TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity
Gender-specific symptom outcomes on cariprazine treatment: a 12-month naturalistic longitudinal follow-up study in schizophrenia
STAT+: Gene-editing startup launches with $230 million and a Chinese licensing deal
A new gene-editing startup has threaded the needle of negotiating contracts with a Chinese drug company, raising $230 million in funding, and executing a reverse merger with a preexisting biotech company.
The result? Serapha Bio, which launched Tuesday to develop a one-and-done treatment for a liver and lung disease called Alpha-1 Antitrypsin Deficiency, or AATD, founding investors RA Capital and RTW Investments told STAT exclusively.
RA and RTW arranged $138 million in Series A funding for Serapha, along with $92 million in additional funds tied to the company’s reverse merger with Boundless Bio. Boundless Bio launched in 2019 to study extrachromosomal DNA and its role in cancer growth but ran into trouble with its lead program.
A Study to Learn About the Study Medicine Called Ponsegromab in Adults With Lung Cancer-Associated Significant Weight Loss
Interventions: Drug: ponsegromab; Drug: placebo
Sponsors: Pfizer
Not yet recruiting
Feasibility and Preliminary Performance of an AI Prototype for Digital ROSE During EBUS-TBNA and Peripheral TBNA: a Prospective Pilot Study (AI-ROSE-FEAS)
Interventions: Diagnostic Test: ROSE
Sponsors: Azienda Ospedaliera di Rilievo Nazionale A.Cardarelli
Not yet recruiting
Breast and Cervical Cancer Stigma in Rwanda
Interventions: Behavioral: Behavioral: RISE Intervention; Behavioral: Behavioral: CHW Educational Intervention
Sponsors: Dana-Farber Cancer Institute; Dana-Farber/Harvard Cancer Center (DF/HCC) Boston, MA; American Association for Cancer Research; Breast Cancer Research Foundation
Not yet recruiting
Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes
Schizophrenia’s genetic landscape just expanded dramatically. A new study in Nature Genetics identifies 641 previously unrecognized genes associated with schizophrenia, thanks to a modeling framework that captures how distant genetic variants regulate gene expression through co‑expression networks. The work reframes schizophrenia not as a collection of isolated genetic hits, but as a disorder shaped by long‑range regulatory relationships across the brain. The study is titled, “Co‑expression‑based models improve eQTL predictions for transcriptome‑wide association studies and highlight new schizophrenia‑associated genes.”
The research team, led by Giulio Pergola, PhD, at the Lieber Institute for Brain Development (LIBD), developed two trans‑aware predictive models—INGENE and MODULE—that quantify how variants far from a gene influence its expression through co‑regulated partners. Traditional transcriptome‑wide association studies (TWAS) focus almost exclusively on cis‑expression quantitative trait loci (cis–eQTLs), variants within ±1 Mb of a gene. But as the paper noted, “Most transcriptome‑wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained.” By contrast, the new models incorporate distal (trans) regulatory effects, capturing regulatory relationships that behave more like social networks than neighborhood blocks.
Using RNA‑seq data from six human post‑mortem brain regions and genetic data from more than 102,000 individuals, the team integrated cis‑based predictors (CIS, EpiXcan) with their new trans‑based frameworks. The combined approach improved gene‑expression prediction for 18,744 genes, and when applied to Psychiatric Genomics Consortium (PGC3) datasets, it identified 766 schizophrenia‑associated genes, including 641 not previously detected by TWAS.
Pergola said the field has been “looking for the light under the lamppost, focusing only on genes close to disease‑associated DNA variants.” By illuminating long‑range interactions, he explained, “we’ve essentially turned on lights across the entire neighborhood, revealing how distant genetic variants coordinate to build the genetic basis of schizophrenia.”
The findings converge on pathways involved in glutamate signaling, neuronal communication, immune processes, and neurodevelopment—biological systems repeatedly implicated in psychiatric risk. MODULE‑derived trans‑single nucleotide polymorphisms (SNPs) showed particularly strong enrichment for schizophrenia‑associated variants, and many overlapped with cis‑eQTLs for transcription factors such as GATAD2A, RERE, IRF3, and SP4, all previously prioritized in schizophrenia GWAS.
Daniel Weinberger, MD, CEO and director of LIBD, emphasized the shift in perspective: “Schizophrenia risk isn’t just about individual genes acting one after another—it’s about how networks of genes work together. Understanding these coordinated genetic programs brings us closer to precision psychiatry.”
By demonstrating that trans‑regulatory architecture is both detectable and biologically meaningful, the study provides a roadmap for expanding TWAS beyond local effects. It also underscores the importance of integrating multi‑region brain transcriptomics with large‑scale genetic cohorts to reveal disease‑relevant regulatory relationships.
The post Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes appeared first on GEN – Genetic Engineering and Biotechnology News.
Network analysis of spousal support and fear of childbirth in pregnant women of advanced maternal age
AI Discovers Potential Antimicrobial “Prionin” Peptides
Prions are best known for their role in rare, fatal neurodegenerative diseases. But a new study by researchers at the University of Pennsylvania suggests that proteins in this family may also conceal molecular fragments that can kill bacteria, including drug-resistant strains.
