The team focused on a subtlety of gene architecture that is often overlooked. Most human protein-coding genes do not have a single transcription start site, or TSS, but several: the analysis uncovered 57,461 start sites mapped to 16,302 genes, meaning roughly three options per gene, with more than three-quarters of genes carrying at least two. Because these alternative start sites usually sit upstream of the same start codon, switching between them typically leaves the final protein untouched. Yet every nucleotide transcribed between the start site and the codon costs the cell phosphate bonds, so the choice of where RNA polymerase fires is, in energy terms, anything but free.

The researchers call the region between a transcription start site and the start codon a measure of futile transcription distance. When a cell uses a start site very close to the codon, the wasted stretch is short. When it fires from a far-flung upstream site, it transcribes thousands of extra bases — including introns — that code for nothing. Strikingly, the data showed that the human genome has evolved to keep most start sites tightly clustered: about 60 percent of all mapped TSSs lie within one kilobase of their start codon, and successive start sites within a gene showed strong constraints on how much farther apart they could drift. The authors interpret this clustering as the footprint of evolutionary pressure to minimize pointless transcription.

To test whether cancer disrupts this design, the team mined cap analysis of gene expression (CAGE) data from the FANTOM5 project, comparing 476 cancer samples with 1,255 non-cancer samples, and then validated the results using an independent technique called PRO-cap on 110 cancer and 305 non-cancer samples. CAGE and PRO-cap both capture the capped 5′ ends of freshly initiated RNA molecules, providing a direct readout of how often each start site is deployed. By combining these deployment frequencies with the distances to each gene’s start codon, the researchers computed an energy cost coefficient for every gene and, ultimately, for entire transcriptomes.

The result was unambiguous: the aggregated energy cost of the cancer promoterome was roughly 10 to the power of 0.5 — a little over threefold — higher than that of non-cancer samples, an excess confirmed by both technologies after correction for sequencing depth. At the level of individual genes, the aggregated cost coefficient was significantly higher in cancers across 15,861 genes. In other words, cancer cells do not merely transcribe more; they transcribe more wastefully, pushing polymerase to fire from start sites that saddle the cell with longer, more expensive upstream transcripts.

But the deregulation proved far more strategic than simple sloppiness. Using Shannon entropy to quantify how randomly start sites were deployed, the researchers found two contrasting behaviors coexisting in cancer. For most genes, particularly those at distant upstream positions, start site deployment became highly stochastic — scattered, unregulated, and gene-function agnostic, effectively leaking transcription from sites that healthy cells keep silent. Yet for a specific subset of genes, cancers did the opposite: deployment became more deterministic, tightly concentrated on the most proximal, energy-cheap start sites. That subset was overwhelmingly enriched for genes driving biosynthesis, nucleotide metabolism, cell proliferation, and the cell cycle — the very engines of the cancer phenotype.

This duality, the authors argue, reflects selection acting on energy economics. A gene that must be expressed at high volume in a rapidly dividing cell profits enormously from shaving thousands of wasted bases off every transcript. The genome apparently anticipated this: biosynthetic and proliferation genes already carry start sites that sit unusually close to their start codons, a layout the researchers suggest may have first evolved to support the rapid cell divisions of early embryogenesis under metabolic constraint. Incipient cancer cells appear to co-opt this pre-existing economy, and even recruit chromatin-regulatory genes — whose deregulated deployment could rewire the epigenome — to lock in the cost-saving configuration.

The study also proposes an evolutionary interpretation of transcriptional noise itself. If start sites are clustered proximally, then purely random deployment of them actually saves energy, because the dice fall most often on the short transcripts. The authors suggest the human transcription start site landscape has been shaped to make stochasticity cheap, and that healthy cells exploit this, maintaining higher gene-space entropy with lower overall cost. Cancer breaches that threshold in two directions at once: embracing expensive randomness at distant start sites while carving out tightly regulated, low-cost proximal deployment for hallmark genes. The combined parameter the team built from effective futile transcription distance and compound entropy turned out to be the single strongest classifier of cancer from non-cancer samples in their functional analyses.

