions. Toxicity characterization of selected bloom events used HPLC-MS, and morphological imaging was performed via light microscopy and scanning electron microscopy.

The results indicate divergent temporal trends between the two basins: algal bloom frequency declined in the shallow Arabian Gulf from 2010 to 2018, while it increased in the deeper Sea of Oman. Bloom occurrence was strongly seasonal, peaking in winter and spring (November–April), and was associated with sea surface temperatures of 24–32°C in shallow and up to 28°C in deep waters. Water depth and current speed were identified as key physical controls on bloom intensity: chlorophyll-a exceeded 10 mg m−3 in waters shallower than 100 m with moderate currents (0.1–0.2 m/s), while deeper, faster-flowing waters supported lower concentrations. Both regions sustained blooms across their respective salinity ranges (~37–39 psu) at pH 8.

The study also identified features of initial bloom timing, noting that first occurrences in shallow waters shifted from November to January after 2016, while deep-water blooms consistently initiated in December throughout the study period. The findings underscore that nutrient availability acts as a critical limiting factor even when physical conditions are otherwise conducive to bloom formation. Nutrient sources discussed include seafloor nutrients brought to the surface by upwelling, atmospheric dust deposition from Shamal wind events, and land-based inputs from coastal development and industrial activities. These results provide a systematic physical and biological characterization of bloom dynamics in two contrasting coastal environments of the Arabian Peninsula.

Key Findings

• Algal bloom frequency showed a general decreasing trend in the shallow Arabian Gulf from 2010 to 2018, while an increasing trend was observed in the deeper waters of the Sea of Oman over the same period.
• Algal blooms exhibited clear seasonality, with highest frequencies and Chl-a concentrations occurring during winter and spring months (November through April), driven by favorable sea surface temperatures of 24–32°C in shallow waters and up to 28°C in deep waters.
• Chlorophyll-a concentrations commonly exceeded 10 mg m−3 in shallow waters (<100 m depth) with water currents of 0.1–0.2 m/s, whereas concentrations remained below 10 mg m−3 in deeper waters (>100 m) where currents exceeded 0.2 m/s.
• Algal blooms were found to be tolerant of the differing salinity ranges between the two regions (~39 psu in shallow waters, ~37 psu in deep waters) and occurred at pH 8 in both regions.
• Even under optimal temperature and depth conditions, algal blooms did not occur in the absence of sufficient nutrient supply, highlighting nutrients as a critical limiting factor in bloom development.

Methods

• In situ water quality monitoring (SST, dissolved oxygen, salinity, pH)
• High Performance Liquid Chromatography (HPLC) for toxicity and metabolite analysis
• Light microscopy and scanning electron microscopy (SEM) for morphological analysis
• VIIRS satellite chlorophyll-a and SST data (4-km resolution)
• MODIS Aqua satellite chlorophyll-a and SST time series
• Empirical orthogonal function (EOF) analysis
• Temporal linear trend fitting and moving average filtering
• CMEMS ocean reanalysis data for surface current velocity
• ETOPO1 bathymetry data
• QGIS geospatial mapping

Organisms

dinoflagellates, cyanobacteria, Microcystis, Gymnodinium, phytoplankton

RNA Catalysis in Model Protocell Vesicles

Authors: Irene A. Chen, Kourosh Salehi-Ashtiani, Jack W. Szostak Source: Journal of the American Chemical Society (2005) DOI: 10.1021/ja051784p
Topics: protocell vesicles RNA catalysis ribozymes fatty acid membranes origin of life vesicle growth and division membrane permeability divalent cation tolerance hammerhead ribozyme prebiotic chemistry

Abstract

We are engaged in a long-term effort to synthesize chemical systems capable of Darwinian evolution, based on the encapsulation of self-replicating nucleic acids in self-replicating membrane vesicles. Here, we address the issue of the compatibility of these two replicating systems. Fatty acids form vesicles that are able to grow and divide, but vesicles composed solely of fatty acids are incompatible with the folding and activity of most ribozymes, because low concentrations of divalent cations (e.g., Mg2+) cause fatty acids to precipitate. Furthermore, vesicles that grow and divide must be permeable to the cations and substrates required for internal metabolism. We used a mixture of myristoleic acid and its glycerol monoester to construct vesicles that were Mg2+-tolerant and found that Mg2+ cations can permeate the membrane and equilibrate within a few minutes. In vesicles encapsulating a hammerhead ribozyme, the addition of external Mg2+ led to the activation and self-cleavage of the ribozyme molecules. Vesicles composed of these amphiphiles grew spontaneously through osmotically driven competition between vesicles, and further modification of the membrane composition allowed growth following mixed micelle addition. Our results show that membranes made from simple amphiphiles can form vesicles that are stable enough to retain encapsulated RNAs in the presence of divalent cations, yet dynamic enough to grow spontaneously and allow the passage of Mg2+ and mononucleotides without specific macromolecular transporters. This combination of stability and dynamics is critical for building model protocells in the laboratory and may have been important for early cellular evolution.

Summary

This study investigates the compatibility of RNA catalysis with fatty acid-based protocell vesicles, addressing a fundamental challenge in assembling a minimal cell-like system capable of Darwinian evolution. Pure fatty acid vesicles are unstable in the presence of divalent cations such as Mg2+, which are required for ribozyme folding and catalytic activity. The authors demonstrate that mixed vesicles composed of myristoleic acid and glycerol monomyristoleate (MA:GMM, 2:1 molar ratio) tolerate up to 4 mM MgCl2, retain encapsulated RNA molecules under these conditions, and allow rapid passive diffusion of Mg2+ across the membrane with equilibration occurring within seconds. The enhanced permeability to Mg2+ and to mononucleotides, without corresponding leakage of larger RNA molecules, suggests that Mg2+ modulates small-scale membrane defects rather than causing large-scale structural disruption.

