Morino, K. | bioRxiv Science

bioRxiv2022

Fiber-parenchyma trade-off underlies changes in tropical forest structure and xylem architecture across a soil water gradient

O_LIWood anatomical traits can underpin tropical forest structural and functional changes across soil water gradients and therefore could improve our mechanistic understanding of how plants adapt to environmental change. C_LIO_LIWe assessed how the variation in the forest maximum height (Hmax), stem diameter, and wood density (WD) is associated with variation in xylem traits (area of fibers and parenchyma, conductive area [CondA, sum of all vessels lumens], vessel lumen area [VLA], vessel density [VD], and vessel wall reinforcement [VWR]) across 42 plots of a Brazilian Atlantic Forest habitat that span strong soil water gradients. C_LIO_LIWe found that in wetter communities, greater height and lower WD were associated with greater parenchyma area (capacitance), and lower fibers, VD, VWR. Contrastingly, in drier communities, lower height was associated with higher fiber area (xylem reinforcement), WD, VD, and VWR, while parenchyma area and vessels are reduced. C_LIO_LITree communities vary from conservative resource-use and structurally dependent hydraulic safety (Fibers) to acquisitive resource-use and capacitance dependent hydraulic safety (parenchyma). Such a fiber-parenchyma trade-off (FPT) underlies the variation in tree height across a soil water gradient. Wood anatomy is fundamental to understanding and predicting the impacts of environmental change on forest structure. C_LI

ecology↗

bioRxiv2021

Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular Response

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase1,2. ABL1-selective tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML3-7. The ultimate goal of CML treatment is likely to be TKI-free maintenance of deep molecular response (DMR), which is defined by quantitative measurement of BCR-ABL1 transcripts on the international scale (IS)8, and durable DMR is a prerequisite to reach this goal9. Thus, an algorithm to enable the early prediction of DMR achievement on TKI therapy is quite valuable. Here, we show that our mathematical framework based on a clinical trial dataset10 can accurately predict the response to frontline nilotinib. We found that our simple dynamical model can predict nilotinib response by using two common laboratory findings (observation values): IS11,12 and white blood cell (WBC) count. Furthermore, our proposed method identified patients who failed to achieve DMR with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs in clinical practice. Significance StatementChronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. The goal of this treatment is the sequential achievement of deep molecular response (DMR). Tyrosine kinase inhibitors (TKIs) are effective in the reduction because they inhibit CML cell proliferation. However, because of individual differences in the TKI efficacy, some patients are unable to achieve DMR, so that early prediction of DMR reachability is necessary for personalized medicine. By combining time series analysis and mathematical modeling, we developed a mathematical method that accurately predicts patients who do not achieve DMR in the early stages of TKI administration. Our prediction method gives a basis of effective personalized treatments for CML patients.

systems biology↗