The findings carry caveats the authors themselves acknowledge. The observed patterns could partly be downstream consequences of MYC-driven transcriptional amplification, elevated RNA polymerase II activity, or the metabolic reprogramming of specific tumor types, rather than a truly universal hallmark — and the magnitude of the cost excess varied across tissue types, with brain cancers a notable exception in the CAGE data. Even so, the near-universal direction of the effect across heterogeneous sample pools, replicated across two independent technologies, suggests that transcriptional energy waste deserves a place alongside the Warburg effect and glutamine addiction as a metabolic signature of malignancy.

Beyond cancer biology, the work hints at a general principle: where a gene begins is not an accident of genome annotation but a calibrated trade-off between regulatory flexibility and metabolic cost. If future experiments confirm the causal chain — that relaxing start site selection precedes and enables malignant transformation — then the transcriptional start site landscape could become a diagnostic window onto a tumor’s energetic state, and perhaps even a therapeutic target. For now, the message is vivid enough: in cancer, even the silent stretches of RNA carry a bill, and the cell has learned to make someone else pay it.

Subject of Research: Deregulated transcription start site deployment and futile transcription energetics in cancer

Article Title: Gene functions determine stochastic or adaptive futile transcription in cancers through deregulated start site deployments

Article References: Satish, A. L., Kumar, P., Morbia, I., & Singh, U. (2026). Gene functions determine stochastic or adaptive futile transcription in cancers through deregulated start site deployments. Frontiers in Bioinformatics, 6, Article 1883461. https://doi.org/10.3389/fbinf.2026.1883461

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1883461

Keywords: transcription start sites, futile transcription, cancer metabolism, CAGE, PRO-cap, gene expression, Shannon entropy, promoterome, Warburg effect, energy cost, biosynthesis, cell proliferation

The research team, led by Imad Abugessaisa of the RIKEN Center for Integrative Medical Sciences together with colleagues including Wendy van de Sande of Erasmus MC, built their atlas using a technique called LQ-ssCAGE, or low-quantity single-strand cap analysis of gene expression. CAGE-based methods capture the 5-prime capped ends of RNA molecules, which mark the exact positions where RNA polymerase II initiated transcription. Because the method both identifies these transcription start sites and quantifies how abundantly each one is used, it provides a two-in-one view of promoter activity and gene expression. The researchers prepared 72 LQ-ssCAGE libraries and, in parallel, 72 standard RNA-seq libraries from the same samples, using the RNA-seq data as an independent quality check on the CAGE measurements.

The experimental design was carefully constructed to mimic a real infection and its treatment. Half of the larvae were infected with Madurella mycetomatis, a high-priority fungal pathogen on the World Health Organization’s fungal priority list and the most common cause of eumycetoma, a neglected tropical disease characterized by tumorous lesions in subcutaneous tissue. The infected and uninfected larvae were then divided into three groups: one treated with solvent only, one with the antifungal drug itraconazole, and one with ravuconazole, which was recently assessed in the first clinical trial for eumycetoma. Samples were collected at four time points, 6, 30, 72, and 168 hours, with three biological replicates per condition, producing a rich time-course dataset of promoter activity during infection and therapy.

Sequencing yielded roughly 53.3 million cap-trapped 5-prime reads, which the team mapped to the current reference genome assembly, CSIRO_AGI_GalMel_v1, a high-quality assembly scoring 99.3 percent on the lepidoptera BUSCO benchmark and containing 15,138 genes and pseudogenes. Quality control was thorough. Gene body coverage showed the expected strong 5-prime enrichment for CAGE libraries and uniform coverage for RNA-seq libraries, sequencing depth was consistent across samples with no outlier batches, and pairwise correlation analysis between normalized RNA-seq and CAGE counts showed strong positive Spearman correlations, confirming that the two independent assays produced reproducible expression profiles.

From the raw data, the researchers identified 1,219,124 individual CAGE transcription start sites, which they filtered down to 332,601 biologically relevant positions. Clustering these signals produced 40,200 tag clusters, from which they derived two tiers of transcription start site peaks: a permissive set of 27,942 peaks detected in at least four samples, and a robust set of 5,437 peaks detected in all samples. They also identified 728 bidirectional clusters, the molecular signature of active enhancers, which after filtering to non-exonic intronic and intergenic locations yielded 141 candidate active enhancers. Users of the atlas can therefore choose between the permissive and robust annotations depending on how conservative their analyses need to be.