Functional RNA catalysis within these protocell models was demonstrated using an artificially evolved hammerhead ribozyme (N15min7) encapsulated in MA:GMM:dodecane vesicles. External addition of Mg2+ activated the encapsulated ribozyme, producing the expected self-cleavage products. Vesicle growth was maintained in this system through two mechanisms: intermembrane amphiphile transfer driven by osmotic gradients, which was largely unaffected by Mg2+ or GMM incorporation, and micelle addition, which required the inclusion of dodecane to destabilize the micellar phase sufficiently for productive incorporation into preformed vesicles.

Collectively, the results show that simple amphiphile membranes can simultaneously provide the stability needed to retain macromolecular cargo and the dynamic permeability needed to supply ionic cofactors and small-molecule substrates for internal biochemistry. This balance between retention and permeability, achieved without protein transporters, is relevant to models of early cellular evolution in which genetic and metabolic functions must be co-localized within a growing, dividing compartment.

Key Findings

• Mixed vesicles composed of myristoleic acid and glycerol monomyristoleate (MA:GMM at 2:1 ratio) tolerated up to 4 mM MgCl2 without significant dye leakage, substantially improving on the tolerance of pure fatty acid vesicles.
• Mg2+ ions rapidly permeated MA:GMM (2:1) vesicle membranes, equilibrating within seconds (permeability coefficient ~2×10-7 cm/s), whereas phospholipid (POPC) vesicles showed no detectable Mg2+ permeation over several hours.
• Exposure to 4 mM Mg2+ increased the permeability of vesicle membranes to small negatively charged solutes such as UMP approximately 4-fold, but did not cause leakage of encapsulated RNA oligomers, indicating selective permeability.
• A hammerhead ribozyme (N15min7) encapsulated in MA:GMM:dodecane vesicles was activated by external addition of Mg2+, demonstrating functional RNA catalysis within simple amphiphile vesicles.
• Addition of dodecane (9 mol%) to MA:GMM membranes destabilized the micellar phase sufficiently to enable vesicle growth by micelle incorporation, with approximately 20-40% surface area increase depending on the amount of micelles added.

Methods

• Vesicle preparation by freeze-thaw cycling and extrusion to 100 nm diameter
• FRET-based vesicle growth assay using NBD-PE and Rh-DHPE dyes
• Calcein dye leakage assay with size-exclusion chromatography
• Radiolabeled 3H-UMP permeability assay
• Mag-fura-2 fluorescent probe for intravesicular Mg2+ measurement
• Stopped-flow mixing for Mg2+ permeability kinetics
• Dynamic light scattering for vesicle and micelle size characterization
• Pyrene excimer/monomer fluorescence assay for aggregate detection
• Denaturing PAGE with phosphorimage analysis for ribozyme cleavage quantitation
• T7 RNA polymerase transcription for ribozyme preparation
• Confocal and epifluorescence microscopy

Organisms

—

Integrated Analysis of Gene Network in Childhood Leukemia from Microarray and Pathway Databases

Authors: Amphun Chaiboonchoe, Sandhya Samarasinghe, Don Kulasiri, Kourosh Salehi-Ashtiani Source: BioMed Research International (2014) DOI: 10.1155/2014/278748
Topics: acute lymphoblastic leukemia (ALL) glucocorticoid-regulated gene expression microarray data analysis gene set enrichment analysis (GSEA) protein-protein interaction networks B-ALL and T-ALL subtype differentiation bioinformatics pathway analysis apoptosis in lymphoid cells systems biology differential gene expression

Abstract

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Summary

This study applies a multi-level bioinformatics framework to characterize glucocorticoid (GC)-regulated gene expression in two subtypes of childhood acute lymphoblastic leukemia (ALL): B-ALL and T-ALL. Using publicly available microarray data from 13 patients (10 B-ALL, 3 T-ALL) with expression measurements at three time points (0, 6/8, and 24 hours post-treatment), the authors reanalyzed data from Schmidt et al. by separating the two subtypes rather than pooling them. Applying revised selection criteria (log2 ratio ±1 in at least 50% of patients per subtype), they identified subtype-specific differentially expressed gene sets, extending the analysis to include the 6–24 hour interval. Cell cycle genes were subsequently removed to focus on GC-specific apoptotic responses, yielding 304 unique GC-responsive genes across both subtypes with minimal overlap between them.

Functional characterization was performed at three levels: gene, gene set, and network/pathway. Gene Set Enrichment Analysis (GSEA) using KEGG and GO gene sets from MSigDB revealed that B-ALL is enriched in B-cell receptor signaling, asthma, and phosphorylation-related processes, while T-ALL is enriched in T-cell receptor signaling and primary immunodeficiency pathways. Ingenuity Pathway Analysis (IPA) further distinguished the subtypes by showing that T-ALL genes are predominantly associated with cell death functions and B-ALL genes with cell cycle functions, suggesting differential timing of GC-induced apoptosis between the two subtypes. Network-level analysis using GeneMANIA and STRING, centered on NR3C1 as a key regulatory gene, confirmed functional interactions and demonstrated partial overlap between the two tools, with GeneMANIA capturing a broader set of interactions.

