MCAST: scanning for cis-regulatory motif clusters

Integration of horizontally acquired light-harvesting genes into an ancestral regulatory network in the cyanobacterium Acaryochloris marina MBIC11017

The acquisition of new capabilities by horizontal gene transfer (HGT) shapes the distribution of traits during microbial diversification. In the Chlorophyll (Chl) d-producing cyanobacterium Acaryochloris marina, the genes involved in the production and disassembly of the light-harvesting phycobiliprotein phycocyanin (PC) were lost in the A. marina common ancestor but then subsequently regained via HGT in A. marina strain MBIC11017. However, it remains unknown how the HGT-acquired PC genes in MBIC11017 have been reintegrated into its existing regulatory network after tens of millions of years since their loss. Here, we investigated potential mechanisms of regulatory assimilation of PC genes by comparing the transcriptomes of A. marina strain MBIC11017 and a PC-lacking close relative under both low irradiance far-red light and high irradiance white light. We found that PC assembly and degradation processes have been re-assimilated into a conserved ancestral response to high light. Further, we identified putative regulatory elements that were likely co-transferred with PC genes and could be recognized by A. marina's pre-existing light response machinery. This study offers insights into how HGT-acquired genes can be reintegrated into an existing transcriptional regulatory network that has evolved in their absence.IMPORTANCEHorizontal gene transfer, the asymmetric movement of genetic information between donor and recipient organisms, is an important mechanism for acquiring new traits. In order for newly acquired gene content to be retained, it must be integrated into the genetic repertoire and regulatory networks of the recipient cell. In a strain of the Chlorophyll d-producing cyanobacterium Acaryochloris marina, the recent reacquisition of the genes required to produce the light-harvesting pigment phycocyanin offers a rare opportunity to understand the mechanisms underlying the regulatory assimilation of an acquired complex trait in bacteria. The significance in our research is in characterizing how an ancestrally lost, complex trait can be reintegrated into a conserved regulatory network, even after millions of years.

The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes

BACKGROUND

The precise prediction of transcription factor binding sites (TFBSs) is pivotal for unraveling the gene regulatory networks underlying biological processes. While numerous tools have emerged for in silico TFBS prediction in recent years, the evolving landscape of computational biology necessitates thorough assessments of tool performance to ensure accuracy and reliability. Only a limited number of studies have been conducted to evaluate the performance of TFBS prediction tools comprehensively. Thus, the present study focused on assessing twelve widely used TFBS prediction tools and four de novo motif discovery tools using a benchmark dataset comprising real, generic, Markov, and negative sequences. TFBSs of Arabidopsis thaliana and Homo sapiens genomes downloaded from the JASPAR database were implanted in these sequences and the performance of tools was evaluated using several statistical parameters at different overlap percentages between the lengths of known and predicted binding sites.

RESULTS

Overall, the Multiple Cluster Alignment and Search Tool (MCAST) emerged as the best TFBS prediction tool, followed by Find Individual Motif Occurrences (FIMO) and MOtif Occurrence Detection Suite (MOODS). In addition, MotEvo and Dinucleotide Weight Tensor Toolbox (DWT-toolbox) demonstrated the highest sensitivity in identifying TFBSs at 90% and 80% overlap. Further, MCAST and DWT-toolbox managed to demonstrate the highest sensitivity across all three data types real, generic, and Markov. Among the de novo motif discovery tools, the Multiple Em for Motif Elicitation (MEME) emerged as the best performer. An analysis of the promoter regions of genes involved in the anthocyanin biosynthesis pathway in plants and the pentose phosphate pathway in humans, using the three best-performing tools, revealed considerable variation among the top 20 motifs identified by these tools.

CONCLUSION

The findings of this study lay a robust groundwork for selecting optimal TFBS prediction tools for future research. Given the variability observed in tool performance, employing multiple tools for identifying TFBSs in a set of sequences is highly recommended. In addition, further studies are recommended to develop an integrated toolbox that incorporates TFBS prediction or motif discovery tools, aiming to streamline result precision and accuracy.