The validation evidence for these calls is strong. Among all CAGE peaks, 32.1 percent overlapped annotated 5-prime untranslated regions, and more strikingly, 80.27 percent of all CAGE tags fell within 5-prime UTRs, exactly where active promoters should sit. Genome-wide enrichment analysis showed that the identified peaks were strongly and specifically enriched at annotated promoters of both protein-coding and non-coding RNA genes, while intronic and intergenic regions were depleted. Dinucleotide frequency analysis at the dominant start sites revealed the characteristic initiator sequence bias expected at insect RNA polymerase II promoters, and the atlas-derived annotations displayed a markedly stronger core promoter signature, including TATA box and GC patch signals, than the existing NCBI reference annotation, demonstrating a genuine improvement in transcription start site resolution.

The atlas also revealed the two canonical shapes of animal promoters. Using the interquartile range of tag clustering as a measure of initiation precision, the team classified 4,746 broad promoters, where transcription starts at many scattered positions, and 1,679 sharp promoters, where initiation is concentrated at a single precise site. As expected from earlier CAGE studies in mammals and flies, sharp promoters carried a stronger TATA box positioned roughly 30 bases upstream of the start site, while broad promoters were GC-rich, consistent with CpG island-associated architecture. Most genes, about 6,899, used a single dominant start site, whereas roughly 2,300 genes used multiple sites, and 71.1 percent of promoter-associated genes were linked to more than one tag cluster, echoing the widespread alternative promoter usage documented in Drosophila.

Perhaps the most clinically intriguing result concerns what did and did not change during infection. RNA-seq confirmed differentially expressed genes between infected and healthy larvae, consistent with the team’s earlier work on iron regulatory pathways during M. mycetomatis grain development. However, no differentially expressed genes were detected between infected larvae and infected larvae treated with itraconazole or ravuconazole. The authors note that this mirrors recent clinical observations in which neither drug showed measurable activity during the first six months of eumycetoma treatment, with persistent fungal viability and no reduction in lesion size or plasma beta-1,3-D-glucan levels. Differential transcription start site usage analysis did flag a handful of genes, including a cystinosin homolog and a peroxidase-like isoform, whose promoter selection shifted between healthy and infected samples, hinting that infection may add a layer of transcriptional regulation beyond simple changes in gene abundance.

Using expression correlations between nearby elements, the team predicted physical interactions between enhancers and promoters, finding that 682 of 37,320 transcription start sites interacted with 156 of 218 active enhancers. Interacting start sites were expressed at significantly higher levels than non-interacting ones, and the analysis also uncovered super-enhancers, clusters of closely spaced bidirectional enhancers. De novo motif discovery on the top 5 percent of expressed start sites pointed to RFX family transcription factors, with the RFX1 ortholog showing 7.1-fold enrichment, a factor conserved across animals and known in flies as a key regulator of ciliary and sensory neuron genes. The authors are candid about limitations: only about 700,000 reads per sample were generated, and saturation analysis suggests the depth was sufficient for highly expressed promoters but likely underestimated weak enhancers. Even so, with all data deposited in NCBI GEO and Mendeley Data, and the annotations browsable in the ZENBU genome browser, this first draft start site atlas gives the growing G. mellonella research community a foundation for gene regulation studies comparable to the reference resources long available for human and mouse.

Subject of Research: Genome-wide mapping of transcription start sites in Galleria mellonella larvae during fungal infection and antifungal treatment

Article Title: Genome-wide mapping of the Galleria mellonella transcription start sites during fungal infection and treatment

Article References: Abugessaisa, I., Konings, M., Manabe, R.-I., Tagami, M., Severin, J., Hasegawa, A., Kawashima, T., Kinoshita, H., Noma, S., Takahashi, C., Verbon, A., Okazaki, Y., van de Sande, W. W., & Kasukawa, T. (2026). Genome-wide mapping of the Galleria mellonella transcription start sites during fungal infection and treatment. iScience, 29(10), Article 117675. https://doi.org/10.1016/j.isci.2026.117675

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117675

Keywords: Galleria mellonella, transcription start sites, CAGE, Madurella mycetomatis, eumycetoma, promoters, enhancers, gene regulation, itraconazole, ravuconazole, genome annotation, infection model