The study demonstrates that combining B-ALL and T-ALL data in analysis obscures subtype-specific molecular responses to GC treatment. The comparison of identified gene sets with those from two prior studies revealed minimal overlap, underscoring the sensitivity of differential expression results to drug type, patient tissue source, and normalization approach. The integrated use of GSEA, IPA, GeneMANIA, and STRING provides a reproducible analytical workflow for dissecting subtype-specific pathway activity and gene network topology in ALL, with potential utility for identifying subtype-relevant therapeutic targets.

Key Findings

• Separating B-ALL and T-ALL patient data rather than combining them revealed that only 8 of 22 originally reported differentially expressed genes were common to both subtypes, indicating subtype-specific gene expression responses to glucocorticoid treatment.
• Differentially expressed GC-regulated genes in B-ALL and T-ALL participate in largely distinct biological processes, with B-ALL enriched in asthma, B-cell receptor signaling, and phosphorylation pathways, while T-ALL was enriched in T-cell receptor signaling, primary immunodeficiency, and leukocyte-related processes.
• IPA network analysis showed that T-ALL molecular and cellular functions are more associated with cell death, while B-ALL functions are more associated with cell cycle progression, suggesting that apoptosis may occur earlier in T-ALL than B-ALL following glucocorticoid treatment.
• Comparison of GC-regulated gene sets with two prior studies revealed minimal gene overlap, with BTG1 being the only gene common across T-ALL, Tissing et al., and Thompson and Johnson datasets, suggesting that drug type, tissue source, and normalization method substantially affect identified gene sets.
• Network analysis using both GeneMANIA and STRING for T-ALL early response genes showed overlapping interactions centered on NR3C1, with STRING interactions being a subset of those found in GeneMANIA, validating the functional associations identified.

Methods

• Robust Multiarray Average (RMA) normalization
• Differential gene expression analysis
• Gene Set Enrichment Analysis (GSEA)
• Enrichment Map (Cytoscape plugin)
• Ingenuity Pathway Analysis (IPA)
• GeneMANIA network analysis
• STRING protein interaction network analysis
• DAVID gene annotation
• R and BioConductor statistical computing
• Jaccard coefficient similarity calculation

Organisms

Homo sapiens

The genome and phenome of the green alga Chloroidium sp. UTEX 3007 reveal adaptive traits for desert acclimatization

Authors: David R Nelson, Basel Khraiwesh, Weiqi Fu, Saleh Alseekh, Ashish Jaiswal, Amphun Chaiboonchoe, Khaled M Hazzouri, Matthew J O'Connor, Glenn L Butterfoss, Nizar Drou, Jillian D Rowe, Jamil Harb, Alisdair R Fernie, Kristin C Gunsalus, Kourosh Salehi-Ashtiani Source: eLife (2017) DOI: 10.7554/eLife.25783
Topics: green algae genomics desert extremophile biology lipid accumulation and palmitic acid biosynthesis osmotic and desiccation stress tolerance heterotrophic carbon source utilization metabolomics and phenomics genome-scale metabolic network reconstruction comparative genomics algal biotechnology and bioproducts triacylglycerol biosynthesis

Abstract

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, Chloroidium sp. UTEX 3007, which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of Chloroidium sp. UTEX 3007 indicated that the alga accumulates a broad range of carbon sources, including several desiccation tolerance-promoting sugars and unusually large stores of palmitate. Growth assays revealed capacities to grow in salinities from zero to 60 g/L and to grow heterotrophically on >40 distinct carbon sources. Assembly and annotation of genomic reads yielded a 52.5 Mbp genome with 8153 functionally annotated genes. Comparison with other sequenced green algae revealed unique protein families involved in osmotic stress tolerance and saccharide metabolism that support phenomic studies. Our results reveal the robust and flexible biology utilized by a green alga to successfully inhabit a desert coastline.

Summary

This study presents an integrated genomic and phenomic characterization of Chloroidium sp. UTEX 3007, a green microalga isolated from multiple desert and coastal habitats across the United Arab Emirates. The authors combined whole-genome sequencing (yielding a 52.5 Mbp assembly with 8153 annotated genes), metabolomics (GC-FID, GC-MS, and UHPLC/Q-TOF-MS/MS), and high-throughput phenotype microarrays to define the molecular and physiological basis of the alga's adaptation to arid, variable-salinity environments. The species was found to grow across a broad salinity range (0–60 g/L NaCl) and to use more than 40 distinct carbon sources heterotrophically, including pentose sugars not previously reported for any green alga. Intracellular metabolite profiling revealed accumulation of desiccation tolerance-promoting sugars (arabitol, ribitol, trehalose) alongside unusually high levels of palmitic acid, comprising approximately 41.8% of total fatty acids and stored primarily as triacylglycerols.

Comparative genomic analysis against related green algae identified unique protein domain families in Chloroidium sp. UTEX 3007 associated with osmotic stress tolerance and saccharide metabolism, consistent with the observed phenotypes. Genome-scale metabolic network reconstruction suggested that TAG biosynthesis in this species may proceed through membrane lipid remodeling via phospholipase D and lecithin retinol acyltransferase activities, rather than the classical acyl-CoA-dependent route. The high palmitate content of the accumulated TAGs is proposed to confer thermostability to cellular lipid stores under the oxidative and thermal stresses characteristic of desert climates.

The authors discuss the potential biotechnological relevance of Chloroidium sp. UTEX 3007 as a candidate source of palm oil alternatives, given its palmitate-rich lipid profile and tolerance of challenging growth conditions. The study also highlights the ecological significance of characterizing extremophilic microalgae in underexplored desert environments, noting that the combination of broad carbon assimilation, euryhalinity, and stress-protective metabolite accumulation collectively accounts for the species' ubiquity across UAE habitats. The datasets generated, including the annotated genome and metabolome, are made publicly available to support further research.