The Penn scientists used a deep learning platform called APEX 1.1 to scan millions of short protein fragments derived from nearly 3,000 prion and prion-like proteins. The search identified more than a thousand candidate antimicrobial peptides, which they called “prionins.” In tests 59 synthesized prionins inhibited bacterial pathogens, and two reduced Acinetobacter baumannii burden in mice.
The discovery is unexpected because prions are usually discussed in the context of misfolding, aggregation, and brain disease—not immunity or antibiotic discovery. The new findings suggest that useful biological activities may be hidden inside proteins whose known roles have little to do with infection, and that artificial intelligence can help reveal them.
“Prions have long been seen almost entirely through the lens of disease,” said César de la Fuente, PhD, associate professor and director of the Machine Biology Group at the University of Pennsylvania. “Our work shows that when AI looks across biology at scale, even proteins with a dark reputation can contain useful molecular instructions. In this case, those instructions point to possible new antibiotics.”
De la Fuente is senior and corresponding author of the researchers’ published paper in Nature Microbiology, titled “Deep learning reveals antimicrobial peptides within prions.”
Antibiotic resistance is among the most urgent challenges in medicine, and many existing antibiotics were discovered by searching traditional natural sources. The new study takes a different route: instead of asking where antibiotics usually come from, it asks whether biology has hidden antimicrobial molecules in places scientists would not normally look.
Certain amyloid-associated protein sequences may participate in host defense, the authors wrote. “Several amyloid-associated proteins, including amyloid-β and the cellular prion protein, have been reported to display antimicrobial or host-protective activities, raising the possibility that aggregation-prone proteins may encode cryptic antimicrobial fragments within their primary sequence.” But until now, scientists had not systematically searched prion and prion-like proteins at scale to ask whether they broadly encode hidden antimicrobial peptides.
“Whether such encrypted peptides are broadly embedded across prion and prion-like proteins has not been systematically examined,” the researchers continued. The Penn team took on that task, using AI to move from scattered observations to a global search across millions of possible protein fragments. They mined prion-related proteins with APEX1.1, a deep learning platform for antimicrobial peptide (AMP) discovery. “… using deep learning, we screened 19.3 million fragments from 2,897 curated prion-related proteins and identified 1,179 candidate antimicrobial peptides, which we term prionins,” they stated.
To test the predictions, the researchers synthesized 75 prionins and evaluated them against a panel of clinically relevant bacterial pathogens, including multidrug-resistant strains. Fifty-nine inhibited at least one pathogen, and 42 showed potent activity at concentrations of 16 micromolar or lower against at least one pathogen.
The team then examined how the molecules worked. Many active prionins damaged bacterial membranes, a common mechanism used by antimicrobial peptides. Importantly, several candidates also showed early signs of selectivity: hemolysis was rare, and 16 active peptides showed neither measurable hemolysis nor cytotoxicity at the highest concentrations tested.
Two of the strongest candidates were tested in a mouse skin-infection model caused by Acinetobacter baumannii, a difficult-to-treat pathogen. A single topical dose of each peptide significantly lowered the bacterial burden, with effects comparable to the antibiotic polymyxin B in the model tested. The researchers observed no treatment-associated weight loss. In summary, they wrote, “What makes this exciting is that the predictions held up experimentally,” said Marcelo D T Torres, PhD, co-first author of the study. “We went from millions of hidden protein fragments to synthesized molecules that killed bacteria in the lab, and then to candidates that worked in an animal infection model. That is the difference between an AI screen and a true discovery platform.”
The findings build on the de la Fuente lab’s broader effort to mine the biological world for “encrypted peptides”—short, hidden sequences within larger proteins that can have biological functions when isolated. Previous work from the group has searched human proteins, extinct organisms, archaea, microbiomes, and venoms. The prion study expands that concept into one of biology’s most unexpected protein classes.
The study also raises a provocative possibility at the intersection of neurodegeneration and innate immunity. It does not establish that these peptides are naturally released during infection or function physiologically in host defense, they stated. But it suggests that prion and prion-like proteins may contain cryptic antimicrobial sequences, opening a new way to think about prion biology and its possible links to immunity. “… it establishes prion-related proteins as a productive source space for antibiotic discovery and provides a framework for testing whether cryptic peptides contribute to defense in specific biological contexts.”
The researchers emphasize that this is an early discovery, not a new treatment ready for patients. The study does not change the established role of misfolded prions in devastating neurodegenerative disease. Instead, it suggests prion and prion-like proteins as a rich and previously overlooked source space for antibiotic discovery. “Our findings identify prion and prion-like proteins as an unexpectedly rich reservoir of encrypted AMPs,” the authors concluded. “This expands a growing view that antimicrobial activity can be hidden within proteins not canonically annotated as immune effectors and extends that concept to prion biology … These results connect prion-related sequence space to antimicrobial function and highlight unconventional protein classes as sources of antibiotic leads.”
“For a long time, drug discovery has been limited not only by what we can test, but by where we choose to look,” de la Fuente said. “AI is changing that. It gives us a way to search the hidden layers of biology and ask whether molecules associated with one story—in this case, disease—may also carry another story with therapeutic potential.”
The post AI Discovers Potential Antimicrobial “Prionin” Peptides appeared first on GEN – Genetic Engineering and Biotechnology News.

