Biology subjects

Feng, Y.-Y.

Publications and source records attributed to Feng, Y.-Y..

8 recordsLinked to original sources

bioRxiv2026

A dimensional link between gain-loss economic preferences and psychopathology

Human decision-makers differ in how they value and trade off reward, risk, and information when evaluating future gains and losses, and these preferences are central to well-being. However, how multi-attribute economic valuation relates to psychopathology remains unclear. Here, 1,954 individuals completed a transdiagnostic symptom battery and a multi-attribute decision-making task that independently manipulated expected reward, reward uncertainty, and non-instrumental information under matched gain and loss contexts. On average, when choosing among gains, participants preferred larger rewards, avoided risk, and sought advance information. When choosing among losses, they still preferred better outcomes and advance information, but less strongly. Their risk preference, by contrast, reversed, from risk-averse to risk-seeking. Substantial individual variability accompanied these population-level context effects, and different patterns of variability were associated with separate dimensions of psychopathology. Anxious-depressive and social withdrawal symptoms were associated with increased reward sensitivity across contexts, whereas compulsive-intrusive symptoms were associated with reduced reward sensitivity. Symptom-related differences in risk and information preferences were not fully explained by a uniform scaling of overall economic sensitivity, revealing additional attribute- and context-specific variation. A data-driven analysis summarized these symptom-preference associations in a single latent dimension that contrasted compulsive-intrusive symptoms with anxious-depressive and social withdrawal symptoms. An independently identified subgroup that strongly sought information about gains but avoided it about losses mirrored this profile and was enriched for compulsive-intrusive symptoms. These findings reveal structured, low-dimensional variation linking multi-attribute decision-making to psychopathology and could support a dimensional, task-based approach to psychiatric nosology.

neuroscience↗

bioRxiv2026

IGF2BP2 links m6A-modified HMGA1 mRNA stability to mitochondrial metabolism and cell proliferation

Chinese hamster ovary (CHO) cells serve as the primary host for industrial therapeutic protein production, yet enhancing their productivity remains a significant challenge. Epigenetic regulation, particularly RNA N6-methyladenosine modification, offers a promising strategy. Here, we show that the m6A reader protein insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) positively regulates recombinant protein yield in CHO cells. IGF2BP2 expression was elevated in high-producing clones, and its stable overexpression promoted cell proliferation, increased the S-phase cell proportion, and boosted titers and specific productivity of recombinant proteins--adalimumab, vitronectin, and donanemab--by 2.0-, 1.6-, 2.6-fold and 1.8-, 1.4-, 2.1- fold, respectively. Mechanistically, IGF2BP2 recognized m6A sites on HMGA1 mRNA, enhancing its stability and expression. Integrated analyses of oxidative stress, mitochondrial function, and metabolomics, along with inhibitor validation, revealed that IGF2BP2 also strengthens antioxidant defense, promotes mitochondrial ATP production and utilization, and reshapes cellular redox and metabolic homeostasis. These findings highlight IGF2BP2 as a critical regulator of recombinant protein expression and mitochondrial oxidative metabolism in CHO cells, illustrating how RNA methylation cooperates with mitochondrial function and proliferation to enhance protein production.

bioengineering↗

bioRxiv2023

Dorsal raphe neurons signal integrated value during multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when grappling with reward uncertainty. However, whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider all these attributes to make a choice, is unclear. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes. Remarkably, these neurons commonly integrated offer attributes in a manner that reflected monkeys overall preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how DRN participates in integrated value computations, guiding theories of DRN in decision-making and psychiatric disease.

neuroscience↗

bioRxiv2022

A neural mechanism for conserved value computations integrating information and rewards

Behavioral and economic theory dictates that we decide between options based on their values. However, humans and animals eagerly seek information about uncertain future rewards, even when this information does not provide any objective value. This implies that decisions can be made by endowing information with subjective value and integrating it with the value of extrinsic rewards, but the mechanism is unknown. Using a novel multi-attribute decision making task we found that human and monkey value judgements are regulated by strikingly conserved computational principles, including how they compute the value of information and scale it with informations timing and ability to resolve a specific form of uncertainty. We then identified a neural substrate in a highly conserved and ancient structure, the lateral habenula (LHb). LHb neurons signal the subjective value of choice options integrating the value of information with extrinsic rewards, and LHb activity both predicts and causally influences online decisions. Key input regions to LHb provide the necessary ingredients for these computations, but do not themselves signal an integrated value signal to guide multi attribute decisions. Our data thus identifies neural mechanisms of the conserved computations underlying multi-attribute, value-based decisions to seek information about the future.

neuroscience↗

bioRxiv2021

Neuronal mechanisms of novelty seeking

Humans and other primates interact with the world by observing and exploring visual objects. In particular, they often seek out the opportunities to view novel objects that they have never seen before, even when they have no extrinsic primary reward value. However, despite the importance of novel visual objects in our daily life, we currently lack an understanding of how primate brain circuits control the motivation to seek out novelty. We found that novelty-seeking is regulated by a small understudied subcortical region, the zona incerta (ZI). In a task in which monkeys made eye movements to familiar objects to obtain the opportunity to view novel objects, many ZI neurons were preferentially activated by predictions of future novel objects and displayed burst excitations before gaze shifts to gain access to novel objects. Low intensity electrical stimulation of ZI facilitated gaze shifts, while inactivations of ZI reduced novelty-seeking. Surprisingly, additional experiments showed that this ZI-dependent novelty seeking behavior is not regulated by canonical neural circuitry for reward seeking. The habenula-dopamine pathway, known to reflect reward predictions that control reward seeking, was relatively inactive during novelty-seeking behavior in which novelty had no extrinsic reward value. Instead, high channel-count electrophysiological experiments and anatomical tracing identified a prominent source of control signals for novelty seeking in the anterior ventral medial temporal cortex (AVMTC), a brain region known to be crucially involved in visual processing and object memory. In addition to their well-known function in signaling the novelty or familiarity of objects in the current environment, AVMTC neurons reflected the predictions of future novel objects, akin to the way neurons in reward-circuitry predict future rewards in order to control reward-seeking. Our data uncover a network of primate brain areas that regulate novelty-seeking. The behavioral and neural distinctions between novelty-seeking and reward-processing highlight how the brain can accomplish behavioral flexibility, providing a mechanism to explore novel objects.

neuroscience↗

bioRxiv2018

pVACtools: a computational toolkit to select and visualize cancer neoantigens

The elucidation of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. We have developed an in silico sequence analysis method - pVACtools, to facilitate comprehensive neoantigen characterization. This modular workflow consists of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization and selection using a graphical web-based interface (pVACviz), and determining the optimal order of neoantigen candidates in a DNA vector-based vaccine(pVACvector).

bioinformatics↗

bioRxiv2018

RegTools: Integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a splice-associated variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts and compared our results to other tools that integrate genomic and transcriptomic data. While many events were corroborated by the aforementioned tools, the flexibility of RegTools also allowed us to identify novel splice-associated variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant.

bioinformatics↗

bioRxiv2017

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

bioinformatics↗