Key Findings

• Chloroidium sp. UTEX 3007 accumulates triacylglycerols composed predominantly of palmitic acid (~41.8% of total fatty acids), at levels roughly equivalent to those found in palm oil from Elaeis guineensis, suggesting potential as an alternative palm oil source.
• The alga grows across a wide salinity range (0–60 g/L NaCl) and can grow heterotrophically on more than 40 distinct carbon sources, including pentose sugars not previously reported for green algae, as well as desiccation tolerance-promoting sugars such as trehalose, sorbitol, and raffinose.
• Genome sequencing yielded a 52.5 Mbp assembly (N50 = 148 kbps) with 8153 functionally annotated genes containing 9455 distinct Pfam domains, and comparative genomics identified unique protein families related to osmotic stress tolerance and saccharide metabolism.
• Metabolic reconstruction and lipid profiling revealed a TAG biosynthesis pathway likely operating via membrane lipid remodeling rather than the conventional acyl-CoA pool, involving phospholipase D and lecithin retinol acyltransferase domain-containing enzymes.
• Intracellular metabolite profiling confirmed accumulation of desiccation-resistance-promoting sugars including arabitol, ribitol, and trehalose, which together with the lipid profile are consistent with osmotic stabilization and adaptation to desert conditions.

Methods

• Whole-genome shotgun sequencing (PCR-free Illumina, ~200x depth)
• Genome assembly and annotation using SNAP with Arabidopsis thaliana HMM
• Pfam domain analysis (Pfam-A.hmm v31.0)
• BLASTP/BLAST2GO comparative proteomics
• Gas chromatography with flame-ionization detection (GC-FID) for fatty acid profiling
• Gas chromatography-mass spectrometry (GC-MS) for polar primary metabolite profiling
• Ultra-high performance liquid chromatography coupled to quadrupole time-of-flight MS (UHPLC/Q-TOF-MS/MS) for intact lipid profiling
• Biolog phenotype microarrays (Omnilog system) for carbon, nitrogen, phosphorus, and sulfur source utilization
• Flow cytometry (BD FACSAria III) for lipid quantification using BODIPY 505/515
• Open pond simulator (OPS) bioreactor growth assays
• Genome-scale metabolic network reconstruction using BioCyc database
• Synteny analysis for chromosome number estimation

Organisms

Chloroidium sp. UTEX 3007, Chlamydomonas reinhardtii, Chlorella variabilis NC64A, Coccomyxa subellipsoidea C-169, Micromonas pusilla, Ostreococcus tauri, Elaeis guineensis, Arabidopsis thaliana

Detection of circular transcripts in C. elegans

Authors: Lila Ghamsari, Xinping Yang, Kourosh Salehi-Ashtiani Source: The Worm Breeder's Gazette (2009) Topics: circular RNA C. elegans transcriptomics non-canonical RNA processing RT-PCR detection methods RNA circularization alternative splicing internal ribosome entry sites post-transcriptional modification genome coding potential RNA biology

Abstract

Circular RNAs of protein coding genes have been observed occasionally. Biological properties of such transcripts have not been deeply explored, nor have their occurrence in C. elegans been described. As the functional properties of these transcripts can be effectively evaluated by future RNAi knockdown experiments in the worm, we investigated whether circularized RNAs are expressed. Our approach entails amplification of reverse transcribed RNA using primers in which the 'reverse' primer matches sequences upstream of the 'forward' primer on a transcript. We carried out RT-PCR on total RNA isolated from N2 worms targeting a set of 94 SL1 positive control transcripts. Most transcript models tested yielded a band in RT-PCR reaction without addition of RNA ligase, suggesting that circular RNA formation is common in vivo. Alignment of obtained sequences to the genome revealed circular junctions in 37 of 94 transcript models examined. These sequences were spliced, although no SL or poly(A) sequences were detected, suggesting that these transcripts had circularized before post-transcriptional processing, or had lost these modifications prior to circularization.

Summary

This study investigated the presence of circular RNAs in Caenorhabditis elegans using an RT-PCR strategy designed specifically to detect circularized transcripts. The approach employed divergently oriented primers—where the reverse primer anneals upstream of the forward primer relative to the linear transcript—such that only reverse transcripts derived from circular RNA templates can support amplification. This configuration excludes amplification from linear RNA templates and thus provides selective detection of circular forms. The authors applied this method to 94 SL1 trans-spliced transcripts previously characterized by RACE in N2 worms.

The results indicated that the majority of tested transcripts produced RT-PCR products in the absence of exogenous RNA ligase, suggesting that circular RNA formation is a relatively frequent event in C. elegans. Sequence analysis of cloned PCR products identified circular junctions in 37 of the 94 transcript models tested. Notably, the junction sequences displayed evidence of splicing but lacked both the spliced leader (SL) sequences and poly(A) tails typically associated with mature C. elegans mRNAs. RNA ligase controls, in which exogenous circularization was induced prior to reverse transcription, showed frequent occurrence of SL and poly(A) sequences at junctions, ruling out technical artifacts as an explanation for their absence in the experimental samples. This suggests either that circularization precedes addition of these modifications or that they are removed before or during circularization.

The authors propose that circular transcripts could have functional significance by enabling translation through internal ribosome entry sites, potentially generating protein products with novel exon combinations not achievable through conventional alternative splicing of linear transcripts, thereby expanding the coding capacity of the genome. The study provides an early systematic survey of circular RNA occurrence in C. elegans and outlines a framework for future functional investigation using RNAi-based approaches available in this model organism.

