Background: The evaluation of alternative splicing, including differential isoform expression and differential exon usage, can provide some insights on the transcriptional changes that occur in response to environmental perturbations. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to assess potential changes in splicing events in the longissimus dorsi muscle of beef calves gestated under control or methionine-rich diets. RNA sequencing and whole-genome bisulfite sequencing were used to evaluate muscle transcriptome and methylome, respectively. Results: Alternative splicing patterns were significantly altered by maternal methionine supplementation. Most of the altered genes were directly implicated in muscle development, muscle physiology, ATP activities, RNA splicing and DNA methylation, among other functions. Interestingly, there was a significant association between DNA methylation and differential exon usage. Indeed, among the set of genes that showed differential exon usage, significant differences in methylation level were detected between significant and non-significant exons, and between contiguous and non-contiguous introns to significant exons. Conclusions: Overall, our findings provide evidence that a prenatal diet rich in methyl donors can significantly alter the offspring transcriptome, including changes in isoform expression and exon usage, and some of these changes are mediated by changes in DNA methylation.

Background: Coexpression network analysis is a powerful tool to reveal transcriptional regulatory mechanisms, identify transcription factors, and discover gene functions. It can also be used to investigate changes in coexpression patterns in response to environmental insults or changes in experimental conditions. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to investigate structural changes in gene coexpression networks in the muscle of bull beef calves gestated under diets with or without methionine supplementation. Both muscle transcriptome and methylome were evaluated using next generation sequencing. Results: Maternal methionine supplementation significantly perturbed coexpression patterns in the offspring’s muscle. Indeed, we found that neither the connection strength nor the connectivity pattern of six modules (subnetworks) detected in the control diet were preserved in the methionine-rich diet. Functional characterization revealed that some of the unpreserved modules are implicated in myogenesis, adipogenesis, fibrogenesis, canonical Wnt/β-catenin pathway, ribosome structure, rRNA binding and processing, mitochondrial activities, ATP synthesis and NAD(P) H oxidoreductases, among other functions. The bisulfite sequencing analysis showed that nearly 2% of all evaluated cytosines were differentially methylated between maternal diets. Interestingly, there were significant differences in the levels of gene body DNA methylation between preserved and unpreserved modules. Conclusions: Overall, our findings provide evidence that maternal nutrition can significantly alter gene coexpression patterns in the offspring, and some of these perturbations are mediated by changes in DNA methylation.

Prenatal hyperthermia has long-lasting impacts on dairy calf metabolism, immunity, and productivity. Yet, the effects of postnatal heat stress on neonatal calves remain unclear. As such, this study aimed to investigate the impact of heat stress on the pre-weaned dairy calf liver gene expression and DNA methylation profiles. Holstein dairy calves were exposed to summer heat stress (daily average THI >78) for 42 d postnatally (i.e., birth to weaning) with access to active fan heat abatement (postnatal cooling, post-CL; calf-height air speed 2.05 m/s, n = 12) or not (postnatal heat stress, post-HS; calf-height air speed 0.15 m/s, n = 12). All thermoregulatory responses were lower in post-CL relative to post-HS calves throughout the experimental period (-12 bpm, -2.5°C, and -0.11°C for respiratory frequency, rectal temperature, and ear skin temperature, respectively). Liver tissue was harvested via biopsy (n = 6 male calves per treatment) at 42 d of age for RNA sequencing and reduced representation bisulfite sequencing. There were 73 differentially expressed genes (DEG), of which 31 were downregulated and 42 were upregulated in post-HS relative to post-CL treatments (FDR cut-off 20%). Ingenuity Pathway Analysis revealed that postnatal heat stress significantly impacted 24 pathways and 60 transcription regulators, including pathway PI3K/AKT, and transcription regulators PPARGC1A, STAT5B, CREB, and X-box binding protein. A total of 14,639 differentially methylated cytosines (DMC) were found across the entire bovine genome; these DMCs mapped to 3,197 differentially methylated genes (DMG), with about 300 DMGs with DMCs located close to the transcription start site. These DMGs are closely related to metabolic pathways, such as PKA, AMPK, MAPK, and STAT3 signaling. Overall, pre-weaning exposure of dairy calves to heat stress changes hepatic methylation profiles, which in turn may impact the expression of genes with critical roles in intracellular signaling and development, metabolic, and immune-related pathways. Providing mechanical cooling via fans to dairy calves in summer seems beneficial to promoting thermoregulation and liver cellular hemostasis.

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots.

Circular extrachromosomal DNA (ecDNA) can drive tumor initiation, progression and resistance in some of the most aggressive cancers and is emerging as a promising anti-cancer target. However, detection currently requires costly whole-genome sequencing (WGS) or labor-intensive cytogenetic or FISH imaging, limiting its application in routine clinical diagnosis. To overcome this, we developed ecPath ( ec DNA from histo path ology ) , a computational method for predicting ecDNA status from routinely available hematoxylin and eosin (H&E) images. ecPath implements a deep-learning method we call transcriptomics-guided learning, which utilizes both transcriptomics and H&E images during the training phase to enable successful ecDNA prediction from H&E images alone, a task not achievable with models trained on H&E images only. It is trained on more than 6,000 tumor whole-slide images from the TCGA cohort with the best performance in predicting ecDNA status in brain and stomach tumors (average AUC=0.78). ecPath revealed that ecDNA-positive tumors are enriched with pleomorphic, larger and high-density nuclei. Testing in an independent cohort, ecPath predicted ecDNA status of 985 pediatric brain tumor patients with an AUC of 0.72. Finally, we applied ecPath to identify ecDNA-positive tumors in the TCGA cohort for which no WGS data were available. Like WGS-based ecDNA-positive labels, the predicted ecDNA-positive status also identify poor prognoses for low grade glioma patients. These results demonstrate that ecPath enables the detection of ecDNA from routinely available H&E imaging alone and help nominate aggressive tumors with ecDNA to study and target it.