The Estrogen Receptor-Related Orphan Receptors Regulate Autophagy through TFEB

Autophagy is an essential self-degradative and recycling mechanism that maintains cellular homeostasis. Estrogen receptor-related orphan receptors (ERRs) are fundamental in regulating cardiac metabolism and function. Previously, we showed that ERR agonists improve cardiac function in models of heart failure and induce autophagy. Here, we characterized a mechanism by which ERRs induce the autophagy pathway in cardiomyocytes. Transcription factor EB (TFEB) is a master regulator of the autophagy-lysosome pathway and has been shown to be crucial regulator of genes that control autophagy. We discovered that TFEB is a direct ERR target gene whose expression is induced by ERR agonists. Activation of ERR results in increased TFEB expression in both neonatal rat ventricular myocytes and C2C12 myoblasts. An ERR-dependent increase in TFEB expression results in increased expression of an array of TFEB target genes, which are critical for the stimulation of autophagy. Pharmacologically targeting ERR is a promising potential method for the treatment of many diseases where stimulation of autophagy may be therapeutic, including heart failure. SIGNIFICANCE STATEMENT: Estrogen receptor-related receptor agonists function as exercise mimetics and also display efficacy in animal models of metabolic disease, obesity, and heart failure.

Transcription factor binding specificities of the oomycete Phytophthora infestans reflect conserved and divergent evolutionary patterns and predict function

BACKGROUND

Identifying the DNA-binding specificities of transcription factors (TF) is central to understanding gene networks that regulate growth and development. Such knowledge is lacking in oomycetes, a microbial eukaryotic lineage within the stramenopile group. Oomycetes include many important plant and animal pathogens such as the potato and tomato blight agent Phytophthora infestans, which is a tractable model for studying life-stage differentiation within the group.

RESULTS

Mining of the P. infestans genome identified 197 genes encoding proteins belonging to 22 TF families. Their chromosomal distribution was consistent with family expansions through unequal crossing-over, which were likely ancient since each family had similar sizes in most oomycetes. Most TFs exhibited dynamic changes in RNA levels through the P. infestans life cycle. The DNA-binding preferences of 123 proteins were assayed using protein-binding oligonucleotide microarrays, which succeeded with 73 proteins from 14 families. Binding sites predicted for representatives of the families were validated by electrophoretic mobility shift or chromatin immunoprecipitation assays. Consistent with the substantial evolutionary distance of oomycetes from traditional model organisms, only a subset of the DNA-binding preferences resembled those of human or plant orthologs. Phylogenetic analyses of the TF families within P. infestans often discriminated clades with canonical and novel DNA targets. Paralogs with similar binding preferences frequently had distinct patterns of expression suggestive of functional divergence. TFs were predicted to either drive life stage-specific expression or serve as general activators based on the representation of their binding sites within total or developmentally-regulated promoters. This projection was confirmed for one TF using synthetic and mutated promoters fused to reporter genes in vivo.

CONCLUSIONS

We established a large dataset of binding specificities for P. infestans TFs, representing the first in the stramenopile group. This resource provides a basis for understanding transcriptional regulation by linking TFs with their targets, which should help delineate the molecular components of processes such as sporulation and host infection. Our work also yielded insight into TF evolution during the eukaryotic radiation, revealing both functional conservation as well as diversification across kingdoms.

FOX transcription factors are common regulators of Wnt/β-catenin-dependent gene transcription

The Wnt/β-catenin pathway is a critical regulator of development and stem cell maintenance. Mounting evidence suggests that the outcome of Wnt signaling is determined by the collaborative action of multiple transcription factors, including members of the highly conserved forkhead box (FOX) protein family. However, the contribution of FOX transcription factors to Wnt signaling has not been investigated in a systematic manner. Here, we performed complementary screens of all 44 human FOX proteins to identify new Wnt pathway regulators. By combining β-catenin reporter assays with Wnt pathway-focused qPCR arrays and proximity proteomics of selected candidates, we determine that most FOX proteins are involved in the regulation of Wnt pathway activity. As proof-of-principle, we additionally characterize class D and I FOX transcription factors as physiologically relevant regulators of Wnt/β-catenin signaling. We conclude that FOX proteins are common regulators of the Wnt/β-catenin-dependent gene transcription that may control Wnt pathway activity in a tissue-specific manner.