Key Findings

• Circular RNA formation appears to be common in vivo in C. elegans, as most of the 94 SL1 positive control transcripts tested yielded RT-PCR bands without the addition of RNA ligase.
• Circular junction sequences were identified in 37 of 94 transcript models examined, with all such sequences being spliced but lacking SL or poly(A) sequences.
• The absence of SL and poly(A) sequences at circular junctions in the no-ligase experiments was not attributable to technical limitations, as RNA ligase controls frequently showed these modifications at junctions.
• The data suggest that circularization may occur before post-transcriptional processing, or that SL and poly(A) modifications are lost prior to circularization.
• Translation of circular transcripts through mechanisms such as internal ribosome entry sites could expand genome coding potential by juxtaposing exons in configurations not achievable through alternative splicing of linear transcripts.

Methods

• RT-PCR with divergent primers (reverse primer upstream of forward primer)
• RNA isolation from N2 C. elegans
• RNA ligase 1 treatment as positive control for circularization
• Cloning of PCR products as minipools
• Sanger end-sequencing from both directions
• Sequence alignment to the C. elegans genome
• RACE (Rapid Amplification of cDNA Ends)

Organisms

Caenorhabditis elegans

COT drives resistance to RAF inhibition through MAP kinase pathway reactivation

Authors: Cory M. Johannessen, Jesse S. Boehm, So Young Kim, Sapana R. Thomas, Leslie Wardwell, Laura A. Johnson, Caroline M. Emery, Nicolas Stransky, Alexandria P. Cogdill, Jordi Barretina, Giordano Caponigro, Haley Hieronymus, Ryan R. Murray, Kourosh Salehi-Ashtiani, David E. Hill, Marc Vidal, Jean J. Zhao, Xiaoping Yang, Ozan Alkan, Sungjoon Kim, Jennifer L. Harris, Christopher J. Wilson, Vic E. Myer, Peter M. Finan, David E. Root, Thomas M. Roberts, Todd Golub, Keith T. Flaherty, Reinhard Dummer, Barbara L. Weber, William R. Sellers, Robert Schlegel, Jennifer A. Wargo, William C. Hahn, Levi A. Garraway Source: Nature (2010) DOI: 10.1038/nature09627
Topics: BRAF V600E melanoma RAF inhibitor resistance MAPK/ERK signaling pathway MAP3K8/COT/Tpl2 kinase kinase ORF functional screen MEK inhibitor resistance acquired and de novo drug resistance combinatorial kinase inhibition C-RAF signaling cancer targeted therapy

Abstract

Oncogenic mutations in the serine/threonine kinase B-RAF (also known as BRAF) are found in 50–70% of malignant melanomas. Pre-clinical studies have demonstrated that the B-RAF(V600E) mutation predicts a dependency on the mitogen-activated protein kinase (MAPK) signalling cascade in melanoma—an observation that has been validated by the success of RAF and MEK inhibitors in clinical trials. However, clinical responses to targeted anticancer therapeutics are frequently confounded by de novo or acquired resistance. Identification of resistance mechanisms in a manner that elucidates alternative 'druggable' targets may inform effective long-term treatment strategies. Here we expressed ~600 kinase and kinase-related open reading frames (ORFs) in parallel to interrogate resistance to a selective RAF kinase inhibitor. We identified MAP3K8 (the gene encoding COT/Tpl2) as a MAPK pathway agonist that drives resistance to RAF inhibition in B-RAF(V600E) cell lines. COT activates ERK primarily through MEK-dependent mechanisms that do not require RAF signalling. Moreover, COT expression is associated with de novo resistance in B-RAF(V600E) cultured cell lines and acquired resistance in melanoma cells and tissue obtained from relapsing patients following treatment with MEK or RAF inhibitors. We further identify combinatorial MAPK pathway inhibition or targeting of COT kinase activity as possible therapeutic strategies for reducing MAPK pathway activation in this setting. Together, these results provide new insights into resistance mechanisms involving the MAPK pathway and articulate an integrative approach through which high-throughput functional screens may inform the development of novel therapeutic strategies.

Summary

This study employed a systematic functional screen of approximately 600 kinase open reading frames (ORFs) to identify mechanisms of resistance to PLX4720, a selective RAF kinase inhibitor, in B-RAF(V600E) malignant melanoma cell lines. Nine ORFs conferred significant resistance, with MAP3K8 (encoding COT/Tpl2) and RAF1 (C-RAF) emerging as the top candidates across multiple B-RAF(V600E) cell lines. COT overexpression sustained MEK and ERK phosphorylation in the presence of RAF inhibition without requiring RAF signaling, indicating that COT can reactivate the MAPK pathway through a RAF-independent mechanism. Biochemical studies further showed that oncogenic B-RAF(V600E) suppresses COT protein stability, and that inhibition of B-RAF—pharmacologically or by shRNA—restores COT protein levels, providing a mechanistic basis for the selective outgrowth of COT-expressing cells during RAF inhibitor treatment.

COT-expressing B-RAF(V600E) cell lines with MAP3K8 copy number gains showed de novo resistance to PLX4720 (GI50 of 8–10 µM), and a short-term melanoma culture derived from a patient who relapsed after MEK inhibitor treatment also expressed COT and was refractory to RAF inhibition. Analysis of lesion-matched tumor biopsies from patients with metastatic B-RAF(V600E) melanoma receiving the clinical RAF inhibitor PLX4032 revealed increased MAP3K8 mRNA expression concurrent with treatment and further elevation at relapse, corroborating the experimental findings in a clinical setting. shRNA-mediated depletion of COT and treatment with a small molecule COT kinase inhibitor both reduced ERK and MEK phosphorylation in COT-amplified cell lines, demonstrating that COT kinase activity is required for MAPK pathway activation in this context.

