Biology subjects

Chang, C. H.

Publications and source records attributed to Chang, C. H..

5 recordsLinked to original sources

bioRxiv2026

A global assessment reveals widespread co-benefits for forest protection

Avoided forest conversion ("AFC") is a high-impact conservation and climate mitigation strategy, yet its impacts on human well-being and ecosystems remain an open question. We present the first global and comprehensive assessment of AFC impacts on human material and non-material well-being, biodiversity, and ecosystem services in a set of 29,038 studies published between 1990-2022. We leveraged an ensemble of open- and closed-weight GPT models, training regimes, and architectures, and applied novel approaches to detect hallucination-induced reasoning errors. 85% of the studies report positive human and environmental co-benefits, with strong evidence for synergies rather than trade-offs. Nevertheless, there are evidence gaps for countries facing imminent forest conversion threats. Methodologically, this study advances an AI workflow for conducting complex, holistic evidence syntheses that traditionally require thousands of expert hours. The results reinforce AFC as an immediate climate and conservation solution capable of delivering substantial environmental and human well-being benefits.

ecology↗

bioRxiv2025

Snekmer Learn/Apply: A kmer-based vector similarity approach to proteinclassification suitable for metagenomic datasets

Advances in whole genome sequencing have led to a rapid and ongoing increase in the amount of sequence data available, but 40-50% of known genes have no functional annotation and only 25-30% have specific functional annotations. Current functional annotation approaches typically rely on computationally expensive pairwise or multiple sequence alignments, preventing rapid development of models for novel protein functions and sometimes limiting methods to one ontology. Representation of sequence in short segments (kmers) has been used in many applications for nucleotide sequence, and more recently has been applied to protein sequence as well. We previously developed Snekmer, a tool which uses kmer patterns to develop alignment-free individual protein family models. Other approaches, such as MMSeqs2 and DIAMOND, use protein kmers as a fast filter to reduce search space for subsequent sequence alignment. Here, we describe a novel addition to the Snekmer tool which builds kmer libraries for protein families and uses those libraries to map functional annotations to new sequences. We first demonstrate that our method accurately applies TIGRFAMs annotations to protein fragments and to a low-sequence identity benchmark dataset, and further use it to annotate a set of drought stress associated soil and rhizosphere metagenome sequences with higher sensitivity towards several important protein function classes than that shown by HMMs. We have incorporated this workflow into Snekmer.

bioinformatics↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2021

Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection.

Complement activation has been implicated in the pathogenesis of severe SARS-CoV-2 infection. However, it remains to be determined whether increased complement activation is a broad indicator of critical illness (and thus, no different in COVID-19). It is also unclear which pathways are contributing to complement activation in COVID-19, and, if complement activation is associated with certain features of severe SARS-CoV-2 infection, such as endothelial injury and hypercoagulability. To address these questions, we investigated complement activation in the plasma from patients with COVID-19 prospectively enrolled at two tertiary care centers. We compared our patients to two non-COVID cohorts: (a) patients hospitalized with influenza, and (b) patients admitted to the intensive care unit (ICU) with acute respiratory failure requiring invasive mechanical ventilation (IMV). We demonstrate that circulating markers of complement activation (i.e., sC5b-9) are elevated in patients with COVID-19 compared to those with influenza and to patients with non-COVID-19 respiratory failure. Further, the results facilitate distinguishing those who are at higher risk of worse outcomes such as requiring ICU admission, or IMV. Moreover, the results indicate enhanced activation of the alternative complement pathway is most prevalent in patients with severe COVID-19 and is associated with markers of endothelial injury (i.e., Ang2) as well as hypercoagulability (i.e., thrombomodulin and von Willebrand factor). Our findings identify complement activation to be a distinctive feature of COVID-19, and provide specific targets that may be utilized for risk prognostication, drug discovery and personalized clinical trials. SUMMMARYComplement has been implicated in COVID-19. However, whether this is distinctive of COVID-19 remains unanswered. Ma et al report increased complement activation in COVID-19 compared to influenza and non-COVID respiratory failure, and demonstrate alternative pathway activation as a key marker of multiorgan failure and death.

immunology↗

bioRxiv2020

Substrate Channeling via a Transient Protein-Protein Complex: The case of D-Glyceraldehyde-3-Phosphate Dehydrogenase and L-Lactate Dehydrogenase

BackgroundD-Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and L-lactate dehydrogenase (LDH) can form a complex that can regulate the major metabolic pathways, however, the exact mechanism remains unknown. We analyzed a possibility of NADH-channeling from GAPDH-NADH complex to LDH isozymes using enzymes from different cells. ResultsEnzyme-kinetics and NADH-binding studies showed that LDH can use GAPDH-NADH complex as a substrate. LDH activity with GAPDH-NADH complex was challenged with anti-LDH antibodies to show that the channeled and the diffusive reactions always take place in parallel. The channeling path is dominant only in assays with limiting free-NADH concertation that mimic cytosolic conditions. Analytical ultracentrifugation showed that the channeling does not require a high affinity complex. Molecular dynamics calculations and protein-protein interaction studies showed that LDH and GAPDH can form a leaky channeling complex only at subsaturating NADH concentrations. The interaction sites are conserved between LDH isozymes from heart and muscle, and between GAPDH molecules from rabbit and yeast cells. Positive electric fields between the NAD(H) binding sites on LDH and GAPDH tetramers, showed that NAD(H)-channeling within the LDH-GAPDH complex, can be an extension of NAD(H)-channeling between the adjacent subunits in each tetramer. ConclusionsIn the case of a transient (GAPDH-NADH)-LDH complex, the relative contribution from the channeled and the diffusive paths depends on the overlap between off-rates for the transient (GAPDH-NADH)-LDH complex and off-rates for the GAPDH-NADH complex. Molecular evolution or metabolic engineering protocols can exploit substrate channeling for metabolic flux control by fine-tuning substrate-binding affinity for the key enzymes in the competing reaction paths. Highlights- Substrate channeling molecular mechanism can regulate energy production and aerobic and anaerobic metabolism in cells - LDH and GAPDH can form a channeling complex only at sub-saturating NADH concentration - Channeled and diffusive paths always compete and take place in parallel - NADH channeling does not require a high-affinity complex - NADH channeling within GAPDH-LDH complex is an extension of NAD(H) channeling within each tetramer - Allosteric modulation of NADH binding affinity in GAPDH tetramer can regulate NAD(H) channeling

biochemistry↗