DNA binding and gene regulatory functions of MSMEG_2295, a repressor encoded by the dinB2 operon of Mycobacterium smegmatis

MSMEG_2295 is a TetR family protein encoded by the first gene of a Mycobacterium smegmatis (Msm) operon that expresses the gene for DinB2 (MSMEG_2294), a translesion DNA repair enzyme. We have carried out investigations to understand its function by performing DNA binding studies and gene knockout experiments. We found that the protein binds to a conserved inverted repeat sequence located upstream of the dinB2 operon and several other genes. Using a knockout of MSMEG_2295, we show that MSMEG_2295 controls the expression of at least five genes, the products of which could potentially influence carbohydrate and fatty acid metabolism as well as antibiotic and oxidative stress resistance. We have demonstrated that MSMEG_2295 is a repressor by performing complementation analysis. Knocking out of MSMEG_2295 had a significant impact on pyruvate metabolism. Pyruvate dehydrogenase activity was virtually undetectable in ΔMSMEG_2295, although in the complemented strain, it was high. We also show that knocking out of MSMEG_2295 causes resistance to H₂O₂, reversed in the complemented strain. We have further found that the mycobacterial growth inhibitor plumbagin, a compound of plant origin, acts as an inducer of MSMEG_2295 regulated genes. We, therefore, establish that MSMEG_2295 functions by exerting its role as a repressor of multiple Msm genes and that by doing so, it plays a vital role in controlling pyruvate metabolism and response to oxidative stress.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

A combination of transcription factors mediates inducible interchromosomal contacts

The genome forms specific three-dimensional contacts in response to cellular or environmental conditions. However, it remains largely unknown which proteins specify and mediate such contacts. Here we describe an assay, MAP-C (Mutation Analysis in Pools by Chromosome conformation capture), that simultaneously characterizes the effects of hundreds of cis or trans-acting mutations on a chromosomal contact. Using MAP-C, we show that inducible interchromosomal pairing between HAS1pr-TDA1pr alleles in saturated cultures of Saccharomyces yeast is mediated by three transcription factors, Leu3, Sdd4 (Ypr022c), and Rgt1. The coincident, combined binding of all three factors is strongest at the HAS1pr-TDA1pr locus and is also specific to saturated conditions. We applied MAP-C to further explore the biochemical mechanism of these contacts, and find they require the structured regulatory domain of Rgt1, but no known interaction partners of Rgt1. Altogether, our results demonstrate MAP-C as a powerful method for dissecting the mechanistic basis of chromosome conformation.

EBWS: Essential Bioinformatics Web Services for Sequence Analyses

The Essential Bioinformatics Web Services (EBWS) are implemented on a new PHP-based server that provides useful tools for analyses of DNA, RNA, and protein sequences applying a user-friendly interface. Nine Web-based applets are currently available on the Web server. They include reverse complementary DNA and random DNA/RNA/peptide oligomer generators, a pattern sequence searcher, a DNA restriction cutter, a prokaryotic ORF finder, a random DNA/RNA mutation generator. It also includes calculators of melting temperature (TM) of DNA/DNA, RNA/RNA, and DNA/RNA hybrids, a guide RNA (gRNA) generator for the CRISPR/Cas9 system and an annealing temperature calculator for multiplex PCR. The pattern-searching applet has no limitations in the number of motif inputs and applies a toolbox of Regex quantifiers that can be used for defining complex sequence queries of RNA, DNA, and protein sequences. The DNA enzyme digestion program utilizes a large database of 1502 restriction enzymes. The gRNA generator has a database of 25 bacterial genomes searchable for gRNA target sequences and has an option for searching in any genome sequence given by the user. All programs are permanently available online at http://penchovsky.atwebpages.com/applications.php without any restrictions.

Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells

Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites, and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators. The usefulness of motif analysis is further exemplified in this review by how motif discovery improves peak calling in ChIP-seq and ChIP-exo experiments and, when coupled with information on gene expression, allows insights into physical mechanisms of transcriptional modulation.