| 1. |
- Brânzanic, Adrian M.V., et al.
(author)
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Importance of the iron–sulfur component and of the siroheme modification in the resting state of sulfite reductase
- 2020
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In: Journal of Inorganic Biochemistry. - : Elsevier BV. - 0162-0134. ; 203
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Journal article (peer-reviewed)abstract
- The active site of sulfite reductase (SiR) consists of an unusual siroheme–Fe4S4 assembly coupled via a cysteinate sulfur, and serves for multi-electron reduction reactions. Clear explanations have not been demonstrated for the reasons behind the choice of siroheme (vs. other types of heme) or for the single-atom coupling to an Fe4S4 center (as opposed to simple adjacency or to coupling via chains consisting of more than one atom). Possible explanations for these choices have previously been invoked, relating to the control of the spin state of the substrate-binding (siro)heme iron, modulation of the trans effect of the (Fe4S4–bound) cysteinate, or modulation of the redox potential. Reported here is a density functional theory (DFT) investigation of the structural interplay (in terms of geometry, molecular orbitals and magnetic interactions) between the siroheme and the Fe4S4 center as well as the importance of the covalent modifications within siroheme compared to the more common heme b, aiming to verify the role of the siroheme modification and of the Fe4S4 cluster at the SiR active site, with focus on previously-formulated hypotheses (geometrical/sterics, spin state, redox and electron-transfer control). A calibration of various DFT methods/variants for the correct description of ground state spin multiplicity is performed using a set of problematic cases of bioinorganic Fe centers; out of 11 functionals tested, M06-L and B3LYP offer the best results – though none of them correctly predict the spin state for all test cases. Upon examination of the relative energies of spin states, reduction potentials, energy decomposition (electrostatic, exchange-repulsion, orbital relaxation, correlation and dispersion interactions) and Mayer bond indices in SiR models, the following main roles of the siroheme and cubane are identified: (1) the cubane cofactor decreases the reduction potential of the siroheme and stabilizes the siroheme–cysteine bond interaction, and (2) the siroheme removes the quasi-degeneracy between the intermediate and high-spin states found in ferrous systems by preserving the latter as ground state; the higher-spin preference and the increased accessibility of multiple spin states are likely to be important in selective binding of the substrate and of the subsequent reaction intermediates, and in efficient changes in redox states throughout the catalytic cycle.
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| 2. |
- Brânzanic, Adrian M.V., et al.
(author)
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Why does sulfite reductase employ siroheme?
- 2019
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In: Chemical Communications. - : Royal Society of Chemistry (RSC). - 1359-7345 .- 1364-548X. ; 55:93, s. 14047-14049
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Journal article (peer-reviewed)abstract
- Sulfite reductase (SiR) contains in the active site a unique assembly of siroheme and a [4Fe4S] cluster, linked by a cysteine residue. Siroheme is a doubly reduced variant of heme that is not used for a catalytic function in any other enzyme. We have used non-equilibrium Green's function methods coupled with density functional theory computations to explain why SiR employs siroheme rather than heme. The results show that direct, through vacuum, charge-transfer routes are inhibited when heme is replaced by siroheme. This ensures more efficient channelling of the electrons to the catalytic iron during the six-electron reduction of sulfite to sulfide, limiting potential side-reactions that could occur if the incoming electrons were delocalized onto the macrocyclic ring.
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| 3. |
- Dumitrescu, Adrian, et al.
(author)
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Finding Small Complete Subgraphs Efficiently
- 2023
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In: Combinatorial Algorithms - 34th International Workshop, IWOCA 2023, Proceedings. - 0302-9743 .- 1611-3349. - 9783031343469 ; 13889 LNCS, s. 185-196
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Conference paper (peer-reviewed)abstract
- (I) We revisit the algorithmic problem of finding all triangles in a graph G= (V, E) with n vertices and m edges. According to a result of Chiba and Nishizeki (1985), this task can be achieved by a combinatorial algorithm running in O(mα) = O(m3 / 2) time, where α= α(G) is the graph arboricity. We provide a new very simple combinatorial algorithm for finding all triangles in a graph and show that is amenable to the same running time analysis. We derive these worst-case bounds from first principles and with very simple proofs that do not rely on classic results due to Nash-Williams from the 1960s. (II) We extend our arguments to the problem of finding all small complete subgraphs of a given fixed size. We show that the dependency on m and α in the running time O(αℓ-2· m) of the algorithm of Chiba and Nishizeki for listing all copies of Kℓ, where ℓ≥ 3, is asymptotically tight. (III) We give improved arboricity-sensitive running times for counting and/or detection of copies of Kℓ, for small ℓ≥ 4. A key ingredient in our algorithms is, once again, the algorithm of Chiba and Nishizeki. Our new algorithms are faster than all previous algorithms in certain high-range arboricity intervals for every ℓ≥ 7.
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| 4. |
- Menkveld, Albert J., et al.
(author)
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Nonstandard Errors
- 2024
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In: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Journal article (peer-reviewed)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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| 5. |
- Ruilope, LM, et al.
(author)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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In: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Journal article (peer-reviewed)abstract
- <b><i>Background:</i></b> Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. <b><i>Patients and</i></b> <b><i>Methods:</i></b> The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m<sup>2</sup> and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.
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