The study also found that COT expression conferred cross-resistance to allosteric MEK inhibitors, consistent with evidence that COT can activate ERK through MEK-independent as well as MEK-dependent pathways. Importantly, combined RAF and MEK inhibition more effectively suppressed ERK activation and reduced viability in COT-expressing cells than either agent alone, supporting combinatorial MAPK pathway blockade as a strategy to address COT-driven resistance. These findings define MAP3K8/COT as a clinically relevant mediator of both de novo and acquired resistance to RAF inhibition and illustrate how large-scale functional kinase screens can be used to identify resistance mechanisms and inform rational combination therapy strategies.

Key Findings

• A high-throughput screen of 597 kinase ORFs in B-RAF(V600E) melanoma cells identified MAP3K8 (COT/Tpl2) and C-RAF as top drivers of resistance to the RAF inhibitor PLX4720, shifting the GI50 by 10–600-fold.
• COT activates ERK through predominantly MEK-dependent but RAF-independent mechanisms, and recombinant COT can also directly phosphorylate ERK1 in vitro, indicating capacity for MEK-independent ERK activation.
• Oncogenic B-RAF(V600E) suppresses COT protein stability, and pharmacological or shRNA-mediated B-RAF inhibition increases COT protein levels, suggesting a mechanism by which RAF inhibition may select for COT-expressing cells.
• MAP3K8 mRNA expression was elevated in lesion-matched tumor biopsies from patients with metastatic B-RAF(V600E) melanoma during and after PLX4032 treatment, providing clinical evidence for COT involvement in acquired resistance.
• Combined RAF and MEK inhibition more effectively suppressed ERK phosphorylation and cell growth in COT-expressing cells than single-agent RAF inhibition, supporting dual MAPK pathway blockade as a strategy to overcome COT-mediated resistance.

Methods

• Lentiviral kinase ORF library screen (CCSB/Broad Institute Kinase ORF Collection, 597 ORFs)
• Cell viability assays (GI50 determination across multi-point drug concentration range)
• Western blotting for phospho-ERK, phospho-MEK, phospho-C-RAF(S338)
• shRNA-mediated gene knockdown (B-RAF, C-RAF, COT/MAP3K8, MEK1/2)
• Quantitative real-time PCR with reverse transcription (qRT-PCR)
• In vitro kinase assay with recombinant COT and ERK1
• Copy number analysis and mutation profiling of cancer cell lines
• Small molecule COT kinase inhibitor treatment
• Patient biopsy analysis (lesion-matched pre-treatment, on-treatment, post-relapse samples)

Organisms

Homo sapiens

CPEB3 is associated with human episodic memory

Authors: Christian Vogler, Klara Spalek, Amanda Aerni, Philippe Demougin, Ariane Müller, Kim-Dung Huynh, Andreas Papassotiropoulos, Dominique J.-F. de Quervain Source: Frontiers in Behavioral Neuroscience (2009) DOI: 10.3389/neuro.08.004.2009
Topics: episodic memory CPEB3 gene single nucleotide polymorphism ribozyme self-cleavage synaptic plasticity behavioral genetics emotional memory mRNA translation regulation verbal memory recall human cognitive genetics

Abstract

Cytoplasmic polyadenylation element-binding (CPEB) proteins are crucial for synaptic plasticity and memory in model organisms. A highly conserved, mammalian-specific short intronic sequence within CPEB3 has been identified as a ribozyme with self-cleavage properties. In humans, the ribozyme sequence is polymorphic and harbors a single nucleotide polymorphism that influences cleavage activity of the ribozyme. Here we show that this variation is related to performance in an episodic memory task and that the effect of the variation depends on the emotional valence of the presented material. Our data suggest a role for human CPEB3 in human episodic memory.

Summary

This study investigated whether genetic variation in the CPEB3 gene is associated with episodic memory performance in humans. CPEB proteins regulate mRNA translation at synapses and have been linked to synaptic plasticity and memory in multiple model organisms. A previously identified mammalian-conserved intronic ribozyme within CPEB3 contains a single nucleotide polymorphism (SNP rs11186856, a U-to-C substitution) that increases self-cleavage activity more than twofold in the rare C allele, potentially reducing CPEB3 protein expression. The study used a behavioral genetics approach in 333 healthy young adults, correlating CPEB3 genotype with performance on a verbal delayed free-recall memory task.

Homozygous carriers of the rare C allele (CC genotype, n=49) recalled significantly fewer words than T allele carriers at both 5-minute and 24-hour delayed recall intervals, while immediate recall performance was equivalent across genotype groups. This pattern argues against confounds such as differences in attention or working memory and points specifically to memory consolidation or retrieval processes. No additive allele-dose effect was detected, as CT heterozygotes performed equivalently to TT homozygotes.

A notable feature of the findings is the modulation of the genotype effect by the emotional valence of the memorized words. The association with poorer recall was strongest and most consistent for positively valenced words, present but weaker for negatively valenced words, and absent for neutral words. This pattern suggests that CPEB3 may interact with neural circuits involved in emotional memory processing, such as the amygdala. The authors interpret the overall results as evidence that CPEB3-mediated regulation of mRNA translation contributes to human episodic memory, particularly for emotionally arousing material.

Key Findings

• Homozygous carriers of the rare C allele of SNP rs11186856 in the CPEB3 ribozyme sequence showed significantly poorer delayed verbal memory recall at both 5 minutes and 24 hours after learning compared to T allele carriers.
• The genotype effect on memory performance was not observed for immediate recall, indicating that the association is specific to delayed episodic memory consolidation rather than attention, motivation, or working memory.
• The memory impairment associated with the CC genotype was most pronounced for words with positive emotional valence and weaker for negative valence words, with no significant association for neutral words.
• No allele-dose effect was observed, as heterozygous CT carriers performed similarly to homozygous TT carriers, with the memory deficit restricted to CC homozygotes.
• Adjacent SNPs within the same haplotype block were also significantly associated with memory performance, consistent with the haplotype structure of the CPEB3 genomic region, while significance dropped to chance levels outside the block.

Methods

• Behavioral genetics association study
• Verbal episodic memory task with immediate and delayed free recall
• Pyrosequencing on PyroMark ID System for genotyping
• Hardy-Weinberg equilibrium testing
• ANCOVA controlling for gender and education (SPSS 11.0.4)
• Haploview 4.0 for haplotype structure analysis
• Genomic DNA extraction from venous blood (QIAamp DNA blood maxi kit)

Organisms

Homo sapiens, Aplysia, Drosophila melanogaster, Mus musculus

Evidence for Transcript Networks Composed of Chimeric RNAs in Human Cells

Authors: Sarah Djebali, Julien Lagarde, Philipp Kapranov, Vincent Lacroix, Christelle Borel, Jonathan M. Mudge, Cédric Howald, Sylvain Foissac, Catherine Ucla, Jacqueline Chrast, Paolo Ribeca, David Martin, Ryan R. Murray, Xinping Yang, Lila Ghamsari, Chenwei Lin, Ian Bell, Erica Dumais, Jorg Drenkow, Michael L. Tress, Josep Lluís Gelpí, Modesto Orozco, Alfonso Valencia, Nynke L. van Berkum, Bryan R. Lajoie, Marc Vidal, John Stamatoyannopoulos, Philippe Batut, Alex Dobin, Jennifer Harrow, Tim Hubbard, Job Dekker, Adam Frankish, Kourosh Salehi-Ashtiani, Alexandre Reymond, Stylianos E. Antonarakis, Roderic Guigó, Thomas R. Gingeras Source: PLoS ONE (2012) DOI: 10.1371/journal.pone.0028213
Topics: chimeric RNAs transcriptome complexity gene boundary definition RACE sequencing tiling arrays RNA sequencing transcript networks human chromosomes 21 and 22 gene expression coordination 3D genome organization

Abstract

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5′ and 3′ transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Summary

This study systematically characterized the transcriptional boundaries of 492 protein-coding genes on human chromosomes 21 and 22 using a combination of RACE reactions, chromosome-tiling arrays, and deep RNA sequencing. A total of 26,688 RACE reactions were performed using RNA from 11 normal human tissues and five transformed cell lines, generating over 195,000 RACEfrags assigned to originating primers. The analysis revealed that for 85% of interrogated genes, transcriptional boundaries extend beyond currently annotated limits, frequently connecting with exons of other annotated genes and forming chimeric RNA molecules. The frequency distribution of distances between index genes and associated RACEfrags followed a power law, with connections spanning up to 34 Mb.

The study identified 2,324 reciprocal gene-to-gene connections—defined as cases where RACE primers in gene A detect signal in gene B and vice versa—representing 2- to 3-fold enrichment over chance expectation. Of 200 selected chimeric connections tested by RT-PCR and sequencing, 56% were confirmed, yielding 208 distinct transcript sequences. Shorter-range chimeric junctions predominantly exhibited canonical splicing signals, while longer-range connections more often showed non-canonical splice sites, with a subset displaying short genomic duplications at junction sites. Chimeric connections were also independently detected by paired-end RNAseq and PET ditag sequencing, supporting their authenticity and ruling out systematic reverse transcriptase template-switching artifacts as the sole explanation.

The biological relevance of the observed chimeric RNA networks is supported by several lines of evidence: the non-random gene connectivity patterns, the evolutionary conservation (greater phylogenetic depth) of genes participating in chimeric interactions, the coordinated expression of connected gene pairs, and the close three-dimensional spatial proximity of contributing genomic loci as measured by Hi-C. These findings suggest that chimeric transcripts arising from multiple genes constitute structured RNA networks rather than isolated transcriptional events, and that standard gene annotation models may underestimate the extent and functional significance of inter-genic transcriptional connectivity in human cells.

Key Findings

• For 85% of 492 protein-coding genes on human chromosomes 21 and 22, transcriptional boundaries extend beyond current annotated termini, most often connecting with exons of other annotated genes to form chimeric RNAs.
• 72% of RACEfrags mapping outside index genes map to exons of other genes, indicating a non-random pattern of chimeric connections rather than transcriptional noise.
• A total of 2,324 reciprocal gene-to-gene connections were identified, representing approximately 2- to 3-fold more than expected by chance, with 37% being cell-type specific.
• Chimeric transcripts detected by RACEarray were independently confirmed by RNAseq and RT-PCR with cloning and sequencing, with 56% of tested chimeric connections validated by sequencing.
• The non-random gene interconnections, coordinated expression of connected genes, and close three-dimensional genomic proximity of contributing loci collectively support the biological relevance of chimeric RNA networks.

