Rashid, B. | Search bioRxiv

bioRxiv2022

A natural fusion of flavodiiron, rubredoxin, and NADH:rubredoxin oxidoreductase domains is the highly efficient water-forming oxidase of T. vaginalis

Microaerophilic pathogens such as Giardia lamblia and Trichomonas vaginalis have robust oxygen consumption systems to detoxify oxygen and maintain the intracellular redox balance. This oxygen consumption is a result of the H2O-forming NADH oxidase activity of two distinct flavin-containing systems: H2O-forming NADH oxidases (NOXes) and multicomponent flavodiiron proteins (FDPs). Both systems are not membrane-bound and recycle NADH into oxidized NAD+ while simultaneously removing O2 from the local environment, making them crucial for the survival of human microaerophilic pathogens. In this study, using bioinformatic and biochemical analysis, we show that T. vaginalis lacks a NOX-like enzyme, and instead harbors three proteins that are very close in their amino acid sequence and represent a natural fusion between N-terminal FDP, central rubredoxin and C-terminal NADH:rubredoxin oxidoreductase domains. We demonstrate that this natural fusion protein with fully populated flavin redox centers unlike a "stand-alone" FDP (also present in T. vaginalis), directly accepts reducing equivalents of NADH to catalyze the four-electron reduction of O2 to water within a single polypeptide and with an extremely high turnover. Using single particle electron cryo-microscopy (cryo-EM) we present structural insight into the spatial organization of the FDP core within this multidomain fusion protein. Our studies represent an important addition to our understanding of systems that allow human protozoan parasites to maintain their optimal redox balance and survive transient exposure to oxic conditions.

biochemistry↗

bioRxiv2020

A classification-based approach to estimate the number of resting fMRI dynamic functional connectivity states

Recent work has focused on the study of dynamic (vs static) brain connectivity in resting fMRI data. In this work, we focus on temporal correlation between time courses extracted from coherent networks or components called functional network connectivity (FNC). Dynamic functional network connectivity (dFNC) is most commonly estimated using a sliding window-based approach to capture short periods of FNC change. These data are then clustered to estimate transient connectivity patterns or states. Determining the number of states is a challenging problem. The elbow criterion is a widely used approach to determine the optimal number of states. In our work, we present an alternative approach that evaluates classification (e.g. healthy controls versus patients) as a measure to select the optimal number of states (clusters). We apply different classification strategies to perform classification between healthy controls (HC) and patients with schizophrenia (SZ) for different numbers of states (i.e. varying the model order in the clustering algorithm). We compute cross-validated accuracy for different model orders to evaluate the classification performance. Our results are consistent with our earlier work which shows that overall accuracy improves when dynamic connectivity measures are used separately or in combination with static connectivity measures. Results also show that the optimal model order for classification is different from that using the standard k-means model selection method and that such optimization improves resulting in cross-validated accuracy. The optimal model order obtained from the proposed approach also gives significantly improved classification performance over the traditional model selection method. In sum, the observed results suggest that if ones goal is to perform classification, using the proposed approach as a criterion for selecting the optimal number of states in dynamic connectivity analysis leads to improved accuracy in hold-out data.

neuroscience↗