Methods

• Rapid amplification of cDNA ends (RACE)
• Chromosome 21 and 22 tiling arrays
• Deep RNA sequencing (RNAseq)
• RT-PCR, cloning and sequencing
• HAVANA manual annotation pipeline
• In silico RACEarray simulator
• Paired-end tag (PET) ditag sequencing
• Hi-C 3D genome proximity analysis
• Power law distribution fitting
• Circular genome visualization

Organisms

Homo sapiens

Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders

Authors: Nidhi Sahni, Song Yi, Mikko Taipale, Juan I. Fuxman Bass, Jasmin Coulombe-Huntington, Fan Yang, Jian Peng, Jochen Weile, Georgios I. Karras, Yang Wang, István A. Kovács, Atanas Kamburov, Irina Krykbaeva, Mandy H. Lam, George Tucker, Vikram Khurana, Amitabh Sharma, Yang-Yu Liu, Nozomu Yachie, Quan Zhong, Yun Shen, Alexandre Palagi, Adriana San-Miguel, Changyu Fan, Dawit Balcha, Amelie Dricot, Daniel M. Jordan, Jennifer M. Walsh, Akash A. Shah, Xinping Yang, Ani K. Stoyanova, Alex Leighton, Michael A. Calderwood, Yves Jacob, Michael E. Cusick, Kourosh Salehi-Ashtiani, Luke J. Whitesell, Shamil Sunyaev, Bonnie Berger, Albert-László Barabási, Benoit Charloteaux, David E. Hill, Tong Hao, Frederick P. Roth, Yu Xia, Albertha J.M. Walhout, Susan Lindquist, Marc Vidal Source: Cell (2015) DOI: 10.1016/j.cell.2015.04.013
Topics: Mendelian disease mutations protein-protein interactions edgotyping protein folding and stability chaperone interactions protein-DNA interactions interactome networks genotype-phenotype relationships missense mutations network biology

Abstract

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to 'edgetic' alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Summary

This study systematically characterizes the functional consequences of missense mutations associated with Mendelian disorders by profiling their effects on multiple classes of macromolecular interactions. The authors constructed a human mutation ORFeome (hmORFeome1.1) comprising 2,890 mutant open reading frames across 1,140 genes, representing a wide range of diseases including cancer susceptibility, cardiac, respiratory, and neurological disorders. Using a multi-assay pipeline encompassing protein-chaperone interaction (PCI) profiling via LUMIER, protein-protein interaction (PPI) profiling via yeast two-hybrid and orthogonal GPCA validation, and protein-DNA interaction (PDI) profiling for transcription factors, the authors assessed both folding integrity and interaction-level perturbations caused by disease mutations at scale.

The results indicate that roughly 72% of disease-associated missense alleles do not show enhanced binding to quality control chaperones such as HSP90, HSC70, or ER chaperones, suggesting that protein misfolding or gross instability is not the predominant mechanism underlying Mendelian disease mutations. Instead, two-thirds of tested disease alleles perturb protein-protein interactions, with approximately 31% classified as edgetic (selectively losing a subset of interactions) and 26% as quasi-null (losing all tested interactions). Quasi-null proteins are more frequently associated with increased chaperone binding and reduced steady-state expression, consistent with structural destabilization, while edgetic proteins largely maintain normal folding and expression profiles. Gain-of-interaction events were rare. Common variants from healthy individuals showed markedly lower rates of interaction perturbation (8%), supporting the utility of interaction profiling as a functional filter to distinguish pathogenic from benign variants.

The study further demonstrates that distinct mutations in the same gene can produce different interaction perturbation profiles, which often correspond to clinically distinguishable disease phenotypes—supporting the concept that allele-specific interaction networks underlie phenotypic specificity. For transcription factors, protein-DNA interaction profiling revealed that many disease alleles affecting DNA binding do not perturb protein-protein interactions, underscoring the necessity of multi-dimensional interaction profiling. Integration of PCI, PPI, and PDI data within network frameworks provides a more complete picture of how mutations translate to disease, and the resulting resources, including the hmORFeome1.1 and associated interaction datasets, offer a foundation for future genotype-to-phenotype analyses.

Key Findings

• The majority (approximately 72%) of disease-associated missense alleles do not show increased chaperone binding, suggesting they do not grossly impair protein folding or stability.
• Two-thirds of disease-associated alleles perturb protein-protein interactions, with approximately 31% classified as 'edgetic' (affecting only a subset of interactions) and 26% as quasi-null (losing all detectable interactions).
• Non-disease common variants from healthy individuals rarely perturb protein-protein interactions (8%), representing a roughly 7-fold reduction compared to disease mutations (57%), indicating that interaction profiling can help distinguish disease-causing mutations from benign variants.
• Different missense mutations in the same gene can produce distinct interaction perturbation profiles, which often correlate with distinct disease phenotypes, supporting interaction-level mechanisms as drivers of phenotypic diversity.
• For transcription factors, many disease alleles that leave protein-protein interactions intact instead perturb protein-DNA interactions, highlighting the importance of profiling multiple interaction types to fully characterize mutational effects.

Methods

• Yeast two-hybrid (Y2H) screening
• LUMIER quantitative interaction assay
• Gaussia princeps luciferase protein complementation assay (GPCA)
• Cellular thermal shift assay (CeTSA)
• Semi-quantitative ELISA for protein abundance
• Co-immunoprecipitation followed by western blot
• FoldX computational stability predictions
• PolyPhen-2 deleteriousness predictions
• Gateway cloning and ORF sequence verification
• Human ORFeome v1.1 and HI-II-14 interactome map
• Protein-DNA interaction (PDI) profiling with